TOMMANDATI U NI MIMI KUTOA MAONI
`
`US009884631B2
`
`( 12 ) United States Patent
`James et al .
`
`( 10 ) Patent No . :
`( 45 ) Date of Patent :
`
`US 9 , 884 , 631 B2
`Feb . 6 , 2018
`
`( 54 ) TRANSITIONING BETWEEN OPERATIONAL
`MODES OF AN AUTONOMOUS VEHICLE
`Applicants : Toyota Motor Engineering &
`Manufacturing North America , Inc . ,
`Erlanger , KY ( US ) ; Toyota Jidosha
`Kabushiki Kaisha , Toyota - shi ( JP )
`( 72 ) Inventors : Michael R . James , Northville , MI
`( US ) ; Katsuhiro Sakai , Ann Arbor , MI
`( US ) ; Toshiki Kindo , Yokohama ( JP ) ;
`Danil V . Prokhorov , Canton , MI ( US ) ;
`Masahiro Harada , Novi , MI ( US )
`( 73 ) Assignees : Toyota Motor Engineering &
`Manufacturing North America , Inc . ,
`Erlanger , KY ( US ) ; Toyota Jidosha
`Kabushiki Kaisha , Toyota - shi ,
`Aichi - ken ( JP )
`Subject to any disclaimer , the term of this
`patent is extended or adjusted under 35
`U . S . C . 154 ( b ) by 40 days .
`( 21 ) Appl . No . : 14 / 730 , 570
`( 22 ) Filed :
`Jun . 4 , 2015
`Prior Publication Data
`( 65 )
`US 2016 / 0355192 A1 Dec . 8 , 2016
`Int . Ci .
`( 2012 . 01 )
`B60W 50 / 08
`B60W 50 / 14
`( 2012 . 01 )
`( Continued )
`( 52 ) U . S . CI .
`8
`CPC . . . . . . . . . . . B60W 50 / 082 ( 2013 . 01 ) ; B60R 11 / 04
`( 2013 . 01 ) ; B60W 40 / 08 ( 2013 . 01 ) ; B60W
`50 / 14 ( 2013 . 01 ) ;
`( Continued )
`( 58 ) Field of Classification Search
`CPC . . . . . . . . . GO5D 1 / 00 ; G05D 1 / 02 ; GO5D 1 / 0088 ;
`GO5D 1 / 0289 ; GO5D 1 / 0259 ;
`( Continued )
`
`( * ) Notice :
`
`( 51 )
`
`( 71 )
`
`( 56 )
`
`B60Q 1 / 46
`340 / 471
`HO4N 7 / 183
`348 / E7 . 086
`
`References Cited
`U . S . PATENT DOCUMENTS
`4 , 346 , 365 A *
`8 / 1982 Ingram . . . . . . . . . . . . .
`5 , 877 , 897 A *
`3 / 1999 Schofield . . . . . . . . . .
`( Continued )
`OTHER PUBLICATIONS
`National Highway Traffic Safety Administration ; “ U . S . Department
`of Transportation Releases Policy on Automated Vehicle Develop
`ment ” ; May 30 , 2013 ; [ retrieved Jun . 3 , 2015 ) ; retrieved from the
`Internet :
`< http : / / www . nhtsa . gov / About + NHTSA / Press + Releases /
`U . S . + Department + of + Transportation + Releases + Policy + on +
`Automated + Vehicle + Development > ( 2 pages ) .
`National Highway Traffic Safety Administration ; “ National High
`way Traffic Safety Administration Preliminary Statement of Policy
`Concerning Automated Vehicles ” ; 2013 ; ( 14 pages ) .
`( Continued )
`Primary Examiner – Yonel Beaulieu
`Assistant Examiner — Martin Weeks
`( 74 ) Attorney , Agent , or Firm — Christopher G . Darrow ;
`Darrow Mustafa PC
`( 57 )
`ABSTRACT
`Arrangements relating to the transitioning of a vehicle
`between operational modes are described . The vehicle can
`transition between a first operational mode and a second
`operational mode . The second operational mode has a
`greater degree of manual involvement than the first opera
`tional mode . For instance , the first operational mode can be
`an unmonitored autonomous operational mode , and the
`second operational mode can be a monitored autonomous
`operational mode or a manual operational mode . It can be
`determined whether an operational mode transition event
`has occurred while the vehicle is operating in the first
`operational mode . In response to determining that an opera
`tional mode transition event has occurred , a time buffer for
`continuing in the first operational mode before switching to
`the second operational mode can be determined . A transition
`alert can be presented within the vehicle . The transition alert
`can represent the determined time buffer .
`23 Claims , 3 Drawing Sheets
`
`103
`
`101
`
`115
`
`Data Stone ( 3 )
`111
`Map Data
`Friving Scenes112
`Scene Markens
`113
`
`Sensor System
`Environnent
`Sensor ( s )
`
`125
`
`1100
`
`Output
`System
`
`Actuator ( s )
`Vehicle Systeme
`Propulsion System
`
`Braking System
`
`Stoering System
`
`Throttle System
`
`Transmission System
`
`Signaling System
`
`Navigation System
`
`145
`
`- 150
`155
`161
`
`165
`
`- 180
`
`Processor ( s )
`
`Autonomous
`Driving
`Module ( s )
`
`- - 130
`
`Imut
`System
`
`Transition Event
`Detection
`Madule
`
`137
`
`2012
`
`- 191
`
`Time Buller
`Deterrnination
`Module
`
`Display ( 3 )
`
`Speaker ( s )
`
`Haptic
`Actuators )
`
`102 _
`
`104
`
`IPR2025-00943
`Tesla EX1021 Page 1
`
`

