WO2017094656A1 - 車両道路網の走行に用いられる予測車両情報の生成方法及び装置 - Google Patents
車両道路網の走行に用いられる予測車両情報の生成方法及び装置 Download PDFInfo
- Publication number
- WO2017094656A1 WO2017094656A1 PCT/JP2016/085157 JP2016085157W WO2017094656A1 WO 2017094656 A1 WO2017094656 A1 WO 2017094656A1 JP 2016085157 W JP2016085157 W JP 2016085157W WO 2017094656 A1 WO2017094656 A1 WO 2017094656A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- vehicle
- remote vehicle
- lane
- road network
- probability value
- Prior art date
Links
- 238000000034 method Methods 0.000 title claims description 66
- 230000008569 process Effects 0.000 claims description 29
- 230000015654 memory Effects 0.000 claims description 22
- 230000006870 function Effects 0.000 claims description 13
- 238000012545 processing Methods 0.000 claims description 9
- 230000001133 acceleration Effects 0.000 claims description 6
- 238000005315 distribution function Methods 0.000 claims description 5
- 230000001186 cumulative effect Effects 0.000 claims description 4
- 238000004891 communication Methods 0.000 description 88
- 238000010586 diagram Methods 0.000 description 16
- 230000008859 change Effects 0.000 description 13
- 238000004364 calculation method Methods 0.000 description 11
- 230000005540 biological transmission Effects 0.000 description 10
- 230000003287 optical effect Effects 0.000 description 6
- 238000003491 array Methods 0.000 description 4
- 238000013459 approach Methods 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 230000001413 cellular effect Effects 0.000 description 2
- 238000002485 combustion reaction Methods 0.000 description 2
- 230000007423 decrease Effects 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 229910001416 lithium ion Inorganic materials 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- QELJHCBNGDEXLD-UHFFFAOYSA-N nickel zinc Chemical compound [Ni].[Zn] QELJHCBNGDEXLD-UHFFFAOYSA-N 0.000 description 2
- 239000004065 semiconductor Substances 0.000 description 2
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000003416 augmentation Effects 0.000 description 1
- OJIJEKBXJYRIBZ-UHFFFAOYSA-N cadmium nickel Chemical compound [Ni].[Cd] OJIJEKBXJYRIBZ-UHFFFAOYSA-N 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 229910052987 metal hydride Inorganic materials 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 230000007935 neutral effect Effects 0.000 description 1
- 229910052759 nickel Inorganic materials 0.000 description 1
- PXHVJJICTQNCMI-UHFFFAOYSA-N nickel Substances [Ni] PXHVJJICTQNCMI-UHFFFAOYSA-N 0.000 description 1
- -1 nickel metal hydride Chemical class 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000005381 potential energy Methods 0.000 description 1
- 239000000725 suspension Substances 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
- G08G1/161—Decentralised systems, e.g. inter-vehicle communication
- G08G1/163—Decentralised systems, e.g. inter-vehicle communication involving continuous checking
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
- G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/10—Path keeping
- B60W30/12—Lane keeping
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/14—Adaptive cruise control
- B60W30/16—Control of distance between vehicles, e.g. keeping a distance to preceding vehicle
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/12—Limiting control by the driver depending on vehicle state, e.g. interlocking means for the control input for preventing unsafe operation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/50—Determining position whereby the position solution is constrained to lie upon a particular curve or surface, e.g. for locomotives on railway tracks
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
- G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0133—Traffic data processing for classifying traffic situation
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
- G08G1/0141—Measuring and analyzing of parameters relative to traffic conditions for specific applications for traffic information dissemination
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/07—Controlling traffic signals
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
- G08G1/096725—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control where the received information generates an automatic action on the vehicle control
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096766—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096766—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission
- G08G1/096775—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission where the origin of the information is a central station
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096766—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission
- G08G1/096783—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission where the origin of the information is a roadside individual element
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096766—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission
- G08G1/096791—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission where the origin of the information is another vehicle
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
- G08G1/161—Decentralised systems, e.g. inter-vehicle communication
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
- G08G1/164—Centralised systems, e.g. external to vehicles
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2556/00—Input parameters relating to data
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
- G08G1/167—Driving aids for lane monitoring, lane changing, e.g. blind spot detection
Definitions
- the present disclosure relates to vehicle control including route search control and route guidance control.
- the vehicle may include a control system that generates and maintains a travel route, and controls the vehicle to travel on the travel route (going back and forth, and so on).
- a control system that generates and maintains a travel route, and controls the vehicle to travel on the travel route (going back and forth, and so on).
- an autonomously traveling vehicle may be autonomously controlled without direct human intervention in order to travel a travel route from a starting point to a destination. It is desirable to know where the remote vehicle is along the route, whether it is an autonomous vehicle or not.
- One aspect of the various disclosed embodiments is a method of generating predicted vehicle information used for traveling on a vehicle road network.
- This method includes a process for receiving remote vehicle spatial state information in a remote vehicle by a host vehicle, a process for specifying vehicle road network information corresponding to a part of the vehicle road network based on the remote vehicle spatial state information, and a computer recording medium.
- the processor responding to the stored command is configured so that each initial probability value can remotely
- a process for generating at least one initial probability value indicating the likelihood that the vehicle is following, a vehicle road between adjacent values for remote vehicle spatial state information for a plurality of time points including the initial time point Generate remote vehicle deviations for network information, and for each single lane and each deviation, using a new probability value based on the deviation,
- a process for generating a trajectory is executed. The order in which each process is executed is not limited to this.
- an in-vehicle device of a host vehicle that includes a processor that executes instructions stored in a non-transitory computer readable medium to provide a remote vehicle in a remote vehicle.
- a processor that receives spatial state information at a host vehicle, identifies vehicle road network information corresponding to a portion of the vehicle road network based on remote vehicle spatial state information, and responds to instructions stored in a computer recording medium, Based on the comparison between the remote vehicle spatial state information at the time and the vehicle road network information at the initial time, each initial probability value indicates the likelihood that the remote vehicle is following a single lane in the vehicle road network information, At least one initial probability value is generated, and remote vehicle spatial state information is obtained for a plurality of time points including the initial time point.
- the remote vehicle uses a new probability value based on the deviation to determine the single lane.
- the likelihood of tracing is updated, and the updated likelihood is used to generate a trajectory for the remote vehicle that is used with the vehicle road network information when the vehicle road network is driven by the host vehicle.
- the order in which each process is executed is not limited to this.
- Yet another aspect of the various disclosed embodiments is an apparatus that includes a non-transitory memory and a processor.
- This processor is a process for receiving remote vehicle spatial state information for a remote vehicle by executing instructions stored in a non-temporary memory, the processor based on the remote vehicle spatial state information.
- a process for generating a deviation of the remote vehicle relative to the network information, for each single lane and each deviation, a new probability value based on the deviation is used to make the remote vehicle a single lane
- FIG. 1 is a schematic illustration of an example of a portion of a vehicle in which various aspects, features and elements disclosed herein may be implemented.
- FIG. 2 is a schematic illustration of an example of a portion of a vehicle transportation and communication system in which various aspects, features and elements disclosed herein may be implemented.
- FIG. 3 is a flowchart of a method for generating predicted vehicle information for use when traveling on a vehicle road network in accordance with various teachings herein.
- FIG. 4A is a schematic diagram illustrating a remote vehicle in a portion of a vehicle road network at an initial point in time for use in generating predicted vehicle information in accordance with the present disclosure.
- FIG. 4B is a schematic diagram illustrating a remote vehicle in that portion of the vehicle road network at a point in time following the point in FIG. 4A for use in generating predicted vehicle information in accordance with the present disclosure.
- FIG. 4C is a schematic diagram illustrating a remote vehicle in that portion of the vehicle road network at a point in time following the point in FIG. 4B for use in generating predicted vehicle information in accordance with the present disclosure.
- FIG. 5 is a schematic diagram illustrating the calculation of deviations for vehicle road network information between adjacent values for remote vehicle spatial state information.
- FIG. 6 is a schematic diagram illustrating the generation of a trajectory of a remote vehicle over time based on a weighted average of the trajectory predicted based on speed and map curvature.
- FIG. 7 is a diagram illustrating a weighting function that may be used to generate the trajectory of FIG.
- FIG. 8 is a schematic diagram illustrating the generation of a (transitional) remote vehicle trajectory over time.
- FIG. 9 is a schematic diagram illustrating a host vehicle traveling on a vehicle road network using vehicle road network information and a track for a remote vehicle.
- Vehicles can travel from the starting point to the destination within the vehicle road network.
- driving plans for other vehicles are useful information.
- a driving plan supports the decision after obtaining detailed information.
- a vehicle must be able to predict what other objects in a scene are going to do before making a driving plan (driving plan) (also called driving plan or moving plan).
- driving plan also called driving plan or moving plan.
- the infrastructure can use such predictions to determine which of several vehicles attempting to enter an intersection has priority to pass through that intersection. .
- V2V vehicle-to-vehicle
- computer or “computing device” includes any unit or combination of units that can perform any method disclosed herein, or any portion or portions thereof.
- processor refers to one or more dedicated processors, one or more digital signal processors, one or more microprocessors, one or more controllers, one or more microcontrollers, one or more.
- Application processors one or more application-specific integrated circuits (Application Specific Integrated Circuits), one or more application specific standard products (Application Specific Standard Products), for example, one or more field programmable gate arrays (Field Programmable Gate Arrays) ),
- processors such as any other type of integrated circuit or combination thereof, one or more state machines, or any combination thereof.
- the term “memory” as used herein includes, tangibly (as a physical entity), stores, communicates, or transfers any signal or information used or associated with any processor. Indicates any computer-usable or computer-readable medium or device capable of.
- the memory may include one or more read only memories (ROM), one or more random access memories (RAM), one or more registers, a low power double data rate (LPDDR) memory, one or more cache memories, It may be one or more semiconductor memory devices, one or more magnetic media, one or more optical media, one or more magneto-optical media, or any combination thereof.
- instructions may include instructions or representations for performing any method disclosed herein, or any part or portions thereof, hardware, software or any of them. It may be realized in combination.
- the instructions may be implemented as information stored in a memory, such as a computer program, that may be executed by a processor that performs any of the respective methods, algorithms, aspects, or combinations thereof described herein.
- the instructions or portions thereof are implemented as a dedicated processor or circuit that may include dedicated hardware for performing any of the methods, algorithms, aspects, or combinations thereof described herein. Good.
