Note: Descriptions are shown in the official language in which they were submitted.
TITLE OF THE INVENTION
TRANSPORT FACILITATION SYSTEM FOR CONFIGURING A SERVICE VEHICLE FOR A USER
[0001] (intentionally blank)
BACKGROUND OF THE INVENTION
[0002] For a personal use vehicle, a driver can permanently configure the
components
of the vehicle according to the driver's preferences. For example, the driver
can adjust
the seat and mirrors, set preferred radio stations, set a preferred
temperature, adjust
the steering wheel setting, and the like. For frequent use vehicles (e.g., car
rentals or
shared vehicles), drivers and passengers must make adjustments to the various
.. components of the vehicle for each use.
BRIEF DESCRIPTION OF THE DRAWINGS
[0003] The disclosure herein is illustrated by way of example, and not by way
of
limitation, in the figures of the accompanying drawings in which like
reference numerals
refer to similar elements, and in which:
[0004] FIG. 1 is a block diagram illustrating an example transport
facilitation
system in communication with user devices and a fleet of AVs, as described
herein;
[0005] FIG. 2 is a block diagram illustrating an example AV implementing a
control system, as described herein;
[0006] FIG. 3 is a block diagram illustrating an example mobile computing
device
executing a designated application for a transport arrangement service, as
described herein;
[0007] FIGS. 4A and 4B are flow charts describing example methods of utilizing
a
comfort profile for configuring an AV for a user, according to examples
described
herein;
[0008] FIGS. 5A and 5B are flow charts describing additional methods of
configuring an AV for one or more users, according to examples described
herein;
[0009] FIG. 6 is a flow chart describing an example method of optimizing
timing
for configuring an AV for one or more users, according to examples described
herein;
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[0010] FIG. 7 is a block diagram illustrating a computer system upon which
examples described herein may be implemented;
[0011] FIG. 8 is a block diagram illustrating a mobile computing device upon
which examples described herein may be implemented; and,
[0012] FIG. 9 is a block diagram illustrating a computing system for an AV
upon
which examples described herein may be implemented.
DETAILED DESCRIPTION OF THE EMBODIMENTS
[0013] A transport facilitation system is disclosed that can configure an
autonomous
vehicle (AV) for a requesting user prior to pick-up. In certain
implementations, the
transport facilitation system can provide an application-based transportation
arrangement service that can arrange transportation for requesting users
throughout a
given region. In many examples described herein, the transport facilitation
system can
receive pick-up requests from users within the given region, and select AVs
proximate to
the requesting users to service the pick-up requests. Broadly speaking, the
transport
facilitation system can transmit configuration commands to cause the selected
AVs to
configure various interior components (e.g., seat positions, individual seat
adjustments,
seat temperature, air temperature, radio settings, windows, mirrors, lighting,
etc.)
based on the preferences or requirements of the requesting user.
[0014] In some examples, a user interface can be generated on a requesting
user's
mobile computing device, enabling the user to set preference parameters for
the AV
prior to pick-up. For example, the user interface can be generated on a
designated
application for a transportation arrangement service managed by the transport
facilitation system. In certain aspects, the requesting user can configure a
comfort
profile indicating preferred AV settings, and the comfort profile can be
stored by the
transport facilitation system, or locally on the mobile computing device.
[0015] Additionally or alternatively, upon receiving a pick-up request from a
requesting
user, the backend transport facilitation system can select a proximate AV to
service the
pick-up request, determine the configurable parameters of the AV, and cause
the user
interface on the requesting user's mobile computing device to generate a
preference
menu¨ based on the configurable parameters of the selected AV¨ that enables
the
user to make selections to configure various adjustable parameters of the AV
prior to
pickup. The adjustable parameters can include seat positioning, seat
temperature, air temperature, a seating configuration, a home page display on
a display
screen, a language preference, preferred radio stations, interior lighting
(e.g., colors,
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=
brightness), and the like. Additionally or alternatively, the backend
transport facilitation
system can transmit a notification to the requesting user's mobile computing
device to
indicate a particular seat of the AV that has been assigned and configured for
that user
(e.g., with preferred seat adjustments, temperature, audio selections, etc.).
Such
notifications may be advantageous for AV carpooling in which the transport
facilitation
system can route a particular AV to make multiple pick-ups and drop-offs while
configuring individual seats and user preferences of the AV at the same time.
[0016] Additionally or alternatively, upon transmitting a pick-up request,
accelerometer
data from the user's mobile computing device can be analyzed to determine a
height,
weight, body type, and/or gait of the user to make adjustments to a seat on
which the
user will travel in the AV. In some aspects, the mobile computing device can
transmit
the raw accelerometer data and location data to the transport facilitation
system to
analyze for directional acceleration peaks to determine stride length or gait
pattern
signatures of the user. Based on these data, the transport facilitation system
can
determine the height and/or weight of the user on a high level, and on a lower
level, the
user's femur length, leg length (and estimated torso length), and/or an
estimated
posture in order to make adjustments to, for example, a backrest angle, a
thigh
extension (e.g., cushion edge adjustment), a fore-and-aft position, a headrest
angle, a
headrest level, a lumbar position, a seat depth, a seat height, an upper seat
tilt angle,
and a shoulder support element of the user's seat.
[0017] Additionally, an AV is disclosed that optimizes timing to configure
preference
settings when en route to rendezvous with a requesting user. The AV can
receive a set
of configurations for interior systems based on user preferences, and
determine an
optimal timing schedule to configure each of the interior systems such that
the AV is
configured for the requesting user as the AV arrives at the pick-up location.
For certain
systems, like the seating configuration system and the seat adjustment system,
the AV
can execute the user's preferences just prior to pick-up (e.g., 15-20 seconds
prior to
arriving at the pick-up location). For other systems, like the seat
temperature and
climate control systems, the AV can determine a time frame necessary to
achieve the
preferred temperature(s), and can perform an optimization operation to
initiate the
climate control system and/or seat temperature system to achieve the desired
temperature(s) just prior to arriving at the pick-up location in order to
optimize power
consumption. In variations, some or all of the timing characteristics for
optimizing
timing to configure the AV may be performed by the transport facilitation
system.
Accordingly, the optimal timing schedule can be determined by the transport
facilitation
system, and transmitted to the AV for execution prior to pick-up.
[0018] In some aspects, the transport facilitation system can store preference
logs or
comfort profiles in a database that indicate setup preferences¨ corresponding
to the
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adjustable interior component of the AV¨ for users of the transportation
arrangement
service. The transport facilitation system can receive a pick-up request from
computing
device running a designated application of the transportation arrangement
service. The
pickup request can include a unique identifier identifying the requesting user
of the
computing device, and a pick-up location. Using the unique identifier, the
transport
facilitation system can perform a lookup in the database for a comfort profile
indicating
AV setup preferences for the user. Based on the pick-up location, the
transport
facilitation system can select an AV to service the pick-up request. And,
based on the AV
setup preferences for the user, the transport facilitation system can transmit
a set of
configuration commands to configure the adjustable components of the AV for
the user
prior to the AV rendezvousing with the requesting user. Accordingly, the
interior
systems of the AV can be preemptively configured prior to pick-up.
[0019] Additionally, the transport facilitation, system can service pick-up
requests for
respective users over time and receive configuration data corresponding to the
user
configuring the interior systems (e.g., seat positioning, radio station
selections, browsing
data, etc.). The transport facilitation system can identify preference
patterns in the
configuration data and store and update preference data for each of the user
in the
database based on the preference patterns. In some examples, the preference
data for
a user may be updated based on feedback provided by the user. For example, the
transport facilitation system can receive feedback indicating user experience
ratings for
AV rides. In some aspects, if the rating is below a certain threshold (e.g.,
two out of five
stars), the transport facilitation system can be triggered to analyze AV data
for the trip
to identify potential causes for the low rating. The AV data can include
control system
inputs and sensor data corresponding to acceleration, braking, and steering of
the AV
during the trip and/or data indicating an operational mode of the AV during
the trip
(e.g., a normal mode or high caution mode). In the AV data, the transport
facilitation
system can identify anomalous instances indicating potential causes for the
rating being
below the predetermined threshold. For example, the transport facilitation
system can
identify instances of anomalous braking, anomalous acceleration, anomalous
steering,
over-caution, under-caution, speeding, and/or driving too slowly by the AV.
Over time,
the transport facilitation system can identify certain patterns and learn the
user's
preferences based on ratings information, and update the preferences of the
user to
mitigate anomalous instances in future rides. For example, using the ratings
data, the
transport facilitation system can learn that the user prefers more expedient
travel as
opposed to more highly cautious travel.
[0020] Among other benefits, the examples described herein achieve a technical
effect
of providing comfort to users of a transportation arrangement service by
preemptively
configuring the interior systems of an autonomous vehicle (AV) prior to pick-
up. Such
preemptive configuring may be performed based on a stored comfort or
preference
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profile, by way of user inputs prior to pick-up, by analyzing acceleration
data from the
user's mobile device, and/or by machine learning techniques over time.
[0021] As used herein, a computing device refers to devices corresponding to
desktop
computers, cellular devices or smartphones, personal digital assistants
(PDAs), laptop
computers, tablet devices, television (IP Television), etc., that can provide
network
connectivity and processing resources for communicating with the system over a
network. A computing device can also correspond to custom hardware, in-vehicle
devices, or on-board computers, etc. The computing device can also operate a
designated application configured to communicate with the network service.
[0022] One or more examples described herein provide that methods, techniques,
and
actions performed by a computing device are performed programmatically, or as
a
computer-implemented method. Programmatically, as used herein, means through
the
use of code or computer-executable instructions. These instructions can be
stored in
one or more memory resources of the computing device. A programmatically
performed
step may or may not be automatic.
[0023] One or more examples described herein can be implemented using
programmatic modules, engines, or components. A programmatic module, engine,
or
component can include a program, a sub-routine, a portion of a program, or a
software
component or a hardware component capable of performing one or more stated
tasks
or functions. As used herein, a module or component can exist on a hardware
component independently of other modules or components. Alternatively, a
module or
component can be a shared element or process of other modules, programs or
machines.
[0024] Some examples described herein can generally require the use of
computing
devices, including processing and memory resources. For example, one or more
examples described herein may be implemented, in whole or in part, on
computing
devices such as servers, desktop computers, cellular or smartphones, personal
digital
assistants (e.g., PDAs), laptop computers, printers, digital picture frames,
network
equipment (e.g., routers) and tablet devices. Memory, processing, and network
resources may all be used in connection with the establishment, use, or
performance of
any example described herein (including with the performance of any method or
with
the implementation of any system).