`

`US 9 , 884 , 631 B2
`Page 2
`
`( 52 )
`
`( 56 )
`
`( 2006 . 01 )
`( 2012 . 01 )
`( 2012 . 01 )
`
`( 51 ) Int . CI .
`BOOR 11 / 04
`B60W 50 / 16
`B60W 40 / 08
`U . S . CI .
`CPC . . . . . B60W 50 / 16 ( 2013 . 01 ) ; B60W 2040 / 0818
`( 2013 . 01 ) ; B60W 2050 / 143 ( 2013 . 01 ) ; B60W
`2050 / 146 ( 2013 . 01 ) ; B60W 2420 / 42 ( 2013 . 01 ) ;
`B60W 2540 / 04 ( 2013 . 01 ) ; B60W 2550 / 402
`( 2013 . 01 )
`( 58 ) Field of Classification Search
`CPC . . . . GO5D 1 / 0246 ; GO5D 1 / 088 ; G05D 1 / 0061 ;
`B60W 50 / 08 ; B60W 50 / 14 ; B60W 50 / 16 ;
`B60W 40 / 08 ; B60W 2050 / 146 ; B6OR
`11 / 04
`See application file for complete search history .
`References Cited
`U . S . PATENT DOCUMENTS
`8 , 618 , 922 B2 12 / 2013 Debouk et al .
`2013 / 0131907 A1 5 / 2013 Green et al .
`2014 / 0244096 AL
`8 / 2014 An et al .
`2014 / 0303827 Al 10 / 2014 Dolgov et al .
`2016 / 0357186 A1 *
`12 / 2016 Dias . . . . . .
`
`. . . GO5D 1 / 0061
`
`OTHER PUBLICATIONS
`Kamata ; “ Background Map Format for Autonomous Driving " ; U . S .
`Appl . No . 14 / 574 , 151 , filed Dec . 17 , 2014 .
`* cited by examiner
`
`IPR2025-00943
`Tesla EX1021 Page 2
`
`