- portions of the instructions may be distributed across multiple processors on a single device or on a multiplexed device, which may communicate directly or may be a local area network, a wide area network, You may communicate via networks, such as the internet or those combinations.
- any example, embodiment, implementation, aspect, feature or element is independent of any other example, embodiment, implementation, aspect, feature or element, and any other example, It may be used in combination with embodiments, implementations, aspects, features or elements.
- the terms “determine”, “identify or identify” and “generate”, or any variation thereof, are to be selected, elucidated, computed, referenced Determine, receive or accept information, establish, obtain or request information, or identify (identify) in any way using one or more of the devices shown and described herein. ) Or determining.
- the term “or” or “or” is intended to mean an inclusive “or” or “or” rather than an exclusive “or” or “or”. The That is, unless otherwise noted or apparent from the context, “X includes A or B” is intended to indicate any natural inclusive permutations. That is, if X includes A, X includes B, or X includes both A and B, “X includes A or B” is satisfied under any of the above examples. Also, as used in this application and the appended claims, “an”, “a”, “and” and “an” refer to the singular unless otherwise specified. Unless it is clear from the context, it should be generally interpreted to mean “one or more”.
- FIG. 1 is a schematic diagram of an example of a vehicle 1000 in which various aspects, features and elements disclosed herein may be implemented.
- the vehicle 1000 may include a chassis 1100, a powertrain 1200, a controller 1300, wheels 1400, or any other element or combination of elements of the vehicle.
- the vehicle 1000 is illustrated as including four wheels 1400 for simplicity, any other one or more propulsion devices such as a propeller or tread may be used.
- a line interconnecting a plurality of elements such as a power train 1200, a controller 1300, and a wheel 1400 has information such as data or control signals, power such as power or torque, or both information and power. It shows that each element can be exchanged.
- the controller 1300 may receive power from the power train 1200 and may communicate with the power train 1200, the wheels 1400, or both to control the vehicle 1000, which may include acceleration, deceleration, Steering or other control may be included.
- the powertrain 1200 may include a power source 1210, a transmission 1220, a steering unit 1230, an actuator 1240, or any other element or combination of elements of a powertrain such as a suspension, drive shaft, axle or exhaust system.
- a powertrain such as a suspension, drive shaft, axle or exhaust system.
- the wheel 1400 is illustrated separately from the power train 1200, the wheel 1400 may be included in the power train 1200.
- the power source 1210 may include an engine, a battery, or a combination thereof.
- the power source 1210 may be any device or combination of devices that operate to provide energy, such as electrical energy, thermal energy, or kinetic energy.
- the power source 1210 may include an engine, such as an internal combustion engine, an electric motor, or a combination of an internal combustion engine and an electric motor, and is operative to provide kinetic energy as a driving force to one or more of the wheels 1400. Good.
- the power source 1210 is a potential energy unit, such as a nickel-cadmium (NiCd), nickel-zinc (NiZn), nickel metal hydride (NiMH), lithium ion (Li-ion) battery, etc. , Solar cells, fuel cells, or any other device capable of supplying energy.
- Transmission 1220 may receive energy, such as kinetic energy, from power source 1210 and may transmit that energy to wheels 1400 to provide motive power. Transmission 1220 may be controlled by controller 1300, actuator 1240, or both.
- the steering unit 1230 may be controlled by the controller 1300, the actuator 1240, or both, and may control the wheels 1400 to steer the vehicle 1000.
- the vehicle actuator 1240 may receive signals from the controller 1300 and may operate or control the power source 1210, the transmission 1220, the steering unit 1230, or any combination thereof to operate the vehicle 1000.
- the controller 1300 may include a location unit 1310, an electronic communication unit 1320, a processor 1330, a memory 1340, a user interface 1350, a sensor 1360, an electronic communication interface 1370, or any combination thereof. . Although any one or more elements of controller 1300 are each illustrated as a single unit, they may be integrated into any number of separate physical units. For example, user interface 1350 and processor 1330 may be integrated into a first physical unit, and memory 1340 may be integrated into a second physical unit. Although not shown in FIG. 1, the controller 1300 may include a power source such as a battery.
- the location unit 1310, the electronic communication unit 1320, the processor 1330, the memory 1340, the user interface 1350, the sensor 1360, and the electronic communication interface 1370 are illustrated as separate elements, these or any combination thereof may be one or more. It may be integrated in an electronic unit, circuit or chip.
- processor 1330 may include any device or combination of devices capable of manipulating or processing signals or other information, including optical processors, quantum processors, molecules It may be an existing or future developed device that includes a processor, or a combination thereof.
- the processor 1330 may include one or more dedicated processors, one or more digital signal processors, one or more microprocessors, one or more controllers, one or more microcontrollers, one or more integrated circuits, one One or more application specific integrated circuits, one or more field programmable gate arrays, one or more programmable logic arrays, one or more programmable logic controllers, one or more state machines, or any combination thereof May be included.
- the processor 1330 may be operatively coupled to the location unit 1310, the memory 1340, the electronic communication interface 1370, the electronic communication unit 1320, the user interface 1350, the sensor 1360, the powertrain 1200, or any combination thereof.
- the processor may be operatively coupled to memory 1340 via communication bus 1380.
- Memory 1340 may include any tangible, non-transitory computer-usable or computer-readable medium that can be machine-readable, for example, for use by or related to processor 1330. Or any information associated therewith can be stored, communicated, or transported.
- the memory 1340 may be, for example, one or more semiconductor drives, one or more memory cards, one or more removable media, one or more read-only memories, one or more random access memories, a hard disk or floppy disk, optical It may be one or more disks, including disks, magnetic cards, optical cards, or any other type of non-transitory recording medium suitable for storing electronic information, or any combination thereof.
- the electronic communication interface 1370 may be a wireless antenna, a wired communication port, an optical communication port, or any other wired or wireless unit that can interface with a wired or wireless electronic communication medium 1500 as shown.
- FIG. 1 illustrates an electronic communication interface 1370 that communicates over a single communication link, the electronic communication interface 1370 may be configured to communicate over multiplexed communication links.
- the electronic communication unit 1320 may be configured to transmit or receive signals via a wired or wireless electronic communication medium 1500, such as via an electronic communication interface 1370.
- the electronic communication unit 1320 can be any wired or radio frequency (RF), ultraviolet (UV), visible light, optical fiber, wire line, or combination thereof. It may be configured to transmit, receive, or both via a wireless communication medium.
- FIG. 1 shows a single electronic communication unit 1320 and a single electronic communication interface 1370, any number of communication units and any number of electronic communication interfaces may be used.
- the location unit 1310 may determine geolocation information such as longitude, latitude, altitude, traveling direction or speed of the vehicle 1000.
- the location unit may be a global positioning system such as a National Marine Electronics Association (NMEA) unit, a radio triangulation unit, or a combination thereof, which is capable of Wide Area Augmentation System (WAAS). (GPS) units may be included.
- NMEA National Marine Electronics Association
- WAAS Wide Area Augmentation System
- GPS Wide Area Augmentation System
- User interface 1350 includes any unit that can interface with a person, such as a virtual keyboard, physical keyboard, touchpad, display, touch display, speaker, microphone, video camera, sensor, printer, or any combination thereof. Good. User interface 1350 may be operatively coupled with processor 1330 as shown, or may be operatively coupled with any other element of controller 1300. Although the user interface 1350 is illustrated as a single unit, the user interface 1350 may include one or more physical units. For example, the user interface 1350 includes a voice interface for performing voice communication with a person and / or a touch display for performing communication with a person based on visual recognition and touch operation (display capable of input by touch operation). May contain.
- the sensor 1360 often includes one or more sensors 1360, such as an array of sensors, and the sensor 1360 is operable to provide information that may be used to control the vehicle 1000. Good. Sensor 1360 may provide information regarding the current operating characteristics of the vehicle. When multiple sensors 1360 are included, they report information regarding, for example, speed sensors, acceleration sensors, steering angle sensors, friction-related sensors, braking-related sensors, or some aspect of the current dynamic situation of the vehicle 1000. Any sensor or combination of such sensors operable to do so may be included.
- the sensors 1360 may include one or more sensors that are operable to obtain information regarding the physical environment surrounding the vehicle 1000.
- one or more sensors 1360 may detect road shapes and obstacles such as fixed obstacles, vehicles, and pedestrians.
- the sensor 1360 may be a video camera, a laser sensing system, an infrared sensing system, an acoustic sensing system, or any suitable type of on-board environmental sensing device currently known or developed in the future or such It may be a combination of devices or may contain them.
- sensor 1360 and location unit 1310 may be combined.
- the vehicle 1000 may include a trajectory controller.
- the trajectory controller may operate to obtain information describing a current state of the vehicle 1000 and a route planned for the vehicle 1000 and to determine and optimize a trajectory for the vehicle 1000 based on this information.
- the trajectory controller outputs a signal operable to control the vehicle 1000 such that the vehicle 1000 follows a trajectory determined by the trajectory controller (a signal that controls the operation of the vehicle).
- the output of the trajectory controller may be an optimized trajectory that may be supplied to the powertrain 1200, the wheels 1400, or both.
- the optimized trajectory can be a plurality of control inputs, such as a series of steering angles, each corresponding to a point in time or a position.
- the optimized track can be one or more lanes, lines, curves, courses, or combinations thereof.
- the trajectory controller may be implemented at least in part using one or more elements of the controller 1300.
- One or more of the wheels 1400 may be steered wheels that can be turned toward the steering angle under the control of the steering unit 1230 and are torqued under the control of the transmission 1220 to propel the vehicle 1000. It may be a propelled heel wheel, or it may be a steering and driving wheel that can steer and propel the vehicle 1000.
- the vehicle 1000 includes a housing, a Bluetooth (registered trademark) module, a frequency modulation (FM) radio unit, a near field communication (NFC) (NFC).
- a Bluetooth (registered trademark) module may include units or elements not shown in FIG. 1, such as modules, liquid crystal display (LCD) display units, organic light emitting diode (OLED) display units, speakers, or any combination thereof.
- LCD liquid crystal display
- OLED organic light emitting diode
- FIG. 2 is a schematic diagram of an example of a portion of a vehicle transportation and communication system in which various aspects, features and elements disclosed herein may be implemented.