[0025] Furthermore, one or more examples described herein may be implemented
through the use of instructions that are executable by one or more processors.
These
instructions may be carried on a computer-readable medium. Machines shown or
described with figures below provide examples of processing resources and
computer-
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readable mediums on which instructions for implementing examples disclosed
herein
can be carried and/or executed. In particular, the numerous machines shown
with
examples of the invention include processors and various forms of memory for
holding
data and instructions. Examples of computer-readable mediums include permanent
memory storage devices, such as hard drives on personal computers or servers.
Other
examples of computer storage mediums include portable storage units, such as
CD or
DVD units, flash memory (such as carried on smartphones, multifunctional
devices or
tablets), and magnetic memory. Computers, terminals, network enabled devices
(e.g.,
mobile devices, such as cell phones) are all examples of machines and devices
that
utilize processors, memory, and instructions stored on computer-readable
mediums.
Additionally, examples may be implemented in the form of computer-programs, or
a
computer usable carrier medium capable of carrying such a program.
[0026] Numerous examples are referenced herein in context of an autonomous
vehicle
(AV). An AV refers to any vehicle which is operated in a state of automation
with respect
to steering and propulsion. Different levels of autonomy may exist with
respect to AVs.
For example, some vehicles may enable automation in limited scenarios, such as
on
highways, provided that drivers are present in the vehicle. More advanced AVs
can drive
without any human assistance from within or external to the vehicle. Such
vehicles are
often required to make advanced determinations regarding how the vehicle
behaves
given challenging surroundings of the vehicle environment.
[0027] SYSTEM DESCRIPTIONS
[0028] FIG. 1 is a block diagram illustrating an example transport
facilitation system in
communication with user devices and a fleet of AVs or service vehicles, as
described
herein. The transport facilitation system 100 can include a communications
interface
115 to communicate with the user devices 195 and the fleet of autonomous
vehicles
190 over a number of networks 180. In addition or in variations, the transport
facilitation system 100 can communicate with human drivers operating service
vehicles
to facilitate transportation in accordance with a transportation arrangement
service
managed by the transport facilitation system 100. In many examples, the
transport
facilitation system 100 can provide the transportation arrangement service to
link
requesting users with service vehicles and/or AVs in the AV fleet 190 managed
by the
transport facilitation system 100. A designated application 185 corresponding
to the
transportation arrangement service can be executed on the user devices 195. A
requesting user can provide an input on a user device 195 to transmit a pick-
up request
197 to the transport facilitation system 100. The pick-up request 197 can be
received by
the communications interface 115 and sent to a selection engine 135, which can
match
the requesting user with a proximate AV from the fleet 190.
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[0029] In one or more examples, the pick-up request 197 can include a pick-up
location
where a selected AV 109 can rendezvous with the requesting user. The fleet of
AVs 190
can be dispersed throughout a given region (e.g., a city or metropolitan area)
and
transmit vehicle location data 192 to a vehicle interface 105 of the transport
facilitation
system 100. The vehicle interface 105 can transmit the vehicle locations 192
to the
selection engine 135 in order to enable the selection engine 135 to determine
candidate
vehicles that can readily service the pick-up request 197. In some examples,
the pick-up
request 197 can include a unique identifier 136 for the requesting user, which
can be
utilized by a configuration engine 140 to initially determine whether the
requesting user
has a vehicle type preference. For example, the configuration engine 140 can
utilize the
unique identifier 136 for the requesting user to perform a lookup 142 in rider
preference logs 132 in a database 130 of the transport facilitation system
100. A
matching rider preference log 132 for the requesting user can, among other
things
described herein, indicate a preferred vehicle type (e.g., a sport utility
vehicle, a van, a
sports vehicle, a station wagon, a mid-sized, large or compact vehicle, a
luxurious
vehicle, etc.). Additionally or alternatively, the configuration engine 140
can transmit a
prompt to the requesting user, via the designated application 185, asking
whether the
requesting user prefers a certain type of vehicle.
[0030] Based on the pick-up location, the locations of proximate AVs in the
fleet 190 or
other proximate human-driven service vehicles, and optionally a preferred
vehicle type,
the selection engine 135 can select a vehicle (e.g., AV 109) that fulfills the
criteria. In
certain aspects, the selection engine 135 can further utilize a mapping engine
175 to
identify a most optimal vehicle (e.g., AV 109) based on map data 179 (e.g., a
distance to
the pick-up location) and/or traffic data 177 (e.g., a time to reach the pick-
up location).
Upon selecting AV 109 as being the most optimal vehicle, the selection engine
135 can
transmit an invitation 182 to AV 109 to service the pick-up request 197. In
some
examples, AV 109 can accept or deny the invitation depending on a number of
factors
(e.g., remaining fuel or energy, service indicators, owner requirements,
etc.). In certain
implementations, when AV 109 accepts the invitation 182, the transport
facilitation
system 100 can utilize the map data 179 and traffic data 177 to provide AV 109
with
route information indicating a shortest or most optimal route to the pick-up
location.
Alternatively, AV 109 may be provided with local mapping resources to identify
the most
optimal route independently.
[0031] According to some examples described herein, the transport facilitation
system
100 can include a data analyzer 150 that can receive accelerometer data 181
and
location data (e.g., GPS data 183) from user devices 195 to determine user
attributes
153, such as an estimated height of a requesting user. In various
implementations, the
data analyzer 150 can further process the accelerometer data 181 and GPS data
183 to
estimate other high-level attributes 153 of the requesting user, such as
weight and body
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type (e.g., slim, normal, large). In still other implementations, the data
analyzer 150 can
further process the accelerometer data 181 and GPS data 183 to estimate or
determine
low level attributes 153 of the user, such as femur length, leg length,
posture
information, torso length, and the like.
[0032] For example, upon receiving a pick-up request 197 from a particular
user device
195, the transport facilitation system 100 can access accelerometer data 181
and GPS
data 183 (e.g., via the designated application 185) from an accelerometer and
GPS
module of the user device 195. In certain aspects, the accelerometer may be
housed in
an inertial measurement unit, and can provide a stream of accelerometer data
181
.. which the data analyzer 150 can process to determine or estimate the user's
attributes
153. Accordingly, when the requesting user places the device 195 in a pocket,
or
otherwise holds the device 195, and begins walking, the accelerometer data 181
can
include stride signatures, gait signatures, and/or sway signatures as well as
directional
acceleration peaks corresponding to each stride. The nature, timing,
magnitude, and
direction of the acceleration peaks¨ as well as the distance traveled (e.g., a
distance
walked by the user)¨ can be analyzed by the data analyzer 150 to determine the
requesting user's attributes 153, which may then be transmitted to the
configuration
engine 140. Based on the determine attributes 153 of the requesting user, the
configuration engine 140 can determine a vehicle configuration set 188 to
configure a
passenger seat of the selected AV 109 in order to provide the requesting user
with an
optimal comfort setting when entering the selected AV 109.
[0033] In determining the configuration set 188, the configuration engine 140
can
implement machine learning based on the user's attributes 153 determined from
the
accelerometer data 181 and GPS data 183 from the user's device 195. In certain
aspects,
the configuration engine 140 can access other similar comfort profiles 137
using the
user attributes 153. For example, the configuration engine 140 can perform a
lookup
142 in the database 130 using the requesting user's determined height, weight,
body
type, leg length, etc., to identify a set of matching comfort profiles 137 for
user's with
similar attributes. The configuration engine 140 can utilize the matching
comfort
profiles 137 as a basis for generating the configuration get 188 for the
requesting user.
In one example, the configuration engine 140 can rank a set of matching
comfort
profiles 137 based on similarity of user attributes, and utilize a top
grouping (e.g., the
top five or ten) to generate the configuration set 188. Additionally or
alternatively, the
configuration engine 140 can calculate and utilize average(s) of the
configuration
settings (e.g., seat adjustment and positioning settings) of the matching
comfort profiles
137, and generate the configuration set 188 for the requesting user based on
the
calculated averages.
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[0034] In examples, the configuration engine 140 can generate the vehicle
configuration
set 188 to map configurable parameters of an AV or service vehicle seat with
the user
attributes 153 determined from the accelerometer data 181 from the requesting
user's
device 195. For example, the configuration engine 140 can correlate the
determined or
estimated height of the requesting user with a fore-and-aft position of the
seat. The
configuration engine 140 can further correlate the determined or estimated
weight or
body type of the user with a backrest angle, a seat depth, and/or a seat
height. The
configuration engine 140 can further correlate a determined or estimated leg
length of
the requesting user with a thigh extension setting or a cushion edge
adjustment of the
seat in order to minimize knee and lower back strain. Further correlations
between the
determined or estimated attributes 153 of the requesting user with adjustable
parameters of the seat are also contemplated. For example, posture information
indicated in signatures of the accelerometer data 181 can be utilized by the
configuration engine 140 to generate a command to adjust cushion softness, a
lumbar
support element, a shoulder support element, a headrest angle, and the like.
[0035] The vehicle configuration set 188 can be transmitted to the selected AV
109 via
the vehicle interface 105 over a network 180. For example, the selection
engine 135 can
select AV 109 to service the pick-up request 197 from the requesting user, and
transmit
an invitation 182 to AV 109 via the vehicle interface 105. The data analyzer
150 can
process the accelerometer data 191 and the GPS data 183 from the requesting
user's
device 195 to determine the user attributes 153. The configuration engine 140
can map
the user attributes 153 to seat adjustment parameters of AV 109. In certain
examples,
the transport facilitation system 100 can store AV parameter logs 134 in the
database
130 that indicates all the adjustable parameters (e.g., adjustable seat
parameters) of
AVs in the fleet 190. The configuration engine 140 can lookup 142 the
adjustable seat
parameters of AV 109, map the user attributes 153 to various adjustments to a
seat of
AV 109 to maximize user comfort, generate the AV configuration set 188 to
include the
seat adjustments, and transmit the AV configuration set 188 to AV 109 via the
vehicle
interface 105 while AV 109 is en route to pick up the requesting user.
[0036] In certain implementations, the selection engine 135 can further assign
a
particular seat of AV 109 to the requesting user. In such implementations, the
configuration engine 140 can further indicate in the vehicle configuration set
188, the
particular seat assigned to the requesting user (e.g., the front right seat).