`

`U . S . Patent
`
`Feb . 6 , 2018
`
`Sheet 1 of 3
`
`US 9 , 884 , 631 B2
`
`FIG . 1
`FIG . 1
`
`103 _ ro
`
`103
`
`101
`
`- 115
`
`Data Store ( s )
`Map Data
`Driving Scenes
`Scene Markers
`
`Sensor System
`Environment
`Sensoris
`
`Processor ( s )
`
`Autonomous
`Driving
`Module ( s )
`
`- 130
`
`\ 125
`- 126
`
`131
`
`100
`
`106
`
`145
`
`T
`
`Ž
`
`1052
`
`Input
`System
`
`Transition Event
`Detection
`Module
`
`Time Buffer
`Determination
`Module
`
`- 300
`
`Display ( s )
`
`Speaker ( s )
`
`Haptic
`Actuator ( s )
`
`Output
`System
`
`Actuator ( s ) 7140
`
`Vehicle Systems
`Propulsion System
`
`Braking System
`
`Steering System
`
`Throttle System
`
`Transmission System
`
`Signaling System
`
`Navigation System
`
`180
`
`102 _
`
`104
`
`IPR2025-00943
`Tesla EX1021 Page 3
`
`

`

`U . S . Patent
`
`Feb . 6 , 2018
`
`Sheet 2 of 3
`
`US 9 , 884 , 631 B2
`
`200
`
`Determining whether an operational mode transition
`event has occurred while a vehicle is operating
`in a first operational mode
`
`Responsive to determining that an operational mode
`transition event has occurred , determining a time
`buffer for continuing in the first operational mode
`before switching to a second operational mode
`
`210
`
`220
`
`Presenting a transition alert within the vehicle , the
`transition alert corresponding to the determined
`time buffer
`
`h
`
`230
`
`FIG . 2
`
`IPR2025-00943
`Tesla EX1021 Page 4
`
`

`

`U . S . Patent
`
`Feb . 6 , 2018
`
`Sheet 3 of 3
`
`US 9 , 884 , 631 B2
`
`310
`
`30
`
`- 300
`
`? ????
`
`FIG . 3
`
`????
`???? ( Time Left , ?
`00 : 10 ????
`
`315
`
`305
`
`- 300
`
`Minutes : Seconds
`
`FIG . 4
`
`IPR2025-00943
`Tesla EX1021 Page 5
`
`

