WO2018132378A2 - Systèmes et procédés pour véhicules automatisés connectés sur autoroute - Google Patents

Systèmes et procédés pour véhicules automatisés connectés sur autoroute Download PDF

Info

Publication number
WO2018132378A2
WO2018132378A2 PCT/US2018/012961 US2018012961W WO2018132378A2 WO 2018132378 A2 WO2018132378 A2 WO 2018132378A2 US 2018012961 W US2018012961 W US 2018012961W WO 2018132378 A2 WO2018132378 A2 WO 2018132378A2
Authority
WO
WIPO (PCT)
Prior art keywords
traffic
information
vehicle
vehicles
data
Prior art date
Application number
PCT/US2018/012961
Other languages
English (en)
Other versions
WO2018132378A3 (fr
Inventor
Bin Ran
Yang Cheng
Shen Li
Fan DING
Jing Jin
Xiaoxuan CHEN
Zhen Zhang
Original Assignee
Cavh Llc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from CN201710014787.0A external-priority patent/CN106710203A/zh
Priority claimed from US15/628,331 external-priority patent/US10380886B2/en
Application filed by Cavh Llc filed Critical Cavh Llc
Priority to KR1020197023379A priority Critical patent/KR102386960B1/ko
Priority to AU2018208404A priority patent/AU2018208404B2/en
Priority to CA3049019A priority patent/CA3049019A1/fr
Priority to EP18738971.3A priority patent/EP3568843A4/fr
Priority to JP2019557535A priority patent/JP6994203B2/ja
Publication of WO2018132378A2 publication Critical patent/WO2018132378A2/fr
Publication of WO2018132378A3 publication Critical patent/WO2018132378A3/fr
Priority to JP2021195103A priority patent/JP7207670B2/ja

Links

Classifications

    • 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
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0287—Control of position or course in two dimensions specially adapted to land vehicles involving a plurality of land vehicles, e.g. fleet or convoy travelling
    • G05D1/0291—Fleet control
    • 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
    • G08G1/0116—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from roadside infrastructure, e.g. beacons
    • 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
    • G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
    • 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
    • G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/07—Controlling traffic signals
    • G08G1/08—Controlling traffic signals according to detected number or speed of vehicles
    • 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
    • G08G1/096733—Systems involving transmission of highway information, e.g. weather, speed limits where a selection of the information might take place
    • G08G1/096741—Systems involving transmission of highway information, e.g. weather, speed limits where a selection of the information might take place where the source of the transmitted information selects which information to transmit to each vehicle
    • 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
    • G08G1/096733—Systems involving transmission of highway information, e.g. weather, speed limits where a selection of the information might take place
    • G08G1/09675—Systems involving transmission of highway information, e.g. weather, speed limits where a selection of the information might take place where a selection from the received information takes place in the vehicle
    • 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