Once AV 109
receives the vehicle
configuration set 188, AV 109 can execute the configurations for the
particular seat
assigned to the requesting user prior to arriving at the pick-up location.
Additionally, the
selection engine 135 can generate and transmit a confirmation 199 to the
requesting
user's device 195. In certain aspects, the confirmation 199 can indicate
various
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attributes of AV 109 (or other selected service vehicle that services the
user's pick-up
request 197), such as the vehicle type, color, year, license plate number,
etc. The
confirmation 199 can be generated on a user interface of the designated
application 185
of the user device 195, and can further include data indicating the seat
assigned to the
-- requesting user. In certain aspects, the requesting user can accept the
confirmation 199,
or reject the confirmation¨ in which case the selection engine 135 can find an
alternative vehicle and the configuration engine 140 can generate a new
vehicle
configuration set 188 for the alternative vehicle accordingly. Thus, as the
selected AV
109 rendezvous with the requesting user, the requesting user can be presented
with
-- information indicating the configured seat based on the accelerometer data
181 and
GPS data 183 from the user's own device 195, and the seat can be preconfigured
for the
user to optimize comfort.
[0037] Additionally or alternatively, the designated application 185 on the
user device
195 can generate a preference menu 186 to enable the user to input
preferences, such
-- as climate control settings, seat temperature settings, radio station
settings, a preferred
seat in the selected AV 109 (e.g., for carpooling or vanpooling), lighting
settings, home
page settings for an interior display, moon roof or sunroof settings (e.g.,
open or
closed), window settings, ride control settings (e.g., sport mode or cautious
mode
autonomous driving), high level seat adjustment settings (e.g., upright or
relaxed
-- positions), and the like. In some examples, the preference menu 186 can be
generated
on the display screen of the user device 195 in response to transmitting a
pick-up
request 197.
[0038] In variations, the preference menu 186 can be customized based on the
configurable parameters of the AV selected by the AV selection engine 135.
Accordingly,
upon selecting AV 109 to service the pick-up request 197, the configuration
engine 140
can access the AV parameter log 134 for AV 109 to determine its configurable
parameters (e.g., whether AV 109 has satellite radio, a sunroof, 360 degree
seating
configurations, etc.), and the transport facilitation system 100 can generate
the
preference menu 186 for the requesting user based on the configurable
parameters of
-- AV 109. The user may interact with the preference menu 186, and transmit
the
selections 191 back the transport facilitation system 100, and the
configuration engine
140 can generate the vehicle configuration set 188 based on the selections on
the
preference menu 186, and transmit the vehicle configuration set 188 to AV 109,
as
described herein.
-- [0039] As further described, the vehicle configuration set 188 can comprise
a set of
configuration instructions to configure a number of adjustable components of
the
selected service vehicle for the user prior to the selected service vehicle
arriving at the
pick-up location. Accordingly, the vehicle can adjust, through automation, one
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CA 3040081 2019-04-11
components in accordance with the vehicle setup preferences while the service
vehicle
is traveling to the pickup location. Additionally, or alternatively, the
transport facilitation
system 100 can further communicate instructions to the service vehicle (e.g.,
AV 109) to
extend or delay a route taken by the service vehicle to reach the pickup
location in order
.. to operate at least one vehicle setup preference in accordance with the
comfort profile
137 or the configuration set 188.
[0040] Additionally or alternatively, the preference menu 186 can be generated
on the
designated application 185 at any time to enable the user the generally select
certain
preferences provided herein. The preference selections 191 (e.g., seat
position
information, temperature settings, etc.) can be transmitted to a log manager
125 of the
transport facilitation system 100 which can generate preference log updates
128 based
on the selections 191 for the user's comfort profile 137. As provided herein,
the
database 130 can store comfort profiles 137 for users of the transportation
arrangement service that can indicate the general preferences of the user, and
can be
accessed by the configuration engine 140 to, at least partially, generate the
AV
configuration set 188 for execution by the selected AV 109 while en route to
the pick-up
location. In some examples, the transport facilitation system 100 can
communicate to
the user that one or more features for implementing the user's comfort profile
137 is
not available on the selected service vehicle (e.g., AV 109).
.. [0041] Accordingly, the transport facilitation system 100 can manage rider
preference
logs 132 corresponding to users of the transportation arrangement service.
Each of the
rider preference logs 132 can include a preference or comfort profile 137 for
a particular
user, and can be categorized by the transport facilitation system 100 using
unique
identifiers 136 associated with the user devices 195. When a particular pick-
up request
197 is received, the configuration engine 140 can utilize the unique
identifier 136 for the
user device 195, and included with the pick-up request 197, to identify a
comfort profile
137 in a matching rider preference log 132. Furthermore, over time, the
transport
facilitation system 100 can determine (e.g., via machine learning techniques)
a set of
preferences for a particular user.
[0042] In one example, after the requesting user has been picked up by a
selected AV
109 from the fleet 190, the selected AV 109 (or other selected service
vehicle, such as a
human driven car or van) can transmit AV data 196 back to the transport
facilitation
system 100. Among other data items, the AV data 196 can include configuration
or
adjustment data 163 indicating user adjustments to the interior components
(e.g., radio,
seat positioning, configuration, and adjustments, temperature control, etc.).
The log
manager 125 can parse through the AV data 196 and log the adjustment data 163
in the
rider preference log 132 of that particular user. Overtime, the adjustment
data 163¨
included in the AV data 196 received from selected AVs that service pick-up
requests for
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that user¨ can indicate certain learned preferences of the user. In certain
implementations, the transport facilitation system 100 can include a pattern
recognition
engine 160 that can analyze the adjustment data 163 for the particular user
over a time
frame (or over the course of n rides), and identify distinct preferences in
the adjustment
data 163. When a particular preference pattern is identified in the adjustment
data 163
by the pattern recognition engine 160, the pattern recognition engine 160 can
generate
a comfort profile update 169 for the comfort profile 137 of the particular
user to include
the learned preference.
[0043] For example, over the course of n rides (e.g., fifteen, twenty, or
fifty rides), the
adjustment data 163 provided by servicing AVs can indicate that the user
typically
selects a certain radio station, adjusts the climate control system to a
specified
temperature range, prefers a certain set of seat adjustments, and/or accesses
a certain
set of software apps or browses a certain set of webpages. Individual
selections and
adjustments may be logged by the log manager 125 as preference log updates
128. Over
time, the pattern recognition engine 160 can identify patterns, or score
certain
preferences via a scoring technique. Once a particular preference achieves a
certain
threshold (e.g., crosses a certainty probability threshold or score), the
pattern
recognition engine 160 can edit the comfort profile 137 of the user by
generating a
profile update 169 reflecting the determined preference. Thereafter, when the
user
requests pick-up, the configuration engine 140 can generate the AV
configuration set
188 to include the preference determined by the pattern recognition engine 160
in the
profile update 169. This learned preference can be a particular seat
arrangement, a
temperature setting, a radio station setting, and/or other adjustments,
configurations,
or other settings described herein.
[0044] Further, after each AV ride, the designated application 185 on the user
device
195 can enable the user to provide feedback 193 regarding the ride. In certain
implementations, the feedback 193 can include a simple rating system (e.g.,
between
one to five stars) that the user can utilize to rate user experience for the
ride. The
pattern recognition engine 160 can receive the feedback 193 from the user
device 195
to attempt to make correlations between the user's experience for a particular
ride, and
certain occurrences or ride characteristic during the ride itself. As
described above, the
transport facilitation system 100 can receive AV data 196 from servicing AVs
while
servicing pick-up requests. In addition to including adjustment data 163, the
AV data
196 can indicate various control commands and sensor data (e.g., accelerometer
data,
image data, control system mode, etc.) that can indicate a potential
correlation with a
particular rating.
[0045] For example, AVs in the fleet 190 can operate in certain modes while
remaining
within legal and safety constraints. In some examples, the pattern recognition
engine
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160 can correlate (e.g., over the course of several rides) high ratings with
cautious travel
for a nervous rider. In some examples, once the correlation reaches a
predetermined
threshold (e.g., 75% certainty) the pattern recognition engine 160 can
generate a profile
update 169 for the nervous rider's comfort profile 137 indicating a mandatory
requirement that selected AVs only travel according to a certain ride
characteristic, such
as a high caution mode when servicing requests for the nervous rider. Thus,
when the
nervous rider submits a pick-up request 197 and AV 109 is selected to service
the
request 197 by the selection engine 135, the configuration engine 140 can
perform a
lookup 142 in the nervous rider's comfort profile 137 to identify vehicle
setup
3.0 preferences, such as the AV setup preferences 133 of the nervous rider.
As described
herein, the AV setup preferences 133 can also be directly inputted by the
nervous rider
in a preference menu 186 before or after submitting the pick-up request 197.
Furthermore, the comfort profile 137 for the nervous rider can indicate other
configuration preferences, such as radio settings, seat configuration
settings,
temperature settings, etc.¨ and the learned preference that the nervous rider
prefers a
cautious AV ride. The configuration engine 140 can generate and transmit the
vehicle
configuration set 188 to include an instruction for AV 109 to operate in a
high caution
mode (e.g., drive at slower speeds and implement smooth braking, acceleration
and
steering) when driving the nervous rider from the pick-up location to the
rider's
destination.
[0046] More generally, AV data 196 (or other service vehicle data) from
selected AVs
providing transportation to a particular user can be analyzed by the pattern
recognition
engine 160 in light of feedback 193 (e.g., user experience ratings) provided
by that
particular user. The feedback 193 need not be limited to only ratings data,
but can
include voluntary comments and/or survey data as well. In some aspects, the
pattern
recognition engine 160 can be triggered to analyze AV data 196 for a
particular trip
when the user submits a rating below a certain threshold (e.g., two out of
five stars).
The pattern recognition engine 160 can analyze the AV data 196 for anomalous
events,
instances, and/or ride characteristics that are potentially responsible for or
contributed
to the low rating.