`

`US 9 , 884 , 631 B2
`
`TRANSITIONING BETWEEN OPERATIONAL
`MODES OF AN AUTONOMOUS VEHICLE
`
`DETAILED DESCRIPTION
`
`FIG . 2 is an example of a method of transitioning a
`vehicle between a first operational mode and a second
`operational mode , wherein the second operational mode has
`a greater degree of manual involvement than the first opera
`5 tional mode .
`FIG . 3 is an example of a transition alert presented on a
`display , wherein the transition alert is a bar graph .
`FIG . 4 is another example of a transition alert presented
`on a display , wherein the transition alert is a countdown
`10 clock .
`
`FIELD
`The subject matter described herein relates in general to
`vehicles that have a plurality of operational modes including
`an autonomous operational mode and , more particularly , to
`the transitioning between different operational modes .
`BACKGROUND
`Some vehicles are configured to operate in a plurality of
`operational modes . An example of an operational mode is
`This detailed description relates to the transitioning of an
`one in which a computing system is used to navigate and / or
`maneuver the vehicle along a travel route with minimal or no 15 autonomous vehicle from
`a
`first operational mode to
`a
`second operational mode . This detailed description is more
`input from a human driver . Such vehicles are equipped with
`sensors that are configured to detect information about the
`particularly related to instances in which the second opera
`tional mode has a greater degree of manual involvement
`surrounding environment , including the presence of objects
`in the environment . The detected information can be sent to
`than the first operational mode . In response to determining
`the computing system . Other operational modes can include 20 that an operational mode transition event has occurred , a
`luding a manual mode in time buffer for continuing in the first operational mode
`different levels of human input , including a manual mode in
`which a human driver is responsible for navigating and / or
`before switching to the second operational mode can be
`determined . A transition alert corresponding to the deter
`maneuvering the vehicle through the surrounding environ
`ment . Vehicles with a plurality of operational modes are
`mined time buffer can be presented . The present detailed
`configured to allow switching between the various opera - 25 description relates to systems , methods and computer pro
`gram products that incorporate such features . In at least
`tional modes .
`some instances , such systems , methods and computer pro
`gram products can improve performance and / or safety of an
`SUMMARY
`autonomous vehicle .
`Detailed embodiments are disclosed herein ; however , it is
`In one respect , the present disclosure is directed to a 30
`to be understood that the disclosed embodiments are
`method of transitioning a vehicle between a first operational
`intended only as exemplary . Therefore , specific structural
`mode and a second operational mode . The second opera -
`and functional details disclosed herein are not to be inter
`tional mode has a greater degree of manual involvement
`preted as limiting , but merely as a basis for the claims and
`than the first operational mode . The method can include
`determining whether an operational mode transition event 35 as a representative basis for teaching one skilled in the art to
`has occurred while the vehicle is operating in the first
`variously employ the aspects herein in virtually any appro
`operational mode . The method can also include , responsive
`priately detailed structure . Further , the terms and phrases
`to determining that an operational mode transition event has
`used herein are not intended to be limiting but rather to
`occurred , determining a time buffer for continuing in the first
`provide an understandable description of possible imple
`operational mode before switching to the second operational 40 mentations . Various embodiments are shown in FIGS . 1 - 4 ,
`mode . The method can further include presenting a transi -
`but the embodiments are not limited to the illustrated
`tion alert within the vehicle . The transition alert can corre -
`structure or application .
`It will be appreciated that for simplicity and clarity of
`spond to the determined time buffer .
`In another respect , the present disclosure is directed to a
`illustration , where appropriate , reference numerals have
`system for transitioning a vehicle between a first operational 45 been repeated among the different figures to indicate corre
`mode and a second operational mode . The second opera -
`sponding or analogous elements . In addition , numerous
`tional mode has a greater degree of manual involvement
`specific details are set forth in order to provide a thorough
`than the first operational mode . The system can include a
`understanding of the embodiments described herein . How
`user interface located within the vehicle . The system can
`ever , it will be understood by those of ordinary skill in the
`also include a processor operatively connected to the sensor 50 art that the embodiments described herein can be practiced
`system . The processor can be programmed to initiate execut
`without these specific details .
`able operations . The executable operations can include
`Referring to FIG . 1 , an example a vehicle 100 is shown .
`determining whether an operational mode transition event
`As used herein , " vehicle ” means any form of motorized
`has occurred while the vehicle is operating in the first
`transport . In one or more implementations , the vehicle 100
`operational mode . The executable operations can also 55 can be an automobile . While arrangements will be described
`include , responsive to determining that an operational mode
`herein with respect to automobiles , it will be understood that
`transition event has occurred , determining a time buffer for
`embodiments are not limited to automobiles . In some imple
`continuing in the first operational mode before switching to
`mentations , the vehicle 100 may be a watercraft , an aircraft
`the second operational mode . The executable operations can
`or any other form of motorized transport .
`further include presenting a transition alert within the 60
`According to arrangements herein , the vehicle 100 can be
`vehicle , the transition alert corresponding to the determined
`an autonomous vehicle . As used herein , “ autonomous
`vehicle ” means a vehicle that configured to operate in an
`time buffer .
`autonomous mode . “ Autonomous mode ” means that one or
`BRIEF DESCRIPTION OF THE DRAWINGS
`more computing systems are used to navigate and / or maneu
`65 ver the vehicle along a travel route with minimal or no input
`FIG . 1 is an example of an autonomous vehicle configured
`from a human driver . In one or more arrangements , the
`to transition between different operational modes .
`vehicle 100 can be highly automated .
`
`IPR2025-00943
`Tesla EX1021 Page 6
`
`