- the vehicle transport and communication system 2000 may include at least two vehicles 2100/2110, each of which may be configured similarly to the vehicle 1000 shown in FIG. It may travel through one or more portions of one or more vehicle road networks 2200 and may communicate via one or more electronic communication networks 2300. Although not explicitly shown in FIG. 2, the vehicle may travel in an area that is not explicitly or completely contained within the vehicle road network, such as an off-road area.
- the electronic communication network 2300 may be a multiple access system, for example, voice communication, data communication, video communication between each vehicle 2100/2110 and one or more communication devices 2400. Communication such as message communication or any combination thereof may be provided.
- the vehicle 2100/2110 may receive information such as information representing the vehicle road network 2200 from the communication device 2400 via the electronic communication network 2300.
- the electronic communication network 2300 can be used in vehicle-to-vehicle communication for basic safety messages that include vehicle 2100 position information and track information. Each vehicle 2100/2110 may communicate this information directly with one or more other vehicles, as discussed in more detail below.
- the vehicle 2100/2110 communicates via a wired communication link (not shown), a wireless communication link 2310/2320/2370, or a combination of any number of wired or wireless communication links.
- vehicles 2100/2110 may communicate via terrestrial wireless communication link 2310, non-terrestrial wireless communication link 2320, or a combination thereof.
- the terrestrial wireless communication link 2310 is an Ethernet (registered trademark) link, a serial link, a Bluetooth link, an infrared (IR) link, an ultraviolet (UV) link, or an electronic communication. May be included as an optional link.
- the vehicle 2100/2110 may communicate with other vehicles 2100/2110.
- the host vehicle, or main vehicle (HV) 2100 may send one or more automated inter-vehicle messages, such as basic safety messages, via a direct communication link 2370 or via an electronic communication network 2300, You may receive from the vehicle (RV) 2110 used as a target.
- the remote vehicle 2110 may send / deliver messages extensively to multiple host vehicles within a defined transmission range such as 300 meters.
- the host vehicle 2100 may receive the message via a third party, such as a signal repeater (not shown) or other remote vehicle (not shown).
- the vehicle 2100/2110 may periodically send one or more automated inter-vehicle messages based on a defined interval, such as 10 milliseconds.
- Automated inter-vehicle messages include vehicle identification information; spatial state information such as longitude, latitude and / or altitude information, geospatial location accuracy information; vehicle acceleration information, yaw rate information, speed information, vehicle traveling direction information, braking system state information Kinematic state information such as throttle information and steering wheel angle information; vehicle route information; vehicle operating state information such as vehicle size information, headlight state information, direction indicator information, wiper state information, transmission information; or vehicle state May include any other information related to the transmission of or a combination of such information.
- the transmission state information may indicate whether the transmission of the vehicle is in a neutral state, a parking state, a forward state, or a reverse state.
- the electronic communication unit 1320 can receive SONAR, RADAR and / or LIDAR signals, and calculate the vehicle position, velocity, acceleration and instantaneous vehicle traveling direction or vehicle direction from these signals. can do.
- the vehicle 2100 may communicate with the electronic communication network 2300 via the access point 2330.
- Access point 2330 may include a computing device to communicate with vehicle 2100, electronic communication network 2300, one or more communication devices 2400, or combinations thereof via a wired or wireless communication link 2310/2340. May be configured.
- the access point 2330 includes a base station, a base transceiver station (BTS), a node B (Node-B), an enhanced node B (eNode-B), a home node B (Home Node-B) (HNode).
- BTS base transceiver station
- Node-B node B
- eNode-B enhanced node B
- HNode home Node B
- -B may be a wireless router, wired router, hub, repeater, switch, or any similar wired or wireless device.
- the vehicle 2100 may communicate with the electronic communication network 2300 via a satellite 2350 or other non-terrestrial communication device.
- Satellite 2350 may include a computing device and is configured to communicate with vehicle 2100, electronic communication network 2300, one or more communication devices 2400, or combinations thereof via one or more communication links 2320/2360. May be.
- the satellite is illustrated as a single unit, the satellite may include any number of interconnected elements.
- the vehicle 2110 may similarly communicate with the electronic communication network 2300 via the access point 2330 and / or the satellite 2350.
- the electronic communication network 2300 may be any type of network configured to provide voice communication, data communication, or any other type of electronic communication.
- the electronic communication network 2300 includes a local area network (LAN), a wide area network (WAN), a virtual private network (VPN), a cellular phone network, a cellular telephone network, the Internet, or any other electronic communication system.
- the electronic communication network 2300 is a communication such as a communication control protocol (TCP), a user datagram protocol (UDP), an Internet protocol (IP), a real-time transport protocol (RTP), a hypertext transport protocol (HTTP), or a combination thereof.
- TCP communication control protocol
- UDP user datagram protocol
- IP Internet protocol
- RTP real-time transport protocol
- HTTP hypertext transport protocol
- HTTP hypertext transport protocol
- the vehicle 2100 can identify a portion or state of the vehicle road network 2200.
- the vehicle may include one or more in-vehicle sensors 2150, such as sensor 1360 shown in FIG. 1, which includes a speed sensor, a wheel speed sensor, a camera, a gyroscope, an optical sensor, a laser. Sensors, radar sensors, acoustic sensors, or any other sensor or device capable of determining or identifying a portion or condition of the vehicle road network 2200 or combinations thereof may be included.
- the vehicle 2100 may receive information communicated via the electronic communication network 2300, such as information representing the vehicle road network 2200, information identified by one or more in-vehicle sensors 2150, or a combination thereof. In use, one or more portions of one or more vehicle road networks 2200 may travel.
- FIG. 2 shows one vehicular road network 2200, one electronic communication network 2300, and one communication device 2400, but any number of networks or communication devices may be used.
- the vehicle transport and communication system 2000 may include devices, units or elements not shown in FIG. Although each vehicle 2100/2110 is illustrated as a single unit, the vehicle may include any number of interconnected elements.
- vehicle 2100 is illustrated as communicating with the communication device 2400 via the electronic communication network 2300, the vehicle 2100 and / or the vehicle 2110 may be a communication device via any number of direct or indirect communication links. You may communicate with 2400.
- each vehicle 2100/2110 may communicate with communication device 2400 via a direct communication link, such as a Bluetooth communication link.
- FIG. 3 is a flowchart of a method for generating predicted vehicle information for use when traveling on a vehicle road network in accordance with various teachings herein.
- the method may be implemented in a vehicle such as the vehicle 1000 shown in FIG. 1 or the vehicle 2100/2110 shown in FIG.
- the method may be implemented in whole or in part outside the vehicle, such as within one or more processors of the communication device 2400, such as a remote vehicle trajectory or a generated route.
- Related information is transmitted to one or more vehicles.
- the method includes receiving remote vehicle information at step 3000, identifying vehicle road network information at step 3010, and one or more in the vehicle road network information at step 3020. Generating an initial probability value for the lane, generating a deviation of the vehicle along the lane for subsequent time points in step 3030, updating the probability value for the lane in step 3040, and step 3050 Generating a remote vehicle trajectory over time, and traveling in the vehicle road network using the remote vehicle trajectory as input in step 3060.
- the host vehicle receives remote vehicle information while traveling on a part of the vehicle road network.
- the remote vehicle information received by the host vehicle at step 3000 includes remote vehicle spatial state information, and may include remote vehicle kinematic state information for the remote vehicle or data that can generate this information.
- the remote vehicle spatial state information may include, for example, a plurality of geospatial coordinates of the remote vehicle. These coordinates may be GPS coordinates for the latitude and longitude of the remote vehicle in some embodiments.
- the remote vehicle kinematic state information may include speed, acceleration, angle of orientation, etc. or information that can determine this information.
- remote vehicle spatial state information may be received by importing information from one or more data sets.
- this information is imported from a signal transmitted via the wireless electronic communication medium 1500 from the location unit 1310 of FIG.
- This information may relate to records from a single remote vehicle or multiple remote vehicles.
- Each record in the data set may be associated with a vehicle identifier, and individual remote vehicles may be uniquely identified or identified based on the respective vehicle identifier.
- These records may include data and time stamps and may be read periodically or as needed, such as when the vehicle road network on which the vehicle is traveling changes. .
- remote vehicle spatial state information may be received from the location of the infrastructure facility device within the vehicle road network.
- infrastructure facilities devices may include smart devices such as traffic lights, road sensors, road cameras, or any other non-vehicle device capable of detecting vehicles associated with a vehicle road network.
- remote vehicle spatial status information may be received from a portable device while attached to the vehicle.
- a portable device such as a smartphone carried by a vehicle occupant may include geographic location information such as GPS information or assisted GPS (AGPS) information, and includes information that associates the occupant with the vehicle. obtain.
- AGPS assisted GPS
- vehicle spatial state information is not limited to any particular technology as long as the technology can associate the vehicle spatial state information with at least one other information such as time and a specific remote vehicle.
- a sonar (SONAR), radar (RADAR) and / or laser (LIDAR) equipped in a vehicle or infrastructure facility input that can be used to calculate or generate vehicle spatial state information in step 3000. May be provided.
- Remote vehicle kinematic status information may be received using similar techniques. For example, if the remote vehicle remains in the same location between two measurements, it can be determined that the remote vehicle is not moving. In contrast, if the remote vehicle spatial state information is different for a remote vehicle between two measurements, that information and the time between the two measurements are used to generate the speed of that remote vehicle. be able to.
- the remote vehicle information received by the host vehicle in step 3000 may be in the form of the automated inter-vehicle message described above. This information may be received in whole or in part via dedicated short-range communication (DSRC) in vehicle-to-vehicle (V2V) and vehicle-to-base facility (V2I) short-range wireless communication.
- DSRC dedicated short-range communication
- V2V vehicle-to-vehicle
- V2I vehicle-to-base facility
- Remote vehicle information may be stored in the host vehicle's memory or other processing area provided for subsequent processing for subsequent processing, and may be stored with a time stamp.
- host vehicle information may be received and stored using similar techniques.
- direct sense signals eg, position and velocity
- that lead to observations may be filtered.
- step 3010 vehicle road network information corresponding to a part of the vehicle road network is specified. This identification can be performed based on remote vehicle spatial state information, where the remote vehicle is located in the vehicle road network information.
- a vehicle road network consists of one or more non-navigable areas such as buildings (non-travelable areas), one or more partially navigable areas such as parking lots, and one or more navigable areas such as roads (runnable areas). Or a combination thereof.
- the vehicle road network may include one or more interchanges between one or more navigable or partially navigable areas.