Definitions

  • the present invention relates generally to a comprehensive system providing full vehicle operations and control for connected and automated vehicles (CAV), and, more particularly, to a system controlling CAVs by sending individual vehicles with detailed and time-sensitive control instructions for vehicle following, lane changing, route guidance, and related information.
  • CAV connected and automated vehicles
  • the present invention provides a comprehensive system providing full vehicle operations and control for connected and automated vehicle and highway systems by sending individual vehicles with detailed and time-sensitive control instructions. It is suitable for a portion of lanes, or all lanes of the highway. Those instructions are vehicle specific and they are sent by lowest level traffic control units (TCUs), which are optimized and passed from top level traffic control centers (TCCs). These TCC/TCUs are in a hierarchical structure and cover different levels of areas.
  • TCUs lowest level traffic control units
  • TCCs top level traffic control centers
  • the systems and methods provide a transportation management system, or use thereof, that provides full vehicle operations and control for connected and automated vehicle and highway systems by sending individual vehicles with detailed and time-sensitive control instructions for one or more or all of vehicle following, lane changing, route guidance, and related information.
  • the systems and methods comprise one or more or all of: a) a hierarchy of traffic control centers/units (TCCs/TCUs), that process information and give traffic operations instructions, wherein said TCCs and TCUs are automatic or semi- automated computational modules that focus on data gathering, information processing, network optimization, and traffic control; b) a network of Road Side Units (RSUs), that receive data flow from connected vehicles, detect traffic conditions, and send targeted instructions to vehicles, wherein, in some embodiments, said RSU network focuses on data sensing, data processing, control signal delivery, and information distribution, and point or segment TCUs can be combined or integrated with a RSU; c) a vehicle sub-system housed on one or more vehicles, collectively comprising, for example, a mixed traffic flow of vehicles at different levels of connectivity and automation; and d) communication systems, that provide wired and wireless communication services to one or more or all the entities in the system.
  • TCCs/TCUs traffic control centers/units
  • RSUs Road Side Units
  • an autonomous vehicle control system housed in a vehicle may comprise one or more or all of: a) a communication link with a hierarchy of traffic control centers/units (TCCs/TCUs), which process information and give traffic operations instructions, wherein said TCCs and TCUs are automatic or semi-automated computational modules that focus on data gathering, information processing, network optimization, and traffic control; b) a communication link with network of Road Side Units (RSUs), which receive data flow from connected vehicles, detect traffic conditions, and send targeted instructions to vehicles, wherein said RSU network focuses on data sensing, data processing, control signal delivery, and information distribution, and said point or segment TCU can be combined or integrated with a RSU; and a vehicle sub-system, configured to receive detailed and time-sensitive control instructions for
  • information is customized for each individual vehicle served by the system; said information including one or more or all of: weather, pavement conditions, and estimated travel time; and said information including vehicle control instructions including one or more or all of speed, spacing, lane designation, and routing.
  • information is sent from an upper level TCC/TCU to a lower level TCC/TCUs, and includes one or more or all of: a desirable speed, a desirable spacing of vehicles, a desirable traffic volume, a desirable traffic split at access points, and traffic signal timing parameters.
  • the system employs hardware comprising one or more or all of: a power supply, traffic sensors, wired and wireless communication modules, and a data storage device and database.
  • the systems and methods are configured for use with a sensor selected from the group consisting of: a microwave system; an inductive loop system; an inferred system; a video camera system; and a laser system.
  • the systems and methods comprise a hierarchy of Traffic Control Centers/Units (TCCs/TCUs) comprising one or more of: Macroscopic TCCs, that process information from regional TCCs and provide control targets to regional TCCs; Regional TCCs, that process information from macroscopic and corridor TCCs and provide control targets to corridor TCCs; Corridor TCCs, that process information from the regional TCC and segment TCUs and provide control targets to segment TCUs; Segment TCUs, that process information from the corridor TCC and point TCUs and provide control targets to point TCUs; and Point TCUs, that process information from the segment TCU and RSUs and provide vehicle-based control instructions to RSUs.
  • TCCs/TCUs Traffic Control Centers/Units
  • the Macroscopic TCC provides control target to Regional TCCs; collects related data from regional TCCs; archives historical data in a data center, to support information processing and a strategy optimizer; provides an automatic or semi-automated computational center that focuses on data gathering, information processing, network optimization, and traffic control signals; and controls multiple regional TCCs in a large scale area and communicates with regional TCCs using high volume capacity and low latency communication media, such as optical fiber.
  • the Regional TCC provides control target to corridor TCCs; collects related data from corridor TCCs; archives historical data in a data center, to support the information processing and a strategy optimizer; provides an automatic or semi-automated computational center that focuses on data gathering, information processing, network optimization, and traffic control signals for a region such as a city; and controls multiple Corridor TCCs within its coverage,