`

`US 9 , 884 , 631 B2
`The vehicle 100 can have a plurality of operational
`vehicle 100 to a stop , keeping the vehicle 100 stopped , or
`modes . For instance , the vehicle 100 can have an unmoni -
`having the vehicle 100 take the next exit on a highway , just
`tored autonomous operational mode . “ Unmonitored autono -
`to name a few possibilities .
`mous operational mode ” means that one or more computing
`The vehicle 100 can be configured to be switched between
`systems are used to navigate and / or maneuver the vehicle 5 the various operational modes . Such switching can be imple
`mented in any suitable manner , now known or later devel
`along a travel route with no input or supervision required
`oped . The switching can be performed automatically , or it
`from a human driver . As an example , the unmonitored
`can be done responsive to receiving a manual input or
`autonomous operational mode can include Level 4 ( L4 ) , as
`request . It will be appreciated that the switching between the
`defined by the National Highway Traffic Safety Adminis
`its Preliminary Statement of Policy Concerning 2010 operational modes can raise concerns about reliable transfer
`tration in
`of operational authority .
`Automated Vehicles ( May 30 , 2013 ) ( “ NHTSA 2013
`In one or more arrangements , the switching can be done
`Policy ” ) , which is incorporated herein by reference . The
`from a first operational mode to a second operational mode .
`vehicle 100 can have a monitored autonomous operational
`In some instances , the second operational mode can have a
`mode . “ Monitored autonomous operational mode ” means
`means 15 greater degree of manual involvement than the first opera
`that one or more computing systems are used to navigate
`tional mode . A " greater degree of manual involvement ”
`and / or maneuver the vehicle with at least some human driver
`means that a human driver is required to or should increase
`his or her level of supervision and / or input with respect to
`supervision required . As an example , the monitored autono -
`mous operational mode can include Level 3 or L3 , as defined
`the control of at least the navigation and / or maneuvering of
`by the NHTSA 2013 Policy . In some instances , when the 20 the vehicle . One example of when the second operational
`vehicle 100 is in a monitored autonomous operational mode ,
`mode can have a greater degree of manual involvement than
`a signal ( e . g . , an audial signal , a visual signal , a haptic
`the first operational mode is when the first operational mode
`signal , etc . ) can be presented to a human driver to take an
`is an unmonitored autonomous operational mode and the
`action within a predetermined amount of time . If such action
`second operational mode is a monitored autonomous opera
`is not taken within the predetermined amount of time , one or 25 tional mode or a manual operational mode . Another example
`is when the first operational mode is a monitored operational
`more safety maneuvers can be implemented
`mode and the second operational mode is a semi - autono
`The vehicle 100 can have one or more semi - autonomous
`mous operational mode or a manual operational mode .
`operational modes . “ Semi - autonomous operational mode "
`In some instances , the second operational mode can have
`means that a portion of the navigation and / or maneuvering
`30 a lesser degree of manual involvement than the first opera
`of the vehicle along a travel route is performed by one or
`tional mode . A " lesser degree of manual involvement "
`more computing systems , and a portion of the navigation
`means that a human driver can decrease his or her level of
`and / or maneuvering of the vehicle along a travel route is
`supervision and / or input with respect to the control of at
`performed by a human driver . As an example , the semi
`least the navigation and / or maneuvering of the vehicle . One