- a portion of a vehicle road network, such as a road may include one or more lanes and may be associated with one or more travel directions. Lane signs may or may not be present.
- a portion of the vehicle road network may be represented as vehicle road network information.
- the vehicle road network information may be expressed as a hierarchy of elements such as markup language elements that may be stored in a database or file.
- the vehicle road network information corresponding to a plurality of parts of the vehicle road network is represented as a schematic diagram or map, but the vehicle road network information is represented in the vehicle road network or a part thereof. Any computer-usable form that can be supported.
- the vehicle road network information includes vehicle roads such as travel direction information, speed limit information, toll information, gradient information such as slope information and angle information, surface material information, aesthetic information, or combinations thereof. Network control information may be included.
- the vehicle road network may be associated with or include a pedestrian road network such as a pedestrian footbridge or sidewalk, or a bicycle road network such as one or more bicycle lanes. You can leave.
- the pedestrian road network may correspond to a non-navigable area or a partially navigable area of the vehicle road network.
- the bicycle road network may correspond to a non-navigable area or a partially navigable area of the vehicle road network.
- FIG. 4A is a schematic diagram illustrating a remote vehicle in a portion of a vehicle road network at an initial time for use when generating predicted vehicle information in accordance with the present disclosure.
- the vehicle road network information corresponding to a part of the vehicle road network can be specified based on the remote vehicle spatial state information.
- the part may be in the lane that follows the current lane, in the lane that precedes the current lane, and in the vicinity of the remote vehicle (not limited to adjacent lanes) One or more adjacent lanes (also called sibling lanes).
- the portion may be a predetermined distance, or may be variable based on the traveling speed of the remote vehicle. For example, the portion can be identified based on a predetermined radius depending on how far the remote vehicle can travel at its current speed.
- FIG. 4A shows only two lanes extending from the common lane 4000, ie, lanes 4100, 4200, each of which has no adjacent lane (related lane).
- a centerline 4210 for lane 4200 is also shown.
- the remote vehicle 4300 is located in the portion of the vehicle road network information at the initial time, and the position is represented as a point A.
- a position on a lane may be referred to as a remote vehicle pose.
- the remote vehicle spatial state information and the vehicle road network information should be specified in the same coordinate system.
- the plurality of coordinates of the remote vehicle spatial state information are converted into a common coordinate system.
- This common coordinate system may be a Universal Transverse Mercator (UTM) coordinate system, in some embodiments, where multiple input points are transformed into multiple UTM coordinates within each UTM zone.
- UTM Universal Transverse Mercator
- the vehicle spatial state information is imported as GPS coordinates
- the vehicle spatial state information specified from the data set may be converted into UTM coordinates according to a known conversion formula.
- Other coordinate systems are possible as long as the selected coordinate system is consistently used.
- step 3020 at least one initial probability value is generated based on the comparison of the remote vehicle spatial state information and the vehicle road network information at the initial time point.
- Each initial probability value indicates the likelihood that the remote vehicle is following a single lane in the vehicle road network information. This likelihood can be considered as the probability that the vehicle follows the extending direction (direction) of a specific lane, or as the driving plan state. For example, if the remote vehicle is in an intersection, the driving plan state may be going straight, turning left, or turning right. When the remote vehicle is on the road (non-intersection), the driving plan state may be going straight, changing lanes to the left, or changing lanes to the right.
- the remote vehicle 4300 is in a position that can be specified by position information of the point A (also referred to as a first calculation point or a first point) at the initial time point.
- the first calculation point may be any point in the series of received data for the remote vehicle.
- the arbitrary point may be located at a first point where, for example, a unique ID associated with the remote vehicle is detected or assigned.
- the initial probability value at the first calculation point may be generated in various ways. As an example, if a remote vehicle is on a lane, that is, a lane that does not have an adjacent lane (related lane) (on a road with only a single lane in the direction of travel), the likelihood of going straight is 100%. Therefore, the initial probability value is 1.
- the probability may be inferred based on the options available to the vehicle at the first calculation point. For example, in FIG. 4A, there are only two driving plan states at the intersection, which are going straight through the lane, ie lane 4100, and turning right, following the lane, ie lane 4200. Therefore, the probability of going straight or turning right is equally 0.5. If there are three lanes at the intersection, this simple probability estimation formula will lead to 1/3 as the probability of each of the three driving plan states of straight ahead, left turn and right turn.
- the likelihood may change based on other variables.
- a remote vehicle transmits a V2V signal or a V2I signal
- the signal often includes other data.
- a turning signal may be used to evaluate one driving plan state more heavily than others. In other words, there may be situations where the remote vehicle is in a position where the probability is not evenly distributed among possible options, or other circumstances that cause the probability not to be evenly distributed among possible options. .
- step 3030 deviations in the remote vehicle spatial state information are generated for a plurality of subsequent time points.
- step 3040 for each single lane, the probability values for those lanes are updated.
- FIG. 4B is a schematic diagram illustrating a remote vehicle 4300 in that portion of the vehicle road network at a point in time following the point in FIG. 4A
- FIG. 4C is a vehicle road at a point in time following the point in FIG. 4B
- FIG. 6 is a schematic diagram showing a remote vehicle 4300 in that portion of the net. That is, the remote vehicle 4300 continues to travel after being located at the first point A in FIG. 4A, and is identified by the remote vehicle spatial state information at the second point B in FIG. 4B and then at the third point C in FIG. 4C. Is done.
- the remote vehicle 4300 travels gradually away from the center line 4210 of the lane 4200 when moving forward from the first point A to the second point B, and then from the second point B to the third point C. When traveling forward, the vehicle travels so as to gradually approach the center line 4210 of the lane 4200. In this case, intuitively, the probability that the remote vehicle 4300 follows the lane 4200 decreases at the second point and increases at the third point C. Deviations of the remote vehicle 4200 relative to the vehicle road network information between adjacent values for remote vehicle spatial state information can be generated and used to generate new probabilities over time.
- the deviation sets a dashed line 4400 extending from the Euclidean line 4500 between the second point B and the center line 4210 of the lane 4200 to the first point A in parallel with the center line 4210 of the lane 4200. May be generated.
- the difference between the second point B and the point where the first point A is projected onto the Euclidean line 4500 by the broken line 4400 is the deviation in the trajectory of the remote vehicle 4300 from the first point A to the second point B.
- the next deviation sets a dashed line 4600 extending from the third point C to the Euclidean line 4500 between the second point B and the center line 4210 of the lane 4200 in parallel with the center line 4210 of the lane 4200. May be generated.
- the difference between the second point B and the point at which the third point C is projected onto the Euclidean line 4500 by the broken line 4600 is the deviation in the trajectory of the remote vehicle 4300 from the second point B to the third point C.
- the Euclidean line may extend from the center line 4210 through the third point C (similar to the line 4500 passing through the second point B) and from the second point B to the Euclidean line.
- a parallel line may be projected forward toward.
- the difference between the third point C and the point where the second point B is projected onto the Euclidean line by the parallel line is also the remote vehicle from the second point B to the third point C. It represents the deviation in 4300 trajectories. These deviations are sometimes referred to as relative deviations.
- the new probability value based on the relative deviation between the second point B and the third point C may be the probability of deviation from the previous point.
- a value of 0.5 is used for illustration.
- the remote vehicle 4300 is moving so as to approach the center line 4210, and the updated likelihood must be increased. Therefore, the new probability value is used to divide the likelihood at the previous point. Used for.
- FIGS. 4A-4C illustrate the use of relative deviation when updating the likelihood that a remote vehicle is following a particular lane.
- a computational scheme that relates the deviation to the probability.
- Various functions may be used.
- the lateral distribution at a plurality of points along the center line such as the center line 4210 is a Gaussian distribution. Therefore, the relative deviation is related to the probability using the Gaussian cumulative distribution function.
- ⁇ standard deviation
- FIG. 5 shows the calculation of deviations for vehicle road network information between adjacent values for remote vehicle spatial state information.
- a positive value is used for the deviation to the left and a negative value is used for the deviation to the right.
- a single lane 5000 is shown having a left lane boundary 5010, a right lane boundary 5020, and a center line 5030.
- Centerline 5030 is most often not labeled or shown in conventional map data, and is the midpoint between left lane boundary 5010 and right lane boundary 5020 for purposes herein. It may be determined as
- a solid line 5100 indicates a vehicle track along the lane 5000. It is assumed that the vehicle is observed at points A, B, C, D, E, and F (that is, white points) as time passes.
- a black dot indicates a point on the center line 5030 closest to the position of the vehicle at the moment of observation. This defines the Euclidean line as briefly described with respect to FIGS. 4B and 4C. The Euclidean distance between each white point and black point is the deviation of the vehicle from the center line 5030 at the moment of observation. This is a point-to-point deviation to be noticed.
- the Euclidean distance extends between the current value for the remote vehicle spatial status information and the centerline from the previous value for the remote vehicle spatial status information, with the remote vehicle centerline parallel to the lane centerline. Projecting towards the Euclidean line, it may be the distance between the current value for the remote vehicle spatial state information and the center line of the remote vehicle on the Euclidean line. The deviation in this case can be generated as a relative deviation of the remote vehicle from the center line of the lane based on the Euclidean distance. Also, from another point of view, the Euclidean distance is determined by determining the remote vehicle space from the Euclidean line extending between the center line of the remote vehicle parallel to the center line of the lane and the previous value for remote vehicle spatial state information and the center line.
- the deviation in this case can be generated as a relative deviation of the remote vehicle from the center line of the lane based on the Euclidean distance.
- Point A is the point where the initial probability value for lane 5000 is generated in step 3020. Since only one lane is shown here, the probability of going straight is 1.0.
- each deviation can be calculated by extending the Euclidean line so that it passes through the new point and the center line, extending the line from the previous point to the Euclidean line, and the point where the line from the new point and the previous point intersects the Euclidean line. It may be calculated for a new point by calculating the distance between.
- the deviation is calculated in a similar manner as described with respect to FIGS. 4B and 4C.
- the deviation of the vehicle from the center line 5030 at the point B 0.16.
- the respective probability values are updated for lane 5000 in step 3040.
- the update is performed using the probability function f (x) and the generated deviation. Using the values in FIG. 5, it is possible to calculate the straight-line probability (that is, the likelihood of following the lane 5000) according to the following principle.