[0047] For example, the pattern recognition engine 160 can identify
accelerometer data
in the AV data 196 that indicates hard braking on a number of occasions. As
another
example, the pattern recognition engine 160 can further determine a time
versus
distance delta between pick-up and the drop-off that may indicate whether the
AV
traveled too quickly or too slowly in transporting the user. Over the course
of n rides,
the pattern recognition engine 160 may identify one or more potential causes
for low
ratings indicated by the user by analyzing the AV data 196 for each trip. As
provided
herein, once the potential causes achieve a certain threshold (e.g., 75%
probability of
being a cause for the low rating), the pattern recognition engine 160 can
generate a
13
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profile update 169 to edit the comfort profile 137 of the user to mitigate or
alleviate the
cause in future trips. Such potential causes can include patterns of anomalous
braking,
swerving, and/or acceleration (e.g., as measured above a certain g-force
threshold),
overly cautious or overly assertive driving (e.g., driving in a normal mode
versus a
cautious mode versus a high caution mode), and the like. Thus, the comfort
profile 137
for the user can also include negative preferences, such as a negative
preference that
prevents a selected AV 109 from operating in a high caution mode for the user,
which
can cause the selected AV 109 to optimize trip time.
[0048] Along these lines, the pattern recognition engine 160 can analyze
adjustment
data 163 over time (or over the course of n trips by the user) to identify
ranges or
bounds for certain component parameters. For example, a user may never adjust
the air
temperature of the interior above seventy degrees, or may never adjust the
fore-aft
parameter of the seat beyond a certain forward position. The pattern
recognition
engine 160 can compile such adjustment data 163 in the user's preference log
132, and
can calculate a probability (e.g., for each successive trip) that the user
will not exceed
these determined ranges or boundaries. Once the calculated probability reaches
a
certain threshold (e.g., 90% certainty probability), the pattern recognition
engine 160
can amend the user's comfort profile 137 with a profile update 169 indicating
the set
ranges or bounds. Thus, for subsequent rides, the configuration engine 140 can
refer to
the set ranges and bounds of the user's comfort profile 137 to determine
whether any
adjustable parameters of the selected AV 109 are outside such ranges or
bounds. If so,
the configuration engine 140 can generate the AV configuration set 188 to
include an
adjustment command for the selected AV 109 to adjust those parameters to be
within
the ranges or bounds indicated in the user's comfort profile 137.
[0049] Accordingly, a user of the transportation arrangement service managed
by the
transport facilitation system 100 can input initial preferences 191 prior to
using the
service, or after each transmitted pick-up request 197 via the preference menu
186 on
the designated application 185. Additionally or alternatively, the data
analyzer 150 of
the transport facilitation system 100 can automatically determine or estimate,
based on
accelerometer data 181 and GPS data 183 from the user's device 195, the user's
attributes 153, such as the user's height, weight, body type, leg length,
femur length,
posture, and the like. The configuration engine 140 can generate the
configuration set
188 to include seat adjustment commands based on these determined attributes.
Additionally or alternatively still, the user can provide feedback 193 or make
adjustments during AV rides, which can trigger pattern recognition that can
edit or
amend the user's comfort profile 137 accordingly. The configuration engine 140
can
generate a configuration set 188 based on the user's comfort profile 137, and
transmit
the configuration set 188 to the selected AV 109 (or selected human-driven
service
vehicle) which can configure the interior components prior to picking up the
user
14
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accordingly. In certain implementations, the configuration engine 140 can
further
identify a particular seat within the selected AV 109 or service vehicle to be
configured
for the user, and the selection engine 135 can transmit a confirmation 199 to
the user
device 195 indicating the assigned seat. In one example, the transport
facilitation
system 100 can determine a seat assignment within the service vehicle for the
user
based at least in part on the comfort profile 137 of the user. Additionally,
the transport
facilitation system 100 can communicate the seat assignment to the user device
195
prior to pick-up. In further implementations, the transport facilitation
system 100 can
select a route for the service vehicle to the pickup location based on the
seat
assignment of the user.
[0050] In some examples, the transport facilitation system 100 can transmit
route
commands to route the selected AV 109 (or service vehicle) such that the AV
109 (or
service vehicle) picks up the user with the assigned seat corresponding to the
road curb
at the pick-up location (e.g., the assigned seat being on the side of the AV
109 in which
the user will enter from a sidewalk or curb). In further examples, the
configuration set
188 can further include audio adjustment commands to configure an audio focal
point
of the AV 109 to match the assigned seat of the user. In still further
examples, the AV
data 196 can include seat sensor data indicating a position of the user within
the AV 109
(e.g., when the user changes seats). In response, the configuration engine 140
can
transmit an audio configuration command to adjust the audio focal point based
on the
position of the user within the AV 109. In examples described below, one or
more
automatic configurations described with respect to FIG. 1, may be determined,
generated, and executed by the selected AV 109 itself.
[0051] FIG. 2 is a block diagram illustrating an example AV implementing a
control
system, as described herein. In an example of FIG. 2, a control system 220 can
be used
to autonomously operate the AV 200 in a given geographic region for a variety
of
purposes, including transport services (e.g., transport of humans, delivery
services, etc.).
In examples described, an autonomously driven vehicle can operate without
human
control. For example, in the context of automobiles, an autonomously driven
vehicle can
steer, accelerate, shift, brake, and operate lighting components. Some
variations also
recognize that an autonomous-capable vehicle can be operated either
autonomously or
manually.
[0052] One or more components described with respect to FIG. 2 may be
attributed to a
human-driven service vehicle, such as a car or van. For example, the service
vehicle can
include a wireless communication interface to communicate with a backend,
transport
facilitation system 100, such as those descrilqed with respect to FIG. 1.
Furthermore, the
service vehicle can include a number of adjustable components that affect a
seating or
an environment of the vehicle, and a controller to control a setting for each
of the
CA 3040081 2019-04-11
adjustable components (e.g., lighting, seat adjustments, radio, etc.). Thus,
as described
herein in with respect to the AV 200, the controller of the service vehicle
(whether an
AV or a human-driven vehicle) can receives a set of instructions from a
network service
(e.g., transportation arrangement service provided by the transport
facilitation system
100) via the wireless communication interface, and autonomously implement,
while the
vehicle is in motion, a comfort profile in at least one passenger zone about
one seat of
the vehicle, by adjusting a setting of one or more of the components in
accordance with
the set of instructions.
[0053] In one implementation, the control system 220 can utilize specific
sensor
resources in order to intelligently operate the vehicle 200 in most common
driving
situations. For example, the control system 220 can operate the vehicle 200 by
autonomously steering, accelerating, and braking the vehicle 200 as the
vehicle
progresses to a destination. The control system 220 can perform vehicle
control actions
(e.g., braking, steering, accelerating) and route planning using sensor
information, as
well as other inputs (e.g., transmissions from remote or local human
operators, network
communication from other vehicles, etc.).
[0054] In an example of FIG. 2, the control system 220 includes a computer or
processing system which operates to process sensor data that is obtained on
the vehicle
with respect to a road segment upon which the vehicle 200 operates. The sensor
data
can be used to determine actions which are to be performed by the vehicle 200
in order
for the vehicle 200 to continue on a route to a destination. In some
variations, the
control system 220 can include other functionality, such as wireless
communication
capabilities, to send and/or receive wireless communications with one or more
remote
sources. In controlling the vehicle 200, the control system 220 can issue
instructions and
data, shown as commands 235, which programmatically control various
electromechanical interfaces of the vehicle 200. The commands 235 can serve to
control
operational aspects of the vehicle 200, including propulsion, braking,
steering, and
auxiliary behavior (e.g., turning lights on). In examples described herein,
the commands
235 can further serve to control configurable interior systems of the AV 200
via a
component interface 255, such as seating configurations, seat positioning,
seat
adjustment, seat heating or cooling, radio station selections, a display
setup, a climate
control system, an interior lighting system, windows, and/or a sunroof or moon
roof.
[0055] In examples described herein, the AV 200 can include a wireless
communication
interface to communicate with the backend, transport facilitation system 100
described
with respect to FIG. 1.
[0056] The AV 200 can be equipped with multiple types of sensors 201, 203
which can
combine to provide a computerized perception of the space and environment
16
CA 3040081 2019-04-11
surrounding the vehicle 200. Likewise, the control system 220 can operate
within the AV
200 to receive sensor data 211 from the collection of sensors 201, 203, and to
control
various electromechanical interfaces for operating the vehicle 200 on
roadways.
[0057] In more detail, the sensors 201, 203 operate to collectively obtain a
complete
-- sensor view of the vehicle 200, and further to obtain situational
information proximate
to the vehicle 200, including any potential hazards proximate to the vehicle
200. By way
of example, the sensors 201, 203 can include multiple sets of cameras sensors
201
(video cameras, stereoscopic pairs of cameras or depth perception cameras,
long range
cameras), remote detection sensors 203 such as provided by radar or LIDAR,
proximity
-- or touch sensors, and/or sonar sensors (not shown).
[0058] Each of the sensors 201, 203 can communicate with the control system
220
utilizing a corresponding sensor interface 210, 212. Each of the sensor
interfaces 210,
212 can include, for example, hardware and/or other logical components which
are
coupled or otherwise provided with the respective sensor. For example, the
sensors
-- 201, 203 can include a video camera and/or stereoscopic camera set which
continually
generates image data of an environment of the vehicle 200. As an addition or
alternative, the sensor interfaces 210, 212 can include a dedicated processing
resource,
such as provided with a field programmable gate array ("FPGA") which can, for
example,
receive and/or process raw image data from the camera sensor.
-- [0059] In some examples, the sensor interfaces 210, 212 can include logic,
such as
provided with hardware and/or programming, to process sensor data 209 from a
respective sensor 201, 203. The processed sensor data 209 can be outputted as
sensor
data 211. As an addition or variation, the control system 220 can also include
logic for
processing raw or pre-processed sensor data 209.
-- [0060] According to one implementation, the vehicle interface subsystem 250
can
include or control multiple interfaces to control mechanisms of the vehicle
200. The
vehicle interface subsystem 250 can include a propulsion interface 252 to
electrically (or
through programming) control a propulsion component (e.g., an accelerator
pedal), a
steering interface 254 for a steering mechanism, a braking interface 256 for a
braking
-- component, and a lighting/auxiliary interface 258 for exterior lights of
the vehicle.
According to implementations described herein, control signals 249 can further
be
transmitted to a component interface 255 of the vehicle interface subsystem
250 to
control various components of the AV 200 based on user preferences or
attributes. The
vehicle interface subsystem 250 and/or the control system 220 can further
include one
-- or more controllers 240 which can receive commands 233, 235 from the
control system
220. The commands 235 can include route information 237 and operational
parameters
239¨ which specify an operational state of the vehicle 200 (e.g., desired
speed and
17
CA 3040081 2019-04-11
pose, acceleration, etc.). The commands can further include personalization
commands
233 to cause the controller 240 to configure a number of adjustable components
of the
AV 200 via the component interface 255.