`autonomous operational mode can include Levels 2 ( L2 ) 22 ) 35 example of when the second operational mode can have a
`and / or Level 1 ( L1 ) , as defined by the NHTSA 2013 Policy .
`lesser degree of manual involvement than the first opera
`One example of a semi - autonomous operational mode is
`tional mode is when the first operational mode is a manual
`when an adaptive cruise control system is activated . In such
`operational mode , and the second operational mode is
`a
`case , the speed of a vehicle can be automatically adjusted to
`semi - autonomous operational mode , a monitored autono
`maintain a safe distance from a vehicle ahead based on data 40 mous operational mode , or an unmonitored autonomous
`received from on - board sensors , but the vehicle is otherwise
`operational mode . Another example is when the first opera
`operated manually by a human driver . Upon receiving a
`tional mode is a monitored operational mode , and the second
`driver input to alter the speed of the vehicle ( e . g . by
`operational mode is an unmonitored operational mode .
`depressing the brake pedal to reduce the speed of the
`The vehicle 100 can have a forward end 101 and a
`vehicle ) , the adaptive cruise control system is deactivated 45 rearward end 102 . The vehicle 100 can have an associated
`longitudinal axis 103 , which can be the central axis of the
`and the speed of the vehicle is reduced .
`The vehicle 100 can have a manual operational mode .
`vehicle 100 . The vehicle 100 can have an associated longi
`“ Manual operational mode ” means that a substantial major -
`tudinal direction 104 . “ Longitudinal direction " means any
`ity or all of the navigation and / or maneuvering of the vehicle
`direction that is substantially parallel to and / or co - linear
`along a travel route is performed by a human driver with 50 with the longitudinal axis 103 . The vehicle 100 can have an
`minimal or no input from a computing system . As an
`associated lateral axis 105 , which can be substantially per
`example , the manual operational mode can include Level (
`pendicular to the longitudinal axis 103 . As used herein , the
`( LO ) , as defined by the NHTSA 2013 Policy .
`term “ substantially ” includes exactly the term
`it modifies
`The vehicle 100 can have a special operational mode .
`and slight variations therefrom . Thus , the term “ substantially
`“ Special operational mode ” means that , if a requested 55 perpendicular ” means exactly perpendicular and slight
`human driver action is not taken or confirmed within a
`variations therefrom . In this particular example , slight varia
`predetermined amount of time , the navigation and / or
`tions therefrom can include within normal manufacturing
`maneuvering of the vehicle can be controlled by one or more
`tolerances , within about 10 degrees or less , within about 5
`computing systems to implement one or more safety maneu -
`degrees or less , within about 4 degrees or less , within about
`vers . The safety maneuver can be a predetermined safety 60 3 degrees or less , within about 2 degrees or less , or within
`maneuver based on the current driving environment . For
`about 1 degree or less . The vehicle 100 can have an
`instance , if a human driver does not take control of the
`associated lateral direction 106 . “ Lateral direction ” means
`vehicle 100 within a predetermined amount of time , the
`any direction that is substantially parallel to and / or co - linear
`safety maneuver may include moving the vehicle 100 to the
`with the lateral axis 105 .
`side of the road , moving the vehicle 100 onto the shoulder 65
`The vehicle 100 can include various elements , some of
`of the road , reducing the speed of the vehicle 100 , turning
`which may be a part of an autonomous driving system . Some
`the vehicle 100 into the nearest parking lot , bringing the
`of the possible elements of the vehicle 100 are shown in FIG .
`
`IPR2025-00943
`Tesla EX1021 Page 7
`
`