- the probability GS of straight travel at the point Xt when the vehicle is deviating from the center line 5030 of the lane 5000 toward either the left lane boundary 5010 or the right lane boundary 5020 may be calculated as follows.
- p (GS at point X t ) p (GS at point X t ⁇ 1 ) * f (dev ⁇ at point X t )
- the probability at point C is updated as follows.
- p (GS at point C) p (GS at point B) * f (dev ⁇ at point C)
- p (GS at point C) 0.952 * f (0.14)
- the point F presents a situation different from the previous point analysis. In this situation, the vehicle first moves toward the center line 5030 of the lane 5000 and then moves away from the center line 5030 of the lane 5000. In order to capture this dynamic movement, the line from point E to point F may be divided into two parts: a part from point E to center line 5030 and a part from center line 5030 to point F. . From point E to center line 5030, the probability increases as follows.
- p (GS) is the probability of going straight.
- the probability value depends on the number of lane options that the vehicle has. For each lane, p (GS) is calculated using the respective centerline. Since the probability or likelihood of following each lane is calculated independently, the combined value may result in a likelihood exceeding 100%. Accordingly, the probability of each option is normalized with respect to the other options so as not to exceed a probability of 1.0, for example.
- step 3040 a remote vehicle trajectory up to N seconds is generated.
- trajectories up to 4 seconds are generated.
- Other values for N may be specified by the user or operator.
- FIG. 6 is a schematic diagram illustrating the generation of a trajectory of a remote vehicle over time based on a weighted average of the trajectory predicted based on speed and map curvature.
- FIG. 6 shows one lane 6100 and the track for the vehicle 6000 as the vehicle 6000 follows the lane 6100, but the calculation is for other driving plans (left turn, right turn, lane change, etc.). May be the same.
- the remote vehicle 6000 is most recently located at the point shown in FIG. 6 and is traveling in the lane 6100 from the previous lane 6200.
- FIG. 6 also shows the center line 6150 of the lane 6100.
- the vehicle speed is a vector having the direction (traveling direction) and magnitude of the vehicle.
- the predicted trajectory of the vehicle 6000 based on the vehicle speed is indicated by an arrow 6300 as extending over time.
- the vehicle speed may be obtained, for example, from the remote vehicle kinematic state information optionally received at step 3000 in the process of FIG. 3, or may be calculated in whole or in part from other information received at step 3000. It's okay.
- a trajectory up to K seconds can be predicted.
- FIG. 6 shows a predicted trajectory that follows the curvature of the lane 6100.
- This predicted trajectory up to K seconds indicated by the arrow 6400 follows the center line 6150 of the lane 6100 using the speed of the vehicle 6000.
- the vehicle speed may be obtained, for example, from the remote vehicle kinematic state information optionally received at step 3000 in the process of FIG. 3, or may be calculated in whole or in part from other information received at step 3000. It's okay.
- An arrow 6500 represents a trajectory of the vehicle 6000 that is a weighted average of the trajectory prediction based on the current speed represented by the arrow 6300 and the trajectory prediction based on the map curvature represented by the arrow 6400.
- FIG. 7 is a diagram illustrating a weighting function that may be used to generate the trajectory of FIG. Numbers and lines are optional to show the relevant principles and do not represent the values in FIG.
- the numbers represent the weights given to each trajectory prediction over time.
- This weighting function is based on the assumption that the vehicle moves toward the center line of the predicted lane indicated by arrow 7000 over time. That is, for example, the trajectory speed prediction is directed to the left in FIG. 7, and the trajectory map curvature prediction is directed to the right in FIG.
- the maximum weight (such as 1.0) is given to the trajectory speed prediction, and the minimum weight (0 etc.) is given to the trajectory map curvature prediction.
- the weighting between the two predictions is, for example, that the maximum weight is given to the trajectory map curvature prediction and the minimum weight is used to predict the trajectory velocity at a future time point 7200 in K seconds. Change as given.
- the weighting of the two predictions is done using a cosine function.
- the map prediction weight may be according to the following equation: 0.5-0.5 * cos ((2 ⁇ / (2 * interpolation time length)) * t)
- the speed prediction weight may follow the following formula. 1- (0.5-0.5 * cos ((2 ⁇ / (2 * interpolation time length)) * t))
- the variable “interpolation time length” is equal to the length of time that the map prediction weight changes from 0.0 to 1.0 or the time that the speed prediction weight changes from 1.0 to 0.0. . This variable may be specified by the user.
- the trajectory may be calculated up to K seconds in the future.
- the operational plan is an input to this process. More specifically, as the remote vehicle travels through the vehicle road network, the remote vehicle approaches a change in vehicle road network information (eg, a lane change scene) that needs to be considered in generating the remote vehicle track. .
- vehicle road network information eg, a lane change scene
- the remote vehicle is at a current point 8000 on a single lane 8050 where the current driving plan is not quite straight forward.
- An example of a track for a remote vehicle is shown at 8100. However, when reaching point 8200, the driving plan may go straight or turn right due to the presence of lanes 8300 and 8400.
- the vehicle attitude ie, the position of the vehicle in the vehicle road network information
- speed are recalculated at point 8200.
- the next segment of the track is generated, such as track 8500 for the next driving plan to turn right.
- a track is generated as described above with respect to FIGS. 6 and 7, and a track is generated for each driving plan (eg, lane).
- a host vehicle 9000 and a remote vehicle 9100 are traveling within a portion of the vehicle road network, here an intersection 9300.
- the vehicle road network information includes lanes 1-8 and four stop signs each represented by line 9400.
- the host vehicle 9000 is located in the lane 2 and is scheduled to proceed to the lane 5 as indicated by an arrow extending from the host vehicle 9000.
- the remote vehicle 9100 is located in the lane 8 and may go straight, turn left, or turn right as indicated by the three arrows extending from the remote vehicle 9100.
- the remote vehicle 9100 reaches the stop sign on the lane 8 before the host vehicle 9000 reaches the stop sign on the lane 2.
- a lane change at intersection 9300 is not included as a possible driving plan.
- the host vehicle 9000 is related to the travel of the intersection 9300 based on, for example, the remote vehicle track for the lane and the likelihood that the remote vehicle 9100 follows one driving plan or another driving plan (ie, straight, left turn, or right turn). You may make a decision. For example, the host vehicle 9000 is likely to interfere with the path of the host vehicle 9000 by the remote vehicle 9100 trying to advance to the lane 3 (for example, a driving plan that goes straight is higher than a driving plan that turns left or right). If you have a likelihood or probability), you may decide to wait at the lane 2 stop sign to let the remote vehicle 9100 pass the intersection 9300 first. On the other hand, when the remote vehicle 9100 is about to turn right into the lane 1, the remote vehicle 9100 does not interfere with the host vehicle 9000, so the host vehicle 9000 may choose to continue straight ahead after a short stop.
- the remote vehicle 9100 is about to turn right into the lane 1, the remote vehicle 9100 does not interfere with the host vehicle 9000, so the host vehicle 9000 may
- the host vehicle 9000 may use the driving plan and predicted trajectory in any number of ways. For example, if both vehicles are traveling in the same lane on a straight road, the host vehicle 9000 uses the predicted trajectory to determine whether the host vehicle 9000 catches up with and overtakes the remote vehicle 9100, and when the host vehicle 9000. May decide to catch up with and overtake the remote vehicle 9100. In another example, the remote vehicle 9100 may travel in the left lane of the host vehicle 9000 on a straight road. At this time, the host vehicle 9000 determines whether or not the host vehicle 9000 should apply a brake using the predicted trajectory when the remote vehicle 9100 shows an intention / intent to change the lane in the lane in which the host vehicle 9000 is traveling.
- the host vehicle 9000 is configured such that, for example, a network infrastructure facility device such as an intersection signal controller receives or generates vehicle information (eg, remote vehicle information and host vehicle information) and manages the flow of intersections passing through traffic lights.
- vehicle information eg, remote vehicle information and host vehicle information
- the information generated for the remote vehicle 9100 by using the trajectory and likelihood may be used indirectly.
- Driving the vehicle road network may involve actions such as issuing an alarm to the driver of the host vehicle, or may involve taking corrective actions such as issuing a braking command to the braking system of the host vehicle. . Other corrective actions may be taken while the host vehicle is traveling on the vehicle road network at step 3060.
- the method may be repeated periodically or on demand while the host vehicle is traveling along the vehicle road network.
- the method may be repeated in whole or in part when a lane configuration in the vehicle road network information is changed (when a lane change is likely to be performed).
- this disclosure describes a single remote vehicle as a tracking target, other multiple powered vehicles that maintain their position within a given lane may be tracked. Furthermore, two or more remote vehicles may be tracked over time (over time).
- the above includes at most three lanes that branch from the same preceding lane at the intersection, ie, a straight lane, a left turn lane, and a right turn lane. The calculation is further complicated if additional lanes are introduced or if lane changes within an intersection may occur.