  • the Corridor TCC provides control target to segment TCUs; collects related data from segment TCUs; provides optimizer and processor modules to process information and provide control targets; provides an automatic or semi-automated computational center that focuses on data gathering, information processing, network optimization, and traffic control signals for a long roadway corridor, such as a 10-mile long freeway stretch plus local road in the vicinity; and contains a calculation server, a data warehouse, and data transfer units, with image computing ability calculating the data collected from road controllers, and controls Segment TCUs within its coverage, wherein traffic control algorithms are used to control Point TCUs (e.g. adaptive predictive traffic control algorithm), a Corridor TCC communicates with segment TCUs and its upper Regional TCC using high volume capacity and low latency communication media, such as optical fiber, and said corridor TCC covers 5-20 miles (or longer or shorter distances).
  • a Corridor TCC communicates with segment TCUs and its upper Regional TCC using high volume capacity and low latency communication media, such as optical fiber, and said corridor TCC covers
  • the Segment TCU provides control target to point TCUs; collects related data from point TCUs; provides optimizer and processor modules to process information and provide control targets; provides a smaller traffic control unit covering a small roadway area, and covers a road segment about 1 to 2 miles (or longer or shorter distances); and contains LAN data switching system (e.g., Cisco Nexus 7000) and an engineer server (e.g. IBM engineer server Model 8203 and ORACL data base), and communicates with Point TCUs either by wired or wireless communication media.
  • LAN data switching system e.g., Cisco Nexus 7000
  • an engineer server e.g. IBM engineer server Model 8203 and ORACL data base
  • the Point TCU provides vehicle based control instructions to RSUs; collects related data from point RSUs; provides optimizer and processor modules to process information and provide control targets; and provides a smaller traffic control unit covering a short distance of a roadway (e.g., 50 meters), ramp metering, or intersections, which are installed for every ramp or intersection; and is connected with a number of RSU units, e.g., ten units (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 20, etc.).
  • the RSUs comprise one or more or all of: a sensing module that gathers traffic and related information; a data processing module that provides vehicle-specific measurements, including but not limited to speed, headway, acceleration / deceleration rate, the distance between carriageway markings and vehicles, angle of vehicles and central lines, and overall traffic status; a sensing module that gathers traffic and related information; a data processing module that provides vehicle-specific measurements, including but not limited to speed, headway, acceleration / deceleration rate, the distance between carriageway markings and vehicles, angle of vehicles and central lines, and overall traffic status; a
  • communication module that sends information between vehicles and upper level point TCU; a communication module that sends vehicle-specific driving instructions to vehicles; an interface module that shows data that is sent to an OBU system; and a power supply unit.
  • a vehicle sub-system comprises one or more modules for: a) vehicle-control; b) traffic detection and data collection; c) wireless
  • the system is configured to redistribute essential vehicle driving tasks among vehicles comprising one or more or all of: providing instructions needed for navigation tasks to the vehicles; providing instructions and information for guidance tasks of: safety maintenance, traffic control/road condition, and special information; fulfilling vehicle maneuver tasks, and monitoring safety maintenance tasks, to take over if the system fails; providing data feeds for information exchange tasks at the control level, which is usually provided by the vehicle sensors in a vehicle; fulfilling vehicle control tasks, at the mechanic level, and monitoring surroundings, and standing-by as a backup system; providing vehicles with driving-critical information, some of which are difficult and expensive for vehicle-based sensors to obtain in a constantly reliable way; and fulfilling driving tasks and using each other as the backup in case of any errors or failures.
  • the systems and methods comprise an in-vehicle interface selected from the group consisting of: audio: Voice control and Text-to- Voice; vision: Head-up-display (HUD); and vibration.
  • the vehicle identification and tracking functions operate on any or any combination of: CV security certificate; on Board Unit (OBU) ID; mobile device ID; DGPS (differential GPS); vision sensors in combination with video recognition and object detection; and mobile LiDAR sensors.
  • the systems and methods employ one or more communication systems selected from the group consisting of: OEM operators, such as OnStar; wireless communication service providers, such as ATT and Verizon; and public agencies who maintain the system, such as a DOT who owns optic fiber networks.
  • OEM operators such as OnStar
  • wireless communication service providers such as ATT and Verizon
  • public agencies who maintain the system, such as a DOT who owns optic fiber networks.
  • the systems and method employ a communication technology selected from the group consisting of: wireless communication
  • Ethernet optical communication technologies
  • multi-dimensional connected and automated vehicle-highway systems comprising hardware and software, said system comprising three dimensions: Dimension 1 (Dl): vehicle automation of connected and automated vehicles; Dimension 2 (D2): connectivity of communication among humans, vehicles, and traffic environments; and Dimension 3 (D3):