[0061] The controller(s) 240 can generate control signals 249 in response to
receiving
the commands 233, 235 for one or more of the vehicle interfaces 252, 254, 255,
256,
258. The controllers 240 can use the commands 235 as input to control
propulsion,
steering, braking, and/or other vehicle behavior while the AV 200 follows a
current
route. Thus, while the vehicle 200 actively drives along the current route,
the
controller(s) 240 can continuously adjust and alter the movement of the
vehicle 200 in
response to receiving a corresponding set of commands 235 from the control
system
220. Absent events or conditions which affect the confidence of the vehicle
220 in safely
progressing along the route, the control system 220 can generate additional
commands
235 from which the controller(s) 240 can generate various vehicle control
signals 249 for
the different interfaces of the vehicle interface subsystem 250.
[0062] According to examples, the commands 235 can specify actions to be
performed
by the vehicle 200. The actions can correlate to one or multiple vehicle
control
mechanisms (e.g., steering mechanism, brakes, etc.). The commands 235 can
specify the
actions, along with attributes such as magnitude, duration, directionality, or
other
operational characteristics of the vehicle 200. By way of example, the
commands 235
generated from the control system 220 can specify a relative location of a
road segment
which the AV 200 is to occupy while in motion (e.g., change lanes, move into a
center
divider or towards shoulder, turn vehicle, etc.). As other examples, the
commands 235
can specify a speed, a change in acceleration (or deceleration) from braking
or
accelerating, a turning action, or a state change of exterior lighting or
other
components. The controllers 240 can translate the commands 235 into control
signals
249 for a corresponding interface of the vehicle interface subsystem 250. The
control
signals 249 can take the form of electrical signals which correlate to the
specified vehicle
action by virtue of electrical characteristics that have attributes for
magnitude, duration,
frequency or pulse, or other electrical characteristics. [0063] In an example
of FIG. 2, the
control system 220 can include a route planner 222, event logic 224,
personalization
logic 221, optimization logic 275, and a vehicle control 228. The vehicle
control 228
represents logic that converts alerts of event logic 224 ("event alert 229")
into
commands 235 that specify a set of vehicle actions.
[0064] Additionally, the route planner 222 can select one or more route
segments 226
that collectively form a path of travel for the AV 200 when the vehicle 200 is
on a
current trip (e.g., servicing a pick-up request). In one implementation, the
route planner
222 can specify route segments 226 of a planned vehicle path which defines
turn by turn
directions for the vehicle 200 at any given time during the trip. The route
planner 222
18
CA 3040081 2019-04-11
may utilize the sensor interface 212 to receive GPS information as sensor data
211. The
vehicle control 228 can process route updates from the route planner 222 as
commands
235 to progress along a path or route using default driving rules and actions
(e.g.,
moderate steering and speed).
[0065] In certain implementations, the event logic 224 can trigger a response
to a
detected event. A detected event can correspond to a roadway condition or
obstacle
which, when detected, poses a potential hazard or threat of collision to the
vehicle 200.
By way of example, a detected event can include an object in the road segment,
heavy
traffic ahead, and/or wetness or other environmental conditions on the road
segment.
The event logic 224 can use sensor data 211 from cameras, LIDAR, radar, sonar,
or
various other image or sensor component sets in order to detect the presence
of such
events as described. For example, the event logic 224 can detect potholes,
debris,
objects projected to be on a collision trajectory, and the like. Thus, the
event logic 224
can detect events which enable the control system 220 to make evasive actions
or plan
for any potential hazards.
[0066] When events are detected, the event logic 224 can signal an event alert
229 that
classifies the event and indicates the type of avoidance action to be
performed.
Additionally, the control system 220 can determine whether an event
corresponds to a
potential incident with a human driven vehicle, a pedestrian, or other human
entity
external to the AV 200. In turn, the vehicle control 228 can determine a
response based
on a score or classification of the event. Such response can correspond to an
event
avoidance action 223, or an action that the vehicle 200 can perform to
maneuver the
vehicle 200 based on the detected event and its score or classification. By
way of
example, the vehicle response can include a slight or sharp vehicle
maneuvering for
avoidance using a steering control mechanism and/or braking component. The
event
avoidance action 223 can be signaled through the commands 235 for controllers
240 of
the vehicle interface subsystem 250.
[0067] When an anticipated dynamic object of a particular class does in fact
move into
position of likely collision or interference, some examples provide that event
logic 224
can signal the event alert 229 to cause the vehicle control 228 to generate
commands
235 that correspond to an event avoidance action 223. For example, in the
event of a
bicycle crash in which the bicycle (or bicyclist) falls into the path of the
vehicle 200, the
event logic 224 can signal the event alert 229 to avoid the collision. The
event alert 229
can indicate (i) a classification of the event (e.g., "serious" and/or
"immediate"), (ii)
information about the event, such as the type of object that generated the
event alert
229, and/or information indicating a type of action the vehicle 200 should
take (e.g.,
location of object relative to path of vehicle, size or type of object, etc.).
19
CA 3040081 2019-04-11
[0068] According to examples described herein, AV 200 can include a
communications
array 214 to communicate over one or more networks 280 with a backend,
transport
facilitation system 290, such as the transport facilitation system 100
described with
respect to FIG. 1. When the AV 200 is selected to service a pick-up request,
the
communications array 214 can receive a transport invitation 213 from the
transport
facilitation system 290 to service the pick-up request and drive to a pick-up
location to
rendezvous with the requesting user. In many aspects, the transport invitation
213 can
be transmitted to the route planner 222 in order to autonomously drive the AV
200 to
the pick-up location. In conjunction with or subsequent to receiving the
transport
invitation 213, the communications array 214 can receive an AV configuration
set 218
from the transport facilitation system 290 to personalize the various
configurable
components of the AV 200 for the upcoming rider.
[0069] The AV configuration set 218 can be processed by the personalization
logic 221
of the control system 221 which can generate a set of personalization commands
233
for execution by a controller 240 for the component interface 255. In certain
implementations, the personalization logic 221 can be executed by the control
system
220 in concert with optimization logic 275 in order to execute the
personalization
commands 233 in a timely manner. Such timing characteristics may be beneficial
in the
overall power and energy optimization and management by the AV 200. As an
illustrative example, the AV 200 may operate in a hot, desert climate and
receive a
transport invitation 213 for a pick-up location several miles (e.g., ten
miles) from a
current location. The AV configuration set 218 may indicate a user preference
for a cool
interior that requires energy intensive use of the AV's air conditioning
system. The
optimization logic 275 can generate timing data 231 for the controller 240 to
execute
the climate control aspects of the personalization commands 233 such that the
cool
desired temperature is achieved just prior to the AV 200 arriving at the pick-
up location.
Thus, while the controller 240 can execute personalization commands 233 for
certain
components immediately (e.g., seat configuration and positioning), the
controller 240
can execute other personalization commands 233 as constrained by the timing
data 231
generated by the optimization logic 275 (e.g., the climate control system,
audio and
display systems, etc.).
[0070] Execution of the personalization commands 233 by the controller 240 can
configure AV components¨ such as the audio system (e.g., radio station(s),
volume,
audio focal point), the display system (e.g., displaying a home page or having
desired
content pre-set for viewing), windows/sunroof (e.g., open, partially open, or
closed),
lighting system (e.g., mood lighting, reading lights, colored lights, and/or
brightness),
seat configuration (e.g., front seat(s) rotated rearwards for multiple
passengers), seat
positioning (e.g., adjustments to fore-aft position, a backrest angle, a thigh
extension
length, a headrest angle, a headrest level, a lumbar position, a seat depth, a
seat height,
CA 3040081 2019-04-11
an upper seat tilt angle, or shoulder support), seat temperature, mirror
positions,
and/or a climate control system (e.g., air temperature, and temperature focus
based on
user location within the AV 200). As described, the personalization commands
233 for
any one of the foregoing configurable components can be time-constrained by
the
optimization logic 275 in order to optimize energy usage by the AV 200.
[0071] In certain aspects, the AV configuration set 218 can also include a
control mode
preference for high level operation of the AV 200 through road traffic. For
example, the
AV configuration set 218 can indicate a preferred mode (e.g., a high caution
mode for an
elderly rider) or a negative preference (e.g., avoid high caution mode for a
work
commuter). The personalization logic 221 can submit the control mode 291
information
to the vehicle control 228, which can adjust general control parameters in
operating the
braking, acceleration, and steering systems of the AV 200. For example, a
preferred high
caution mode can cause the vehicle control 228 to increase relative braking
distances
and/or provide more gentle acceleration to increase rider comfort.
[0072] As the AV 200 transports the rider to a specified destination, the
rider may make
adjustments to the various configurable components that, over time or over the
course
of n trips, may indicate certain preference patterns. Thus, the
personalization logic 221
can also monitor such adjustment data 242, and transmit the adjustment data
242 back
to the transport facilitation system 290 for pattern analysis, as described
herein.
[0073] In some examples, the control system 220 can further include a data
compiler
227 that can compile AV data 262 that indicates information directed to trips
that can
include potential causes of a high or low rating provided by a rider. In
certain aspects,
the data compiler 227 can be programmed to identify anomalous instances, such
as
those correlated to event avoidance actions 223. Additionally or
alternatively, over time
or over the course of n trips, the AV data 262¨ included with AV data from
various
other servicing AV s providing transport for the rider¨ can include
information that
indicates general ride preferences of a user without the user providing
explicit feedback.
Thus, the AV data 262 can be streamed or periodically transmitted back to the
transport
facilitation system 290 for pattern analysis, as described herein.
[0074] FIG. 3 is a block diagram illustrating an example mobile computing
device
executing a designated application for a transport arrangement service, as
described
herein. The mobile computing device 300 can store a designated application
(e.g., a
rider app 332) in a local memory 330. In response to a user input 318, the
rider app 332
can be executed by a processor 340, which can cause an app interface 342 to be
generated on a display screen 320 of the mobile computing device 330. The app
interface 342 can enable the user to, for example, check current price levels
and
availability for the transportation arrangement service. In various
implementations, the
21
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app interface 342 can further enable the user to select from multiple ride
services, such
as a carpooling service, a regular rider service, a professional rider
service, a van
transport service, a luxurious ride service, and the like. Example services
that may be
browsed and requested can be those services provided by UBER Technologies,
Inc. of
San Francisco, California.