Landscapes
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Atmospheric Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Automation & Control Theory (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Human Computer Interaction (AREA)
- Traffic Control Systems (AREA)
- Aviation & Aerospace Engineering (AREA)
Abstract
Description
cdf(x=-2)=cdf(-2.0)=0.2275
cdf(x=-1)=cdf(-1.0)=0.16
cdf(x=0)=cdf(0)=0.5
f(x)=2*cdf(-abs(x))
ユークリッド距離は、リモート車両の中心線を車線のセンターラインと平行にリモート車両空間的状態情報のための先の値からリモート車両空間的状態情報のための現在の値とセンターラインとの間に延びるユークリッド線に向けて投影し、リモート車両空間的状態情報のための現在の値とユークリッド線上のリモート車両の中心線との間の距離であってもよい。この場合の偏差は、ユークリッド距離に基づき、車線のセンターラインからのリモート車両の相対偏差として生成できる。
また、他の観点から、ユークリッド距離は、リモート車両の中心線を車線のセンターラインと平行にリモート車両空間的状態情報のための先の値とセンターラインとの間に延びるユークリッド線からリモート車両空間的状態情報のための現在の値に向けて投影し、リモート車両空間的状態情報のための先の値とユークリッド線上のリモート車両の中心線との間の距離であってもよい。この場合の偏差は、ユークリッド距離に基づき、車線のセンターラインからのリモート車両の相対偏差として生成できる。
p(地点XtでのGS)=p(地点Xt-1でのGS)*f(地点XtでのdevΔ)
p(地点XtでのGS)=p(地点Xt-1でのGS)/f(地点XtでのdevΔ)
p(地点BでのGS)=p(地点AでのGS)*f(地点BでのdevΔ)
p(地点BでのGS)=1.0*f(0.06)
p(地点BでのGS)=1.0*2*cdf(-abs(0.06))=0.952
p(地点CでのGS)=p(地点BでのGS)*f(地点CでのdevΔ)
p(地点CでのGS)=0.952*f(0.14)
p(地点CでのGS)=0.952*2*cdf(-abs(0.14))=0.846
p(地点DでのGS)=p(地点CでのGS)*f(地点DでのdevΔ)
p(地点DでのGS)=0.846*f(0.1)
p(地点DでのGS)=0.846*2*cdf(-abs(0.1))=0.779
p(地点EでのGS)=p(地点DでのGS)/f(地点EでのdevΔ)
p(地点EでのGS)=0.779/f(-.15)
p(地点EでのGS)=0.779/(2*cdf(-abs(-.15))=0.884
p(センターラインでのGS)=p(地点EでのGS)/f(センターラインでのdevΔ)
p(センターラインでのGS)=0.884/f(-.25)
p(センターラインでのGS)=0.884/(2*cdf(-abs(-.25))=1.1
p(GS)=min(1.0,p(GS))
p(地点FでのGS)=p(センターラインでのGS)*f(センターラインから地点FまでのdevΔ)
p(地点FでのGS)=1.0*f(-0.1)
p(地点FでのGS)=1.0*2*cdf(-abs(-0.1))=0.92
p(RLC)=1-p(GS)
p(LLC)=0
p(LLC)=1-p(GS)
p(RLC)=0
0.5-0.5*cos((2π/(2*補間時間長))*t)
1-(0.5-0.5*cos((2π/(2*補間時間長))*t))
0.5-0.5*cos((2π/(2*5秒))*2秒)=0.345
1-0.345=0.655
0.345*TM+0.655*TV
Claims (20)
- 車両道路網の走行に用いられる予測車両情報の生成方法であって、
リモート車両におけるリモート車両空間的状態情報をホスト車両で受信し、
前記リモート車両空間的状態情報に基づき前記車両道路網の一部分に対応する車両道路網情報を特定し、
コンピュータ記録媒体に記憶された命令に応答するプロセッサに、初期時点における前記リモート車両空間的状態情報と初期時点における前記車両道路網情報との比較に基づき、各々の初期確率値が前記車両道路網情報内における単一車線を前記リモート車両が辿っている尤度を示す、当該初期確率値を少なくとも一つを生成させ、
前記初期時点を含む複数の時点について、前記リモート車両空間的状態情報のための隣接値の間での、前記車両道路網情報に対する前記リモート車両の偏差を生成し、
各単一車線及び各偏差に対して、前記偏差に基づく新たな確率値を用いて、前記リモート車両が前記単一車線を辿っている尤度を更新し、
前記更新された尤度を用いて、前記車両道路網を前記ホスト車両に走行させる場合に前記車両道路網情報とともに用いられる、前記リモート車両のための軌道を生成する、予測車両情報の生成方法。 - 前記リモート車両空間的状態情報に基づき前記車両道路網の一部分を表す前記車両道路網情報を特定する処理は、
前記リモート車両空間的状態情報を前記車両道路網情報と比較することにより、前記リモート車両が走行可能な少なくとも一つの車線を特定する処理を含む請求項1に記載の方法。 - 前記ホスト車両にて継時的に前記リモート車両のリモート車両運動学的状態情報を受信する処理をさらに行い、
前記更新された尤度を用いて前記軌道を生成する処理は、前記リモート車両運動学的状態情報を用いて前記軌道を生成する処理を含む請求項1又は2の方法。 - 前記リモート車両空間的状態情報は前記リモート車両の空間座標を含み、前記リモート車両運動学的状態情報は前記リモート車両のリモート車両速度又は前記リモート車両のリモート車両加速度の少なくとも一方を含む請求項3に記載の方法。
- 前記ホスト車両において、継時的に前記リモート車両の前記リモート車両運動学的状態情報を受信し、
前記更新された尤度を用いて前記軌道を生成する処理は、
各特定車線に対して、前記リモート車両運動学的状態情報を用いて、前記リモート車両の現在位置から、共通の将来地点にやがて到達する、予測される複数の軌道を形成し、各々の軌道を生成する処理を含む請求項3又は4に記載の方法。 - 前記少なくとも一つの初期確率値を生成する処理は、
前記リモート車両空間的状態情報と前記車両道路網情報とを比較し、
前記初期時点において前記車両道路網情報内で前記リモート車両が利用可能な車線の現在の数を定め、
各初期確率値を、前記車線の現在の数によって定められた値と同じ値にする請求項1~5の何れか一項に記載の方法。 - 前記更新された尤度を用いて前記軌道を生成する処理は、
前記リモート車両の現在速度と進行方向を用いて一連の第1の軌道値を生成し、
前記更新された尤度を用いて一連の第2の軌道値を生成する処理と、
前記一連の第1の軌道値と前記一連の第2の軌道値を結合することによって前記軌道を生成する処理と、を含む請求項1~6の何れか一項に記載の方法。 - 前記一連の第1の軌道値と前記一連の第2の軌道値を結合する処理は、
補間時間のための複数の時間間隔の各々の終わりにおいて、前記一連の第1の軌道値の各第1の軌道値を、前記一連の第2の軌道値の当該第2の軌道値と共に、重み付けする余弦関数を適用する請求項7に記載の方法。 - 前記単一車線は複数の車線の一つであり、
請求項1~8の何れか一項に記載の方法は、前記複数の車線の各車線に対して、
前記車線のセンターラインを定める処理と、
前記複数の時点の各現在の時点に対して、
前記現在の時点における前記リモート車両の現在位置に関連する、前記リモート車両空間的状態情報のための現在の値を受信する処理と、
前記リモート車両の中心線を前記車線のセンターラインと平行に前記リモート車両空間的状態情報のための先の値から前記リモート車両空間的状態情報のための前記現在の値と前記センターラインとの間に延びるユークリッド線に向けて投影し、且つ、前記リモート車両空間的状態情報のための前記現在の値と前記ユークリッド線上の前記リモート車両の中心線との間のユークリッド距離に基づき、前記車線のセンターラインからの前記リモート車両の相対偏差として前記偏差を生成する処理、又は、
前記リモート車両の中心線を前記車線のセンターラインと平行に前記リモート車両空間的状態情報のための先の値と前記センターラインとの間に延びるユークリッド線から前記リモート車両空間的状態情報のための前記現在の値に向けて投影し、且つ、前記リモート車両空間的状態情報のための前記先の値と前記ユークリッド線上の前記リモート車両の中心線との間のユークリッド距離に基づき、前記車線のセンターラインからの前記リモート車両の相対偏差として前記偏差を生成する処理、のいずれか一方の処理と、を含み、
前記新たな確率値は、前記相対偏差に基づくものである請求項1に記載の方法。 - 前記相対偏差に基づく前記新たな確率値を用いて前記初期確率値を更新する処理は、
ガウス分布の累積分布関数を用いて前記相対偏差を前記新たな確率値に関係付ける処理を含む請求項9に記載の方法。 - 前記リモート車両空間的状態情報のための前記隣接値の間の相対偏差が、前記リモート車両が前記単一車線のセンターラインに対して第1の方向に移動していることを示している場合に、前記偏差を正の値として定める処理と、
前記リモート車両空間的状態情報のための前記隣接値の間の前記相対偏差が、前記リモート車両が前記単一車線のセンターラインに対して前記第1の方向と逆の第2の方向に移動していることを示している場合に、前記偏差を負の値として定める処理と、を更に含み、
前記新たな確率値は前記偏差の関数である請求項1~10の何れか一項に記載の方法。 - 各単一車線及び各偏差に対して前記新たな確率値を用いて前記尤度を更新する処理は、
前記リモート車両が前記複数の時点の範囲内の先の時点でよりも前記複数の時点のうちの現在の時点でセンターラインに近いが、前記先の時点と前記現在の時点との間に前記センターラインを横断しない場合、前記先の時点のための先の確率値を前記新たな確率値で除することによって前記現在の時点のための現在の確率値を生成する処理と、
前記リモート車両が前記複数の時点の範囲内の前記先の時点でよりも前記複数の時点のうちの前記現在の時点で前記センターラインから遠いが、前記先の時点と前記現在の時点との間に前記センターラインを横断しない場合、前記先の時点のための前記先の確率値に前記新たな確率値を乗じることによって前記現在の時点のための前記現在の確率値を生成する処理と、
前記リモート車両が前記先の時点と前記現在の時点との間に前記センターラインを横断した場合に、
前記複数の時点の範囲内の前記先の時点のための前記先の確率値を、前記リモート車両が前記先の時点でのスタート地点から前記センターラインに向けて走行することに起因する前記偏差の一部分に基づく第1の確率値で除した結果を生成し、
前記結果に、前記リモート車両が前記センターラインから前記現在の時点での現在位置に向けて走行すること起因する前記偏差の一部分に基づく第2の確率値を乗じて前記現在の確率値を生成することにより、前記複数の時点のうちの前記現在の時点のための前記現在の確率値を生成する処理と、を含む請求項1~11の何れか一項に記載の方法。 - 車両道路網の走行に用いられる予測車両情報を生成する装置であって、
非一時的メモリと、
プロセッサと、を備え、
前記プロセッサが、前記非一時的メモリに記憶された命令を実行することで、
リモート車両のためのリモート車両空間的状態情報を受信し、
前記リモート車両空間的状態情報に基づき前記車両道路網の一部分を表す車両道路網情報を特定し、
各々の初期確率値が前記車両道路網情報内における単一車線を前記リモート車両が辿っている尤度を示す、当該初期確率値の少なくとも一つを、初期時点における前記リモート車両空間的状態情報と前記車両道路網情報との比較に基づき生成し、
前記初期時点を含む複数の時点に対して、前記リモート車両空間的状態情報のための隣接値の間での、前記車両道路網情報に対する前記リモート車両の偏差を生成し、
各単一車線及び各偏差に対して、前記偏差に基づく新たな確率値を用いて、前記リモート車両が前記単一車線を辿っている尤度を更新し、
前記更新された尤度を用いて、前記車両道路網をホスト車両に走行させる場合に前記車両道路網情報とともに用いられる、前記リモート車両のための軌道を生成する予測車両情報の生成装置。 - 前記プロセッサは、複数の車線を特定することにより、前記リモート車両空間的状態情報に基づき、前記車両道路網の一部分を表す車両道路網情報を特定するように構成され、
前記複数の車線の各々は、
前記初期時点において前記リモート車両が走行している現在の車線、
前記現在の車線に隣接する隣接車線、
前記現在の車線の前に現れる先の車線、又は
前記現在の車線の後に現れる将来の車線の一つである請求項13に記載の装置。 - 前記プロセッサは、
継時的に前記リモート車両のリモート車両運動学的状態情報を受信し、
前記リモート車両運動学的状態情報を用いて前記軌道を生成することによって、前記更新された尤度用いて前記軌道を生成するように構成される請求項13又は14に記載の装置。 - 前記プロセッサは、前記リモート車両から送信されたワイヤレス信号内の前記リモート車両運動学的状態情報を受信するように構成される請求項15に記載の装置。
- 前記プロセッサは、前記ホスト車両の少なくとも一つのセンサを用いて前記リモート車両運動学的状態情報を受信するように構成される請求項15に記載の装置。
- 前記プロセッサは、前記リモート車両空間的状態情報と前記車両道路網情報とを比較し、前記初期時点において前記車両道路網情報内で前記リモート車両が利用可能な車線の現在の数を定めることによって、前記少なくとも一つの初期確率値を生成するように構成され、
各初期確率値は前記車線の現在の数によって除された一つに等しく、
前記車線の現在の数は1より大きい請求項13~17の何れか一項に記載の装置。 - 前記プロセッサは各車線に対して前記偏差のためのそれぞれの値を生成するように構成され、
前記プロセッサは、各車線に対して、
前記リモート車両が前記複数の時点の範囲内の先の時点でよりも前記複数の時点のうちの現在の時点で前記車線のセンターラインに近いが、前記先の時点と前記現在の時点との間に前記センターラインを横断しない場合、前記先の時点のための先の確率値を前記新たな確率値で除することによって前記現在の時点のための現在の確率値を生成する処理と、
前記リモート車両が前記複数の時点の範囲内の前記先の時点でよりも前記複数の時点のうちの前記現在の時点で前記センターラインから遠いが、前記先の時点と前記現在の時点との間に前記センターラインを横断しない場合、前記先の時点のための前記先の確率値に前記新たな確率値を乗じることによって前記現在の時点のための前記現在の確率値を生成する処理と、
前記リモート車両が前記先の時点と前記現在の時点との間に前記センターラインを横断した場合に、
前記複数の時点の範囲内の前記先の時点のための前記先の確率値を、前記リモート車両が前記先の時点でのスタート地点から前記センターラインに向けて走行することに起因する前記偏差の一部分に基づく第1の確率値で除した結果を生成し、
前記結果に、前記リモート車両が前記センターラインから前記現在の時点での現在位置に向けて走行することに起因する前記偏差の一部分に基づく第2の確率値を乗じて前記現在の確率値を生成することにより、前記複数の時点のうちの前記現在の時点のための前記現在の確率値を生成する処理と、