`

`US 9 , 884 , 631 B2
`
`5
`
`driving scenes ” is defined as one or more driving scenes .
`1 and will now be described . It will be understood that it is
`“ Driving scenes ” means sensor system data of a location
`not necessary for the vehicle 100 to have all of the elements
`within a geographic area . As an example , the driving scenes
`shown in FIG . 1 or described herein . The vehicle 100 can
`can be images or videos . The driving scenes can include any
`have any combination of the various elements shown in FIG .
`suitable sensor system data of a road , an intersection ,
`1 . Further , the vehicle 100 can have additional elements to
`buildings , structures , traffic control devices , lane markers ,
`those shown in FIG . 1 . In some arrangements , vehicle 100
`landmarks , features . In some instances , the set of driving
`may not include one or more of the elements shown in FIG .
`scenes 112 can be located onboard the vehicle 100 . Alter
`1 . Further , while the various elements are shown as being
`natively , at least a portion of the set of driving scenes 112
`located within the vehicle 100 in FIG . 1 , it will be under
`stood that one or more of these elements can be located 10 can be located remote from the vehicle 100 .
`external to the vehicle 100 . Further , the elements shown may
`In one or more arrangements , the one or more data stores
`be physically separated by large distances .
`115 can include a set of scene markers 113 . The term “ set of
`The vehicle 100 can include one or more processors 110 .
`scene markers ” is defined as one or more scene markers . A
`“ Processor ” means any component or group of components
`" scene marker ” is an object or feature of interest located in
`that are configured to execute any of the processes described 15 and / or describing a driving scene . Examples of scene mark
`herein or any form of instructions to carry out such processes
`ers can include any suitable sensor system data of a road , an
`or cause such processes to be performed . The processor 110
`intersection , buildings , structures , traffic control devices ,
`may be implemented with one or more general - purpose
`lane markers , landmarks , road paint , signs , poles , curbs ,
`and / or one or more special - purpose processors . Examples of
`features . In some instances , the set of scene markers 113 can
`suitable processors include microprocessors , microcon - 20 be located onboard the vehicle 100 . Alternatively , at least a
`trollers , DSP processors , and other circuitry that can execute
`portion of the set of scene markers 113 can be located remote
`software . Further examples of suitable processors include ,
`from the vehicle 100 .
`but are not limited to , a central processing unit ( CPU ) , an
`In one or more arrangements , a priority can be established
`array processor , a vector processor , a digital signal processor
`with respect to the set of scene markers 113 . For instance ,
`( DSP ) , a field - programmable gate array ( FPGA ) , a program - 25 the priority can include sequentially ranking the set of scene
`mable logic array ( PLA ) , an application specific integrated
`markers 113 . Alternatively , the priority can include assign
`circuit ( ASIC ) , programmable logic circuitry , and a control -
`ing a priority level to the set of scene markers 113 . For
`ler . The processor 110 can include at least one hardware
`instance , the priority levels can include high , medium ,
`circuit ( e . g . , an integrated circuit ) configured to carry out
`and / or low . The ranking can be configured by a user or some
`instructions contained in program code . In arrangements in 30 other entity .
`which there is a plurality of processors 110 , such processors
`The vehicle 100 can include an autonomous driving
`can work independently from each other or one or more
`module 120 . The autonomous driving module 120 can be
`processors can work in combination with each other . In one
`implemented as computer readable program code that , when
`or more arrangements , the processor 110 can be a main
`executed by a processor , implement various processes
`processor of the vehicle 100 . For instance , the processor 110 35 described herein , including , for example , determining a
`can be an engine control unit ( ECU ) .
`travel route , implementing the determined travel route ,
`The vehicle 100 can include one or more data stores 115
`determining a modification to a current driving maneuver of
`for storing one or more types of data . The data store 115 can
`the vehicle 100 and / or causing , directly or indirectly , a
`include volatile and / or non - volatile memory . Examples of
`current driving maneuver of the vehicle 100 to be modified .
`suitable data stores 115 include RAM ( Random Access 40 The autonomous driving module 120 can be a component of
`Memory ) , flash memory , ROM ( Read Only Memory ) ,
`the processor 110 , or the autonomous driving module 120
`PROM ( Programmable Read - Only Memory ) , EPROM
`can be executed on and / or distributed among other process
`( Erasable Programmable Read - Only Memory ) , EEPROM
`ing systems to which the processor 110 is operatively
`( Electrically Erasable Programmable Read - Only Memory ) ,
`connected .
`registers , magnetic disks , optical disks , hard drives , or any 45
`The autonomous driving module 120 can include instruc
`other suitable storage medium , or any combination thereof .
`tions ( e . g . , program logic ) executable by the processor 110 .
`The data store 115 can be a component of the processor 110 ,
`Such instructions can include instructions to execute various
`or the data store 115 can be operatively connected to the
`vehicle functions and / or to transmit data to , receive data
`processor 110 for use thereby . The term “ operatively con -
`from , interact with , and / or control the vehicle 100 or one or
`nected , " as used throughout this description , can include 50 more systems thereof ( e . g . one or more of vehicle systems
`direct or indirect connections , including connections without
`145 ) . Alternatively or in addition , the data store 115 may
`direct physical contact .
`contain such instructions .
`In one or more arrangements , the one or more data stores
`As noted above , the vehicle 100 can include a sensor
`115 can include map data 111 . The map data 111 can include
`system 125 . The sensor system 125 can include one or more
`maps of one or more geographic areas or regions . The map 55 sensors . “ Sensor ” means any device , component and / or
`data 111 can include information or data on roads , traffic
`system that can detect , determine , assess , monitor , measure ,
`control devices , structures , features , landmarks in the one or
`quantify and / or sense something . The one or more sensors
`more geographic areas . The map data 111 can be in any
`can be configured to detect , determine , assess , monitor ,
`suitable form . In some instances , the map data 111 can
`measure , quantify and / or sense in real - time . As used herein ,
`include aerial views of an area . In some instances , the map 60 the term “ real - time ” means a level of processing respon
`data 111 can include ground views of an area , including 360
`siveness that a user or system senses as sufficiently imme
`degree ground views . The map data 111 can be highly
`diate for a particular process or determination to be made , or
`detailed . In some instances , the map data 111 can be located
`that enables the processor to keep up with some external
`onboard the vehicle 100 . Alternatively , at least a portion of
`process .
`the map data 111 can be located remote from the vehicle 100 . 65
`In arrangements in which the sensor system 125 includes
`In one or more arrangements , the one or more data stores
`a plurality of sensors , the sensors can work independently
`115 can include a set of driving scenes 112 . The term “ set of
`from each other . Alternatively , two or more of the sensors
`
`IPR2025-00943
`Tesla EX1021 Page 8
`
`