[0075] The user can generate a pick-up request 367 via user inputs 318
provided on the
app interface 342. For example, the user can select a pickup location, view
the various
service types and estimated pricing, and select a particular service for
transportation to
an inputted destination. In many examples, the user can input the destination
prior to
pick-up. The processor 340 can transmit the pick-up request 367 via a
communications
interface 310 to the backend transport facilitation system 399 over a network
380. In
response, the mobile computing device 300 can receive a confirmation 369 from
the
transport facilitation system 399 indicating the selected AV that will service
the pick-up
request 367 and rendezvous with the user at the pick-up location.
[0076] In certain implementations, the confirmation 369 and or the pickup
request 367
can trigger the mobile computing device 300 to begin transmitting
accelerometer data
352 and location data 362 from an inertial measurement unit 350 and GPS unit
or
module 360 of the mobile computing device 300. The transport facilitation
system 399
can analyze the location data 362 and the accelerometer data 352 to determine
a set of
user attributes 317 for the user in order to configure a seat of the selected
AV
accordingly, as described herein. In variations, the processor 340 of the
mobile
computing device 300 can receive and analyze the accelerometer data 352 and
the
location data 362 and determine or estimate the set of user attributes 317. In
doing so,
the processor 340 can analyze peak signatures in the accelerometer data 352
and
correlate such signatures to a distance walked from the location data 362 to
determine
or estimate such attributes as a height, weight, gait pattern, body type,
posture, leg
length, femur length, and/or torso length. Once calculated, the processor can
transmit
the user attributes 317 to the transport facilitation system 399 over the
network 380 in
order to enable the transport facilitation system 399 configure the AV seat
accordingly.
[0077] In one or more examples, the rider app 332 can also generate an AV
configuration interface 344 so that the user can set preferences and/or
configure the
interior components of the AV prior to pick-up. In one aspect, the AV
configuration
interface 344 can be generated automatically after the pick-up request 367 is
transmitted. In variations, the AV configuration interface 344 can be
initiated via user
input 318 on the app interface 342. The user can utilize the AV configuration
interface
344 to set preferences for air temperature, high level seating preferences
(e.g., relaxed
versus upright), seat temperature, radio station settings, display settings
(e.g., a
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particular program or content setup and/or a home page), interior lighting, a
travel
mode (e.g., increasing ride comfort versus minimizing travel time), and the
like.
[0078] In various examples, after each ride, the app interface 342 can enable
the user to
provide feedback 322 for the ride. The feedback 322 can include an overall
user
experience rating for the ride (e.g., between 1 and 5 stars), and/or can
include a survey
or comments section to provide additional feedback. The processor 340 can
transmit
the feedback data 322 to the transport facilitation system 399 for AV data
analysis or
preference pattern recognition.
[0079] METHODOLOGY
[0080] In the below descriptions of FIGS. 4A, 4B, 5A, 5B, and 6, reference may
be made
to reference characters representing like features from FIGS. 1 through 3.
Furthermore,
the processes described below in connection with FIGS. 4A and 4B, and FIGS. 5A
and 5B,
may be performed by an example transport facilitation system 100 as shown and
described with respect to FIG. 1. Further still, the operations illustrated in
FIGS. 4A and
4B¨ and FIGS. 5A and 5B as described below¨ need not be performed in any
particular
order. Accordingly, certain processes or operation sets discussed below and
illustrated
in the flow chart examples of FIGS. 4A, 4B, and FIGS. 5A and 5B can be
performed prior
to, concurrently with, or subsequent to other processes or operation sets¨ as
illustrated by reference circles "A" and "B" in FIGS. 4A and 4B, and FIGS. 5A
and 5B.
[0081] Additionally, the example transport facilitation system 100 performing
the
operations of FIGS. 4A and 4B, and FIGS. 5A and 5B, can store preference logs
132 and
comfort profiles 137 that include data indicating potential preferences for
users (e.g.,
implementing machine learning over time) and/or comfort or preference settings
initially determined by the transport facilitation system 100 (e.g., using
accelerometer
and location data from the rider's mobile computing device 195), inputted by
the user
(e.g., via a preference menu 186), or determined over time by the transport
facilitation
system 100 (e.g., via machine learning and/or pattern recognition).
[0082] FIGS. 4A and 4B are flow charts describing example methods of utilizing
a
comfort profile for configuring an AV for a user, according to examples
described herein.
'Referring to FIG. 4A, the transport facilitation system 100 can receive a
pick-up request
197 from a user device 195 (400). In many aspects, the pick-up request 197 can
indicate
a unique identifier 136 for the user (402) (e.g., an application identifier,
account
identifier, and/or phone identifier) and a pick-up location (404). In certain
aspects, the
transport facilitation system 100 can utilize the pick-up location to select
an AV 109 to
service the pick-up request (405). For example, the AV 109 may be selected by
the
transport facilitation system 100 based on proximity to the pick-up location,
to
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estimated time of arrival to the pick-up location (e.g., determined by traffic
conditions).
In variations, the transport facilitation system 100 can filter the AV
selection based on a
preferred vehicle type or service type indicated by the user (e.g., via direct
input on the
designated app 185 or via lookup 142 in the comfort profile 137). Once the AV
109 is
selected (and the invitation 182 to service the request is accepted), the
transport
facilitation system 100 can transmit the pick-up location to the AV 109 to
enable the
rendezvous (410).
[0083] Using the unique identifier 136, the transport facilitation system 100
can perform
a lookup 142 in the database 130 for the requesting user's comfort profile 137
(415). As
described herein, the comfort profile 137 can indicate AV configuration or
setup
preferences 133 for the requesting user, as described herein. Additionally or
alternatively, the transport facilitation system 100 can receive AV
configuration
preferences from a preference menu 186 on the user device 195 (420). For
example, the
user can input setup preferences indicating high level preferences (e.g., warm
or cool
interior temperature, relaxed or upright seating, etc.) when setting up an
account with
the transportation arrangement service, or after each pick-up request 197.
Based on the
configuration preferences in the comfort profile 137 and/or received from the
user
device 195, the transport facilitation system 100 can generate an AV
configuration set
188 (425).
[0084] As described herein, the AV configuration set 188 can include
adjustment
parameters for various configurable components of the selected AV 109. For
example,
the AV configuration set 188 may indicate a seating configuration (432) that
the AV 109
is to execute prior to arriving at the pickup location. The AV 109 may include
front seat
motors that can pivot the front seats rearward. Thus, for a user that requests
transportation for a group, the user can input the seating configuration
preference into
the preference menu 186 to pivot the front seats rearward. In response, the
transport
facilitation system 100 can incorporate the seating configuration preference
into the AV
configuration set 188.
[0085] Additionally or alternatively, the AV configuration set 188 can include
adjustments to the seat(s), such as a temperature setting or a specified seat
position
(434), as described herein. The AV configuration set 188 can further include
climate
and/or temperature settings for the interior of the AV 109 (436). In some
examples, the
climate setting can indicate a temperature for the entire interior of the AV
109.
Alternatively, the climate setting can indicate a localized temperature based
on the
user's assigned location within the AV 109 (e.g., for pooled rides). Thus,
when the
transport facilitation system 100 routes the AV 109 to pick-up multiple riders
over the
course of a trip, the AV configuration set 188 for each rider can indicate a
localized
climate control setting for the seat assigned to each respective rider.
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[0086] In certain aspects, the AV configuration set 188 can include visual
settings to
configure a display and/or interior lighting of the AV 109 (438). For example,
the visual
settings can cause the AV 109 to implement mood lighting (e.g., configuring a
certain
color and/or brightness), turn on a reading light for the user's assigned
seat, have a
display set up with a home page or content requested by the rider.
Additionally or
alternatively, the AV configuration set 188 can indicate audio settings, such
as a
preferred radio station or a set of radio station selections for the rider,
and/or can
indicate a desired volume (440). Further, the audio settings may include an
audio focal
point setting to balance and fade the audio system to focus the sound on the
rider's
assigned seat.
[0087] In further aspects, the AV configuration set 188 can include preferred
settings for
windows, a sunroof or moon-roof, mirrors, a convertible setting (e.g., to
close or open a
top of a convertible AV), and the like (442). Once the AV configuration set
188 has been
generated, the transport facilitation system 100 can transmit the AV
configuration set
.. 188 to the selected AV 109 so that the AV 109 can configure the interior
components to
the preferred settings prior to picking up the rider.
[0088] Referring to FIG. 4B, the transport facilitation system 100 can service
pick-up
requests for users throughout a given region over time and over the course of
any
number of rides or over a given duration (450). Furthermore, as described
herein, the
transport facilitation system 100 can store comfort profiles 137 for those
users that
indicate AV configuration and/or setup preferences 133 (452). In certain
implementations, a respective user's comfort profile 137 can include the
user's
attributes 153, such as the user's height, weight, and body type. In one
example, the
transport facilitation system 100 can classify the comfort profiles 137 in the
database
130 based on user attributes 153 in order to perform readily identify and/or
search for
matching sets of comfort profiles 137 (e.g., to generate a new configuration
set 188 for
a new user). For a given user of the transportation arrangement service, the
transport
facilitation system 100 can receive feedback data 193 (e.g., ratings) after
some or all of
the rides (454). In some aspects, the transport facilitation system 100 can
determine
whether a current rating for a particular ride (e.g., a present ride just
after drop-off) is
below a certain threshold (e.g., two out of five stars) (456). If so (457),
then the low
rating can trigger an analysis of the AV ride itself. In variations, the
transport facilitation
system 100 can analyze data from each ride regardless of the rating in order
to identify
or correlate certain aspects or instances of the ride that may have
contributed to the
rating.
[0089] Thus, the transport facilitation system 100 can analyze AV data 196 for
the ride
for potential causes for the rating (e.g., whether the rating is low or high)
or anomalous
instances that may be responsible for the rating
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(458). The transport facilitation system 100 can further analyze historical
data in the
rider's preference log 132 to determine whether a pattern exists between the
present
trip and past trips (460) (e.g., a ride characteristic preference). For
example, the rider
may have a history of inputting low ratings for rides that include a certain
ride
characteristic (e.g., the AV traveling too slowly when operating in a high
caution mode,
or where instances of hard braking or acceleration are present in the AV data
196 for
the ride). The transport facilitation system 100 can determine whether a
pattern or a
correlation exists (462). If not (463), the transport facilitation system 100
can log
possible correlations between any number of potential causes or anomalies
responsible
for the rating in the rider preference log 132 for future reference (466).