によって前記新たな確率値を用いて前記尤度を更新するように構成される請求項13~18の何れか一項に記載の装置。 - 前記プロセッサは、各車線のために前記軌道を生成する前の前記車線の現在の数に基づき前記複数の時点の各々での前記現在の確率値を正規化するように構成される請求項19に記載の装置。
Priority Applications (8)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
KR1020187014643A KR101970931B1 (ko) | 2015-11-30 | 2016-11-28 | 차량 도로망의 주행에 사용되는 예측 차량 정보의 생성 방법 및 장치 |
RU2018122451A RU2714056C2 (ru) | 2015-11-30 | 2016-11-28 | Работа рассматриваемого транспортного средства с использованием прогнозирования намерений удаленных транспортных средств |
CN201680067253.8A CN108292475B (zh) | 2015-11-30 | 2016-11-28 | 车辆道路网的行驶中使用的预测车辆信息的生成方法以及装置 |
JP2017553835A JP6540826B2 (ja) | 2015-11-30 | 2016-11-28 | 車両道路網の走行に用いられる予測車両情報の生成方法及び装置 |
BR112018010601-1A BR112018010601A2 (ja) | 2015-11-30 | 2016-11-28 | A generation method and a device of prediction vehicle information which are used for a run of a vehicle road network |
CA3006546A CA3006546A1 (en) | 2015-11-30 | 2016-11-28 | Host vehicle operation using remote vehicle intention prediction |
EP16870593.7A EP3385930B1 (en) | 2015-11-30 | 2016-11-28 | Method and device for generating forecast vehicular information used for traveling on vehicle road network |
MX2018006120A MX368090B (es) | 2015-11-30 | 2016-11-28 | Operacion de vehiculo principal utilizando prediccion de intencion de vehiculo remoto. |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US14/954,083 US10152882B2 (en) | 2015-11-30 | 2015-11-30 | Host vehicle operation using remote vehicle intention prediction |
US14/954,083 | 2015-11-30 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2017094656A1 true WO2017094656A1 (ja) | 2017-06-08 |
Family
ID=58777686
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/JP2016/085157 WO2017094656A1 (ja) | 2015-11-30 | 2016-11-28 | 車両道路網の走行に用いられる予測車両情報の生成方法及び装置 |
Country Status (10)
Country | Link |
---|---|
US (1) | US10152882B2 (ja) |
EP (1) | EP3385930B1 (ja) |
JP (1) | JP6540826B2 (ja) |
KR (1) | KR101970931B1 (ja) |
CN (1) | CN108292475B (ja) |
BR (1) | BR112018010601A2 (ja) |
CA (1) | CA3006546A1 (ja) |
MX (1) | MX368090B (ja) |
RU (1) | RU2714056C2 (ja) |
WO (1) | WO2017094656A1 (ja) |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2021520003A (ja) * | 2018-05-31 | 2021-08-12 | ニッサン ノース アメリカ,インク | 確率的オブジェクト追跡及び予測フレームワーク |
US11273838B2 (en) * | 2019-07-17 | 2022-03-15 | Huawei Technologies Co., Ltd. | Method and apparatus for determining vehicle speed |
Families Citing this family (27)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
DE102015015944A1 (de) * | 2015-12-08 | 2017-06-08 | Audi Ag | Verfahren zur Unterstützung eines Fahrers eines Kraftfahrzeugs hinsichtlich bevorstehender Überholmanöver und Kraftfahrzeug |
US10486707B2 (en) * | 2016-01-06 | 2019-11-26 | GM Global Technology Operations LLC | Prediction of driver intent at intersection |
US11640168B2 (en) * | 2016-08-31 | 2023-05-02 | Faraday & Future Inc. | System and method for controlling a driving system |
CN108082185B (zh) * | 2017-03-30 | 2021-01-01 | 长城汽车股份有限公司 | 一种车辆的行驶控制方法、装置和车辆 |
JP6717778B2 (ja) * | 2017-05-15 | 2020-07-08 | トヨタ自動車株式会社 | 道路リンク情報更新装置及び車両制御システム |
CN108932462B (zh) * | 2017-05-27 | 2021-07-16 | 华为技术有限公司 | 驾驶意图确定方法及装置 |
KR20190035159A (ko) * | 2017-09-26 | 2019-04-03 | 삼성전자주식회사 | 차량 움직임 예측 방법 및 장치 |
US10745010B2 (en) * | 2017-12-21 | 2020-08-18 | International Business Machines Corporation | Detecting anomalous vehicle behavior through automatic voting |
EP3514494A1 (en) * | 2018-01-19 | 2019-07-24 | Zenuity AB | Constructing and updating a behavioral layer of a multi layered road network high definition digital map |
CN111886611A (zh) | 2018-01-31 | 2020-11-03 | 北美日产公司 | 用于自主运载工具的批路线规划的计算框架 |
CN111788532B (zh) | 2018-02-28 | 2022-05-10 | 北美日产公司 | 用于自主运载工具决策的运输网络基础设施 |
US11378956B2 (en) * | 2018-04-03 | 2022-07-05 | Baidu Usa Llc | Perception and planning collaboration framework for autonomous driving |
US10569773B2 (en) | 2018-05-31 | 2020-02-25 | Nissan North America, Inc. | Predicting behaviors of oncoming vehicles |
US10745011B2 (en) | 2018-05-31 | 2020-08-18 | Nissan North America, Inc. | Predicting yield behaviors |
US10564643B2 (en) | 2018-05-31 | 2020-02-18 | Nissan North America, Inc. | Time-warping for autonomous driving simulation |
EP3640679B1 (en) * | 2018-10-15 | 2023-06-07 | Zenuity AB | A method for assigning ego vehicle to a lane |
DK201970148A1 (en) * | 2018-12-10 | 2020-07-06 | Aptiv Tech Ltd | Motion graph construction and lane level route planning |
US20200267681A1 (en) * | 2019-02-19 | 2020-08-20 | Qualcomm Incorporated | Systems and methods for positioning with channel measurements |
US11180140B2 (en) * | 2019-03-28 | 2021-11-23 | Baidu Usa Llc | Camera-based low-cost lateral position calibration method for level-3 autonomous vehicles |
US11934191B2 (en) * | 2019-07-05 | 2024-03-19 | Huawei Technologies Co., Ltd. | Method and system for predictive control of vehicle using digital images |
CN112534483B (zh) * | 2020-03-04 | 2021-12-14 | 华为技术有限公司 | 预测车辆驶出口的方法和装置 |
CN112133089B (zh) * | 2020-07-21 | 2021-11-19 | 西安交通大学 | 一种基于周围环境与行为意图的车辆轨迹预测方法、系统及装置 |
CN114056347A (zh) * | 2020-07-31 | 2022-02-18 | 华为技术有限公司 | 车辆运动状态识别方法及装置 |
US11688179B2 (en) * | 2020-09-03 | 2023-06-27 | Pony Ai Inc. | Inferring intent using computer vision |
CN112687121A (zh) * | 2020-12-21 | 2021-04-20 | 苏州挚途科技有限公司 | 行驶轨迹的预测方法、装置及自动驾驶车辆 |
CN113129603B (zh) * | 2021-03-26 | 2022-06-17 | 深圳市跨越新科技有限公司 | 平行道路超速判定方法、装置、终端及存储介质 |
CN113324555B (zh) * | 2021-05-31 | 2024-05-03 | 阿波罗智联(北京)科技有限公司 | 一种车辆导航路径的生成方法、装置及电子设备 |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2007137248A (ja) * | 2005-11-17 | 2007-06-07 | Toyota Motor Corp | 走行支援装置及び走行支援システム |
JP2011192177A (ja) * | 2010-03-16 | 2011-09-29 | Toyota Motor Corp | 前方状況予測装置 |
JP2013152540A (ja) * | 2012-01-24 | 2013-08-08 | Toyota Motor Corp | 走行車線認識装置 |
Family Cites Families (28)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7629899B2 (en) * | 1997-10-22 | 2009-12-08 | Intelligent Technologies International, Inc. | Vehicular communication arrangement and method |
US7796081B2 (en) * | 1997-10-22 | 2010-09-14 | Intelligent Technologies International, Inc. | Combined imaging and distance monitoring for vehicular applications |
US6581005B2 (en) * | 2000-11-30 | 2003-06-17 | Nissan Motor Co., Ltd. | Vehicle position calculation apparatus and method |
US6882287B2 (en) * | 2001-07-31 | 2005-04-19 | Donnelly Corporation | Automotive lane change aid |
US7102496B1 (en) * | 2002-07-30 | 2006-09-05 | Yazaki North America, Inc. | Multi-sensor integration for a vehicle |
JP3925474B2 (ja) * | 2003-07-18 | 2007-06-06 | 日産自動車株式会社 | 車線変更支援装置 |
DE102004027983A1 (de) * | 2003-09-23 | 2005-04-21 | Daimler Chrysler Ag | Verfahren und Vorrichtung zur Erkennung von Spurwechselvorgängen für ein Fahrzeug |
US7729857B2 (en) * | 2005-08-18 | 2010-06-01 | Gm Global Technology Operations, Inc. | System for and method of detecting a collision and predicting a vehicle path |