`

`US 9 , 884 , 631 B2
`
`one or more objects in the external environment of the
`can work in combination with each other . In such case , the
`vehicle 100 , the position of each detected object relative to
`two or more sensors can form a sensor network . The sensor
`the vehicle 100 , the distance between each detected object
`system 125 and / or the one or more sensors can be opera -
`and the vehicle 100 in one or more directions ( e . g . in the
`tively connected to the processor 110 , the data store 115 , the
`autonomous driving module 120 and / or other element of the 5 longitudinal direction 104 , the lateral direction 106 and / or
`vehicle 100 and / or the autonomous driving system .
`other direction ( s ) ) , the speed of each detected object and / or
`The sensor system 125 can include any suitable type of
`the movement of each detected object .
`sensor . For example , the sensor system 125 can include one
`In one or more arrangements , one or more of the envi
`or more sensors configured to detect , determine , assess ,
`ronment sensors 126 can use at least in part ultrasound . Such
`monitor , measure , quantify and / or sense information about 10 sensors can include an ultrasound source configured to emit
`the vehicle 100 . Alternatively or in addition , the sensor
`ultrasonic signals and a detector configured to detect reflec
`system 125 can include one or more sensors configured to
`tions of the ultrasonic signal . The one or more ultrasound
`detect , determine , assess , monitor , measure , quantify and / or
`based environment sensors 126 can be configured to detect ,
`sense information about the external environment in which
`determine , assess , monitor , measure , quantify and / or sense ,
`the vehicle 100 is located , including information about 15 directly or indirectly , the presence of one or more objects in
`objects in the external environment . Such objects may be
`the external environment of the vehicle 100 , the position of
`stationary object or moving objects . Alternatively or in
`each detected object relative to the vehicle 100 , the distance
`addition to one or more of the above examples , the sensor
`between each detected object and the vehicle 100 in one or
`system 125 can include one or more sensors configured to
`more directions ( e . g . in the longitudinal direction 104 , the
`detect , determine , assess , monitor , measure , quantify and / or 20 lateral direction 106 and / or other direction ( s ) ) , the speed of
`sense the location of the vehicle 100 and / or the location of
`each detected object and / or the movement of each detected
`objects in the environment relative to the vehicle 100 .
`object . Such detecting can be based on a characteristic ( e . g .
`Various examples of these and other types of sensors will be
`the intensity ) of a reflected ultrasonic signal .
`described herein . It will be understood that t