However, if a
distinct correlation is found (464), then the transport facilitation system
100 can
calculate whether the correlation exceeds a certainty probability threshold
(468) (e.g., a
75% certainty that the characteristic or anomalous instance contributed to the
rating).
[0090] If the identified correlation does not exceed the threshold (467), then
the
correlation can be logged in the rider's preference log for future reference
(466).
However, if the identified correlation does exceed the probability threshold
(469), then
the transport facilitation system 100 can amend or edit the rider's comfort
profile 137 to
include a learned preference not previously indicated by the rider (470). As
an example,
the rider may show a history of inputting low ratings when small, compact AVs
are
selected to transport the rider. Over a number of trips indicating a
correlation between
small, compact AVs and the low ratings, the transport facilitation system 100
can amend
the rider's comfort profile 137 to include a negative preference to avoid
selecting small,
compact AVs in the future. Thus, prior to selecting an AV 109 to service a
received pick-
up request 197, the transport facilitation system 100 can consult the rider's
comfort
profile 137 to filter out small, compact AVs.
[0091] Referring still to FIG. 4B, the transport facilitation system 100 can
receive
adjustment data 163¨ during or after the ride¨ corresponding to adjustments
made by
the rider to the interior systems and components of the AV 109 (472). The
adjustment
data 196 can indicate adjustments made to, for example, the seat temperature,
configuration, or position (474), the climate control system (476), the audio
system
(478), and/or the visual system (e.g., interior lights or the display) (479).
The transport
facilitation system 100 can compile the adjustment data 163 in the rider's
preference
log 132 for pattern recognition analysis (480). Over time, the compiled
adjustment data
163 can indicate certain patterns by the user that may reflect new or
alternative
preferences. Accordingly, the transport facilitation system 100 can analyze
historical
adjustment data 163 in the preference log 132 (482) to determine whether a
pattern
exists that may indicate a preference (484). If no pattern is identified
(485), then the
pattern analysis process may end (486).
26
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[0092] However, if a pattern is identified (487), then the transport
facilitation system
100 can determine whether the preference corresponding to the pattern exceeds
a
certainty probability threshold (488). If not (489), then the transport
facilitation system
100 can log another instance for the detected pattern and end the process
(486).
However, if the preference corresponding to the pattern does exceed the
threshold
(490), then the transport facilitation system 100 can amend or edit the
rider's comfort
profile 137 to include the learned preference (495).
[0093] FIGS. 5A and 5B are flow charts describing additional example methods
of
configuring an AV for one or more users, according to examples described
herein.
Referring to FIG. 5A, the transport facilitation system 100 can receive a pick-
up request
197 from a user device 195 (500). The pick-up request can include a unique
identifier
136 (502) and a pick-up location (504). Utilizing the pick-up location, the
transport
facilitation system 100 can select a proximate AV 109 to service the pick-up
request
(507), and if accepted, transmit the pick-up location to the selected AV 109
to enable a
rendezvous between the AV 109 and the requesting user (509).
[0094] According to examples described herein, the transport facilitation
system 100
can receive accelerometer data 181 and location data (e.g., GPS data 183) from
the user
device 195 (505). In some aspects, a confirmation 199 can trigger the
transmission of
the accelerometer 181 and GPS data 183. In variations, the accelerometer data
181 and
GPS data 183 can be received at any time by the transport facilitation system
100 for
analysis. Thus, the transport facilitation system 100 can analyze the
accelerometer data
181 and the GPS data 183 to determine or estimate a number of user attributes
153
(510). For example, the data can include directional accelerometer peaks that
may
include force vectors (including a magnitude) that can indicate a gait
pattern, stride
length, and relative weight of the user. The transport facilitation system 100
can analyze
these data to calculate, for example, a height (511), a weight or body type
(512), a leg
length (513) (and/or femur length), and/or a relative posture (514) (e.g.,
upright versus
slouched) of the user.
[0095] Based on the determined or estimated user attributes 153, the transport
facilitation system 100 can query the database 130 for matching comfort
profiles 137 in
order to generate a seat configuration set (515). As described herein, the
transport
facilitation system 100 can identify a set of matching comfort profiles 137
for user's with
similar attributes, and can base the seat configuration set based on the
matching
comfort profiles. For example, the transport facilitation system 100 can
calculate and
utilize average(s) of the seat adjustment and positioning settings of the
matching
comfort profiles 137, and generate the seat configuration set for the
requesting user
based on the calculated averages. In one example, this seat configuration set
can be
stored as an initial configuration for the requesting user. The transport
facilitation
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system 100 can then overwrite the initial configuration when the requesting
user makes
manual adjustments thereafter, and as the transport facilitation system 100
builds a full
comfort profile 137 for the requesting user over time.
[0096] As further described herein, the seat configuration set can cause the
selected AV
109 to adjust various parameters of a seat (e.g., an assigned seat) for the
user. For
example, the seat configuration set can indicate certain a backrest angle, a
thigh
extension adjustment (e.g., cushion edge adjustment), a fore-and-aft position,
a
headrest angle, a headrest level, a lumbar position, a seat depth, a seat
height, an upper
seat tilt angle, and/or a shoulder support adjustment for the user's seat.
Accordingly,
the transport facilitation system 100 can transmit the seat configuration set
to the
selected AV 109 to adjust the user's seat prior to arriving at the pick-up
location (520).
[0097] In certain implementations, the transport facilitation system 100 can
determine a
specified seat within the AV 109 that is assigned to the user (525). In one
example, the
transport facilitation system 100 assigns the seat to the user based on
availability (e.g.,
for pooled rides). In other examples, a default seat may be assigned (e.g.,
the front left
seat) based on convention or rider preference. According to implementations
described
herein, the transport facilitation system 100 can transmit a seat confirmation
to the user
device 195 indicating the assigned and/or pre-configured seat (530).
[0098] Additionally, the transport facilitation system 100 can transmit a
route command
to the selected AV 109 indicating route information to pickup the user on a
curbside
corresponding to the assigned and/or pre-configured seat (535). For example,
the pick-
up location can include a street side, which the transport facilitation system
100 can
utilize to route the AV 109 and/or assign a particular seat to the user such
that the seat
side matches the street side in order to avoid having the user walk around to
the road
traffic side of the AV 109.
[0099] FIG. 5B is a flow chart describing another example of configuring an AV
for a user.
Referring to FIG. 5B, the transport facilitation system 100 can receive a pick-
up request
197 from a user device 195 (550), and select an AV 109 to service the pick-up
request
197 (555). In some examples, once selected, the transport facilitation system
100 can
receive or look up AV parameter data (e.g., in stored AV parameter logs 134)
indicating
the configurable components of that particular AV 109 (560). Examples
described herein
recognize that different AVs may be manufactured to include any number of
configurable interior components. For example, basic AVs may simply include an
interior
space with a seating arrangement and no configurable components. More
luxurious AVs
may include configurable components and systems related to seating
configuration
(586), seat temperature adjustment, seat positioning, and seating adjustments
(588),
climate control (e.g., for air temperature and localization) (590), visual
systems (e.g.,
28
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lighting and/or a display system including one or more displays) (592), audio
system
settings (e.g., radio channel, volume, balance, and fade adjustments) (594),
and other
components such as windows, sunroof, convertible settings, mirrors, and the
like (596).
[0100] According to certain implementations, an AV may include network and/or
computation features for riders. For example, the AV may include virtual
reality or
augmented reality features to facilitate work or provide, for example, task-
oriented
activities such as gameplay. In one example, transport facilitation system 100
can
provide services that enable access to certain features of the AV, such as
conferencing,
secure networking, content viewing, game playing, and the like. Furthermore,
such
features may be accessible via a user account managed by the backend transport
facilitation system 100. Such networked and/or computation services can be
preconfigured in the AV configuration set 188 transmitted to the selected AV
109, or
inputted by the requesting user via the preference menu 186 on the designated
application 185. Thus, the AV configuration set 188 can further include
configurations of
the networked/computation services available on the AV 109 (598).
[0101] In one or more examples, the transport facilitation system 100 can
generate a
preference menu 186 based on the actual configurable components and parameters
of
the actual AV 109 selected to service the pick-up request 197, and transmit
the
preference menu 186 to the user device 195 (565). The user can choose to
disregard the
menu 186, exit from the menu 186, or make various selections to personalize
the
selected AV 109 prior to being picked up. The transport facilitation system
100 can
receive the preference selections 191 from the user device 195 (570), and
optionally
access the user's comfort profile 137 to identify any additional preferences
133 (575).
Then, the transport facilitation system 100 can generate an AV configuration
set 188
indicating the set of configuration preferences to personalize the AV 109
(580). In some
aspects, the AV configuration set 188 can include a set of instructions
commanding a
control system of the AV 109 to automatically configure each of the components
according to the user preferences while en route to the pick-up location.
Thereafter, the
transport facilitation system 100 can transmit the AV configuration set 188 to
the
selected AV 109 (585).
[0102] FIG. 6 is a flow chart describing an example method of optimizing
timing for
configuring an AV for one or more users, according to examples described
herein. In the
below description of FIG. 6, reference may be made to reference characters
representing like features illustrated in FIGS. 1-3. Furthermore, the
processes described
in connection with FIG. 6 may be performed by an example AV 200 as shown and
described above with respect to FIG. 2. Referring to FIG. 6, the AV 200 can
receive a
transport invitation 213 to service a pick-up request 197 from a
transportation
facilitation system 280 (600). In some examples, the AV 200 may reject the
invitation
29
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213 if a certain conflict exists, such as a lack of fuel or power or a service
requirement. In
variations, the AV 200 can accept the invitation 213, which can comprise a
carpooling
request or a single use request (605). For carpooling examples, the AV 200 can
transport
additional passengers and can be routed to several locations to pick-up and
drop off
passengers on a dynamically calculated route (e.g., by route planner 222).
[0103] Prior to or after accepting the invitation 213, the AV 200 can receive
the pick-up
location (610), and autonomously drive to the pick-up location accordingly
(615). Prior
to initiating travel, or while en route to the pick-up location, the AV 200
can receive an
AV configuration set 218 for the user from the transport facilitation system
280 (620).
As described herein, the AV configuration set 218 can include adjustment
parameters
for various configurable components or interior systems of the AV 200. In
various
implementations, the AV 200 can determine an optimal timing schedule to
implement
each configuration command for each component (625). In doing so, the AV 200
can
calculate adjustment timing for some or all of the components (630). For some
components, the AV 200 can determine that immediate execution is optimal
(632). For
example, configuration and adjustments to the seats can be performed at any
time prior
to arriving at the pick-up location. For other components, the AV 200 may
determine
that optimized or timed execution of the adjustments or configurations may be
more
optimal for energy and/or practical reasons (634).