US20080065328A1 (en) * | 2006-09-08 | 2008-03-13 | Andreas Eidehall | Method and system for collision avoidance |
US20090140887A1 (en) * | 2007-11-29 | 2009-06-04 | Breed David S | Mapping Techniques Using Probe Vehicles |
KR100947174B1 (ko) * | 2008-03-07 | 2010-03-12 | 조용성 | 구간별 교통정보 검지장치를 이용한 실시간 교통정보 제공시스템 및 그 방법 |
US8538613B2 (en) * | 2010-11-15 | 2013-09-17 | GM Global Technology Operations LLC | Method for determining an estimated driving range for a vehicle |
US8452535B2 (en) * | 2010-12-13 | 2013-05-28 | GM Global Technology Operations LLC | Systems and methods for precise sub-lane vehicle positioning |
US8914181B2 (en) * | 2010-12-29 | 2014-12-16 | Siemens S.A.S. | System and method for active lane-changing assistance for a motor vehicle |
EP2562060B1 (en) * | 2011-08-22 | 2014-10-01 | Honda Research Institute Europe GmbH | A method and system for predicting movement behavior of a target traffic object |
US8810431B2 (en) * | 2011-10-20 | 2014-08-19 | GM Global Technology Operations LLC | Highway merge assistant and control |
US9771070B2 (en) * | 2011-12-09 | 2017-09-26 | GM Global Technology Operations LLC | Method and system for controlling a host vehicle |
US8706417B2 (en) * | 2012-07-30 | 2014-04-22 | GM Global Technology Operations LLC | Anchor lane selection method using navigation input in road change scenarios |
DE102012021282A1 (de) * | 2012-10-29 | 2014-04-30 | Audi Ag | Verfahren zur Koordination des Betriebs von vollautomatisiert fahrenden Kraftfahrzeugen |
US8788134B1 (en) | 2013-01-04 | 2014-07-22 | GM Global Technology Operations LLC | Autonomous driving merge management system |
US8825378B2 (en) * | 2013-01-25 | 2014-09-02 | Nissan North America, Inc. | Vehicle drift determination apparatus and method |
KR20140126975A (ko) * | 2013-04-24 | 2014-11-03 | 주식회사 만도 | 차량의 충돌 회피 장치 및 방법 |
KR101462519B1 (ko) * | 2013-04-30 | 2014-11-18 | 현대엠엔소프트 주식회사 | 내비게이션 및 그 경로 안내 방법 |
EP3812962A1 (en) * | 2013-12-04 | 2021-04-28 | Mobileye Vision Technologies Ltd. | Navigating a vehicle to pass another vehicle |
US9165477B2 (en) * | 2013-12-06 | 2015-10-20 | Vehicle Data Science Corporation | Systems and methods for building road models, driver models, and vehicle models and making predictions therefrom |
US9227635B1 (en) * | 2014-09-25 | 2016-01-05 | Nissan North America, Inc. | Method and system of assisting a driver of a vehicle |
CN104464344B (zh) * | 2014-11-07 | 2016-09-14 | 湖北大学 | 一种车辆行驶路径预测方法及系统 |
US9443153B1 (en) * | 2015-06-12 | 2016-09-13 | Volkswagen Ag | Automatic labeling and learning of driver yield intention |
-
2015
- 2015-11-30 US US14/954,083 patent/US10152882B2/en active Active
-
2016
- 2016-11-28 CA CA3006546A patent/CA3006546A1/en not_active Abandoned
- 2016-11-28 MX MX2018006120A patent/MX368090B/es active IP Right Grant
- 2016-11-28 WO PCT/JP2016/085157 patent/WO2017094656A1/ja active Application Filing
- 2016-11-28 CN CN201680067253.8A patent/CN108292475B/zh active Active
- 2016-11-28 KR KR1020187014643A patent/KR101970931B1/ko active IP Right Grant
- 2016-11-28 JP JP2017553835A patent/JP6540826B2/ja active Active
- 2016-11-28 EP EP16870593.7A patent/EP3385930B1/en active Active
- 2016-11-28 RU RU2018122451A patent/RU2714056C2/ru active
- 2016-11-28 BR BR112018010601-1A patent/BR112018010601A2/ja not_active Application Discontinuation
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2007137248A (ja) * | 2005-11-17 | 2007-06-07 | Toyota Motor Corp | 走行支援装置及び走行支援システム |
JP2011192177A (ja) * | 2010-03-16 | 2011-09-29 | Toyota Motor Corp | 前方状況予測装置 |
JP2013152540A (ja) * | 2012-01-24 | 2013-08-08 | Toyota Motor Corp | 走行車線認識装置 |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2021520003A (ja) * | 2018-05-31 | 2021-08-12 | ニッサン ノース アメリカ,インク | 確率的オブジェクト追跡及び予測フレームワーク |
RU2762786C1 (ru) * | 2018-05-31 | 2021-12-22 | Ниссан Норт Америка, Инк. | Планирование траектории |
JP7140849B2 (ja) | 2018-05-31 | 2022-09-21 | ニッサン ノース アメリカ,インク | 確率的オブジェクト追跡及び予測フレームワーク |
US11273838B2 (en) * | 2019-07-17 | 2022-03-15 | Huawei Technologies Co., Ltd. | Method and apparatus for determining vehicle speed |
Also Published As
Publication number | Publication date |
---|---|
RU2714056C2 (ru) | 2020-02-11 |
EP3385930A4 (en) | 2019-01-02 |
CA3006546A1 (en) | 2017-06-08 |
EP3385930A1 (en) | 2018-10-10 |
JPWO2017094656A1 (ja) | 2018-09-06 |
RU2018122451A3 (ja) | 2020-01-10 |
KR101970931B1 (ko) | 2019-04-19 |
MX2018006120A (es) | 2018-08-01 |
BR112018010601A2 (ja) | 2018-11-13 |
US10152882B2 (en) | 2018-12-11 |
CN108292475A (zh) | 2018-07-17 |
RU2018122451A (ru) | 2020-01-10 |
JP6540826B2 (ja) | 2019-07-10 |
EP3385930B1 (en) | 2020-03-04 |
KR20180072801A (ko) | 2018-06-29 |
MX368090B (es) | 2019-09-19 |
CN108292475B (zh) | 2021-11-02 |
US20170154529A1 (en) | 2017-06-01 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
WO2017094656A1 (ja) | 車両道路網の走行に用いられる予測車両情報の生成方法及び装置 | |
CN112368662B (zh) | 用于自主运载工具操作管理的定向调整动作 | |
CN110431037B (zh) | 包括运用部分可观察马尔可夫决策过程模型示例的自主车辆操作管理 | |
CN110325928B (zh) | 自主车辆运行管理 | |
CN111492202B (zh) | 车辆运行的位置确定 | |
EP3580104B1 (en) | Autonomous vehicle operational management blocking monitoring | |
JP6963158B2 (ja) | 集中型共有自律走行車動作管理 | |
KR20190107169A (ko) | 자율주행 차량 운용 관리 제어 | |
CN112868031B (zh) | 具有视觉显著性感知控制的自主运载工具操作管理 | |
CN114730188B (zh) | 自主运载工具的安全性保证远程驾驶 | |
US11753009B2 (en) | Intelligent pedal lane change assist | |
US20230245564A1 (en) | System and Method for Intersection Collision Avoidance | |
US11215985B2 (en) | Pathfinding assistance system for teleoperation | |
US10037698B2 (en) | Operation of a vehicle while suppressing fluctuating warnings | |
CN116907520A (zh) | 用于运载工具的方法和系统以及存储介质 |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 16870593 Country of ref document: EP Kind code of ref document: A1 |
|
WWE | Wipo information: entry into national phase |
Ref document number: MX/A/2018/006120 Country of ref document: MX |
|
ENP | Entry into the national phase |
Ref document number: 2017553835 Country of ref document: JP Kind code of ref document: A |
|
ENP | Entry into the national phase |
Ref document number: 20187014643 Country of ref document: KR Kind code of ref document: A |
|
ENP | Entry into the national phase |
Ref document number: 3006546 Country of ref document: CA |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
REG | Reference to national code |
Ref country code: BR Ref legal event code: B01A Ref document number: 112018010601 Country of ref document: BR |
|
WWE | Wipo information: entry into national phase |
Ref document number: 2018122451 Country of ref document: RU Ref document number: 2016870593 Country of ref document: EP |
|
ENP | Entry into the national phase |
Ref document number: 2016870593 Country of ref document: EP Effective date: 20180702 |
|
ENP | Entry into the national phase |
Ref document number: 112018010601 Country of ref document: BR Kind code of ref document: A2 Effective date: 20180524 |