[0104] For example, the climate control system of the AV 200 can be energy-
intensive,
especially for extreme temperature differentials between the exterior and
interior of
the AV 200. Thus, constant optimization of the climate control system may be
desirable
over the course of many trips in order to optimize energy use. Furthermore, an
empty
AV 200 traveling with outputted audio or display content is impractical, and
thus
optimal timing for such systems may indicate initiating the display and audio
systems
just prior to arriving at the pick-up location. In many aspects, the AV 200
can determine
a distance or an estimated time to travel to the pick-up location based on
distance
and/or traffic conditions (635), and execute the configuration commands
according to
the calculated timing schedule (640) while en route to the pick-up location.
Accordingly,
when the AV 200 arrives to pick-up the user (645), all of the configurations
can be
executed in accordance with the AV configuration set 218.
[0105] While the AV 200 autonomously drives to the drop-off destination, the
AV 200
can monitor the user's position within the AV 200 (650). In one aspect, the AV
200 can
monitor the user's position using seat sensors (652). Additionally or
alternatively, the
AV 200 can monitor the user's position using one or more interior cameras
(654).
Accordingly, during the trip, the user may shift positions or change seats
within the
passenger interior. As the user moves, the AV 200 can dynamically adjust an
audio focal
point (e.g., the balance and fade of the audio system) based on the user's
location (655).
CA 3040081 2019-04-11
Additionally or alternatively, the AV 200 can dynamically adjust the climate
control (e.g.,
the localized temperature) for the user based on the user's location within
the AV 200
(660). Additionally or alternatively still, the AV 200 can dynamically adjust
seat
temperature settings based on the user's location within the AV 200 (665).
When the AV
200 arrives at the destination (e.g., a drop-off location) (670), the process
can repeat
with another user, or continue for carpool implementations, as denoted by
reference
circle "C."
[0106] HARDWARE DIAGRAMS
[0107] FIG. 7 is a block diagram that illustrates a computer system upon which
examples
described herein may be implemented. A computer system 700 can be implemented
on,
for example, a server or combination of servers. For example, the computer
system 700
may be implemented as part of a network service for providing transportation
services.
In the context of FIG. 1, the transport facilitation system 100 may be
implemented using
a computer system 700 such as described by FIG. 7. The transport facilitation
system
100 may also be implemented using a combination of multiple computer systems
as
described in connection with FIG. 7.
[0108] In one implementation, the computer system 700 includes processing
resources
710, a main memory 720, a read-only memory (ROM) 730, a storage device 740,
and a
communication interface 750. The computer system 700 includes at least one
processor
710 for processing information stored in the main memory 720, such as provided
by a
random access memory (RAM) or other dynamic storage device, for storing
information
and instructions which are executable by the processor 710. The main memory
720 also
may be used for storing temporary variables or other intermediate information
during
execution of instructions to be executed by the processor 710. The computer
system
700 may also include the ROM 730 or other static storage device for storing
static
information and instructions for the processor 710. A storage device 740, such
as a
magnetic disk or optical disk, is provided for storing information and
instructions.
[0109] The communication interface 750 enables the computer system 700 to
communicate with one or more networks 780 (e.g. cellular network) through use
of the
network link (wireless or wired). Using the network link, the computer system
700 can
communicate with one or more computing devices, one or more servers, and/or
one or
more AVs. In accordance with examples, the computer system 700 receives pick-
up
requests 784 from mobile computing devices of individual users. The executable
instructions stored in the memory 730 can include configuration instructions
722, which
the processor 710 executes to determine user configuration preferences and
generate
AV configuration sets 754, as described above. The executable instructions
stored in the
memory 720 can also include pattern recognition instructions 724, which enable
the
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computer system 700 to identify patterns in current and historical AV data 782
that may
be correlated to a learned preference. By way of example, the instructions and
data
stored in the memory 720 can be executed by the processor 710 to implement an
example transport facilitation system 100 of FIG. 1. In performing the
operations, the
processor 710 can receive pick-u p requests 784, generate and transmit
invitations 752
to AVs to service the pick-u p requests 784, receive AV data 782 to learn
preferences,
and transmit AV configuration sets 754 via the communication interface 750.
[0110] The processor 710 is configured with software and/or other logic to
perform one
or more processes, steps and other functions described with implementations,
such as
described by FIGS. 1 through 6, and elsewhere in the present application.
[0111] Examples described herein are related to the use of the computer system
700 for
implementing the techniques described herein. According to one example, those
techniques are performed by the computer system 700 in response to the
processor 710
executing one or more sequences of one or more instructions contained in the
main
memory 720. Such instructions may be read into the main memory 720 from
another
machine-readable medium, such as the storage device 740. Execution of the
sequences
of instructions contained in the main memory 720 causes the processor 710 to
perform
the process steps described herein. In alternative implementations, hard-wired
circuitry
may be used in place of or in combination with software instructions to
implement
examples described herein. Thus, the examples described are not limited to any
specific
combination of hardware circuitry and software.
[0112] FIG. 8 is a block diagram that illustrates a mobile computing device
upon which
examples described herein may be implemented. In one example, a mobile
computing
device 800 may correspond to, for example, a cellular communication device
(e.g.,
feature phone, smartphone etc.) that is capable of telephony, messaging,
and/or data
services. In variations, the mobile computing device 800 can correspond to,
for example,
a tablet or wearable computing device. Still further, the mobile computing
device 800
can be distributed amongst multiple portable devices of drivers, and
requesting users.
[0113] In an example of FIG. 8, the computing device 800 includes a processor
810,
memory resources 820, a display device 830 (e.g., such as a touch-sensitive
display
device), one or more communication sub-systems 840 (including wireless
communication sub-systems), input mechanisms 850 (e.g., an input mechanism can
include or be part of the touch-sensitive display device), and one or more
location
detection mechanisms (e.g., GPS component) 860. In one example, at least one
of the
communication subsystems 840 sends and receives cellular data over data
channels and
voice channels.
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[0114] A requesting user of the network service can operate the mobile
computing
device 800 to transmit a pick-up request including a pick-up location. The
memory
resources 820 can store a designated user application 807 to link the
requesting user
with the network service to facilitate a pickup. Execution of the user
application 807 by
the processor 810 can cause a user GUI 837 to be generated on the display 830.
User
interaction with the user GUI 837 can enable the user to transmit a pick-up
request in
connection with the network service, which enables an AV to accept an
invitation to
service the pick-up request.
[0115] FIG. 9 is a block diagram illustrating a computer system upon which
example AV
processing systems described herein may be implemented. The computer system
900
can be implemented using one or more processors 904, and one or more memory
resources 906. In the context of FIG. 2, the control system 220 can
implemented using
one or more components of the computer system 900 shown in FIG. 9.
[0116] According to some examples, the computer system 900 may be implemented
within an autonomous vehicle with software and hardware resources such as
described
with examples of FIG. 2. In an example shown, the computer system 900 can be
distributed spatially into various regions of the autonomous vehicle, with
various
aspects integrated with other components of the autonomous vehicle itself. For
example, the processors 904 and/or memory resources 906 can be provided in the
trunk
of the autonomous vehicle. The various processing resources 904 of the
computer
system 900 can also execute personalization/optimization instructions 912
using
microprocessors or integrated circuits. In some examples, the
personalization/optimization instructions 912 can be executed by the
processing
resources 904 or using field-programmable gate arrays (FPGAs).
[0117] In an example of FIG. 9, the computer system 900 can include a local
communication interface 950 (or series of local links) to vehicle interfaces
and other
resources of the autonomous vehicle (e.g. the computer stack drives). In one
implementation, the communication interface 950 provides a data bus or other
local
links to electro-mechanical interfaces of the vehicle, such as wireless or
wired links to
and from the AV control system 220, and can provide a network link to a
transport
facilitation system over one or more networks 960.
[0118] The memory resources 906 can include, for example, main memory, a read-
only
memory (ROM), storage device, and cache resources. The main memory of memory
resources 906 can include random access memory (RAM) or other dynamic storage
device, for storing information and instructions which are executable by the
processors
904. The processors 904 can execute instructions for processing information
stored with
the main memory of the memory resources 906. The main memory 906 can also
store
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temporary variables or other intermediate information which can be used during
execution of instructions by one or more of the processors 904. The memory
resources
906 can also include ROM or other static storage device for storing static
information
and instructions for one or more of the processors 904. The memory resources
906 can
also include other forms of memory devices and components, such as a magnetic
disk or
optical disk, for purpose of storing information and instructions for use by
one or more
of the processors 904.
[0119] According to some examples, the memory 906 may store a plurality of
software
instructions including, for example, personalization/optimization instructions
912. The
personalization/ optimization instructions 912 may be executed by one or more
of the
processors 904 in order to implement functionality such as described with
respect to
FIGS. 2 and 6.
[0120] In certain examples, the computer system 900 can receive AV
configuration sets
962 via the communication interface 950 and network 960 from a transport
facilitation
system. In executing the personalization/ optimization instructions 912, the
processing
resources 904 can generate and execute configuration commands 918 to adjust
and
configure the various configurable components 920 of the AV. Furthermore, the
processing resources 904 can transmit AV data 952, as described herein, to the
transport facilitation system over the network 960.
[0121] While examples of FIGS. 7 through 9 provide for computing systems for
implementing aspects described, some or all of the functionality described
with respect
to one computing system of FIGS. 7 through 9 may be performed by one or more
other
computing systems described with respect to FIGS. 7 through 9.
[0122] It is contemplated for examples described herein to extend to
individual
elements and concepts described herein, independently of other concepts, ideas
or
systems, as well as for examples to include combinations of elements recited
anywhere
in this application. Although examples are described in detail herein with
reference to
the accompanying drawings, it is to be understood that the concepts are not
limited to
those precise examples. As such, many modifications and variations will be
apparent to
practitioners skilled in this art. Accordingly, it is intended that the scope
of the concepts
be defined by the following claims and their equivalents. Furthermore, it is
contemplated that a particular feature described either individually or as
part of an
example can be combined with other individually described features, or parts
of other
examples, even if the other features and examples make no mentioned of the
particular
feature. Thus, the absence of describing combinations should not preclude
claiming
rights to such combinations.
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