  • Dl comprises one or more capabilities of: a) driver assistance employing a driving mode-specific execution by a driver assistance system of either steering or acceleration/deceleration using information about a driving environment and with an expectation that a human driver perform all remaining aspects of a dynamic driving task; b) partial automation employing a driving mode- specific execution by one or more driver assistance system of both steering and acceleration/deceleration using information about the driving environment and with an expectation that the human driver perform all remaining aspects of the dynamic driving task; c) conditional automation employing driving mode-specific performance by an automated driving system of all aspects of the dynamic driving task with an expectation that the human driver will respond appropriately to a request to intervene; d) high automation employing driving mode-specific performance by an automated driving system of all aspects of the dynamic driving task, even if the human driver does not respond appropriately to the request to intervene; and e) full automation employing full-time performance by an automated driving system of all aspects of the dynamic driving task under all roadway and environmental conditions that can be managed by a human driver.
  • D2 comprises one or more capabilities of: a) information assistance, wherein a human driver receives simple traffic condition information from roadside units to assist driving and decision making; b) limited connected sensing, wherein the human driver and vehicle can access information via onboard unit and roadside units to better assist driving and decision making compared with the information assistance of a); c) redundant information sharing, wherein the human driver and vehicle can access multiple layers of information via on-board unit, roadside units, Traffic Operation Center (TOC), and vehicles, wherein vehicles are operated through various controlling strategies and methods, including human driving, vehicle automated driving, and TOC controlled driving; d) optimized connectivity, wherein information on the transportation network is not overloaded and redundant and wherein optimized information with reduced redundancy is provided to drivers and vehicles to facilitate optimized and safe driving.
  • TOC Traffic Operation Center
  • D3 comprises one or more capabilities of: a) key point system integration, wherein connected vehicles exchange information with roadside units at traffic key points (e.g., road intersections), obtain vehicle control instructions and other information to address local issues and keep smooth and safe traffic movement; b) segment system integration, wherein connected vehicles receive specific control instructions and information from a microscopic TOC to manage and control traffic of a specific road segment; c) corridor system integration, wherein connected vehicles receive navigation instructions from a macroscopic TOC (e.g., that manages citywide or s nationwide traffic) that controls the traffic volume, predicts traffic congestions, and proposes to the macroscopic TOC for global optimization; and d) macroscopic system integration, wherein a macroscopic TOC optimizes traffic distractions from a highest level to increase traffic efficiency, lower traffic costs of people and goods, and realize global optimization for a whole network.
  • key point system integration wherein connected vehicles exchange information with roadside units at traffic key points (e.g., road intersections), obtain vehicle control instructions and other information to address local
  • levels of system integration, automation, and connectivity comprise: 1) Vehicle Automation Level, which uses the SAE definition; 2) Connectivity Level, which is defined based on information volume and content: (e.g., CO: No Connectivity: both vehicles and drivers do not have access to any traffic information; CI : Information assistance: vehicles and drivers can only access simple traffic information from the Internet, such as aggregated link traffic states, and information is of certain accuracy, resolution, and of noticeable delay; C2: Limited connected sensing:
  • Redundant Information Sharing vehicles and drivers can connect with neighboring vehicles, traffic control device, live traffic condition map, and high-resolution infrastructure map (information is with adequate accuracy and almost in real time, complete but redundant from multiple sources); and C4: Optimized connectivity: optimized information is provided and smart infrastructure can provide vehicles with optimized information feed); and 3) Transportation System Integration Level, which is defined by the levels of system coordination/optimization (e.g., SO: No integration; SI : Key point system integration, covering a small area such as intersections, ramp metering, and only for the major travel mode; S2: Segment system integration, covering a short road segment such as a freeway segment between two ramp access points, and for most of the travel modes; S3 : corridor system integration, covering a corridor with connecting roads and ramps, and for all coexisting traffic modes; S4: Regional system integration, covering a city or urban area; and S5 : Macroscopic system integration, covering several regions and inter-regional traffic.
  • SO No integration
  • SI Key point system integration, covering a small area
  • methods employing any of the systems described herein for the management of one or more aspects of traffic control.
  • the methods include those processes undertaken by individual participants in the system (e.g., drivers, public or private local, regional, or national transportation facilitators, government agencies, etc.) as well as collective activities of one or more participants working in coordination or independently from each other.
  • FIG. 1 presents an exemplary system overview.
  • FIG. 2 presents an exemplary definition of a 3D CAVH (Connected
  • FIG. 3 illustrates an exemplary redistribution of driving tasks
  • FIG. 4 provides a distribution of driving tasks for a typical AV (Automated Vehicle) based system
  • FIG. 5 illustrates an exemplary distribution of driving tasks in an embodiment of the technology provided herein
  • FIG. 6 illustrates exemplary system components
  • FIG. 7 illustrates an exemplary TCU (Traffic Control Unit) subsystem
  • FIG. 8 illustrates an exemplary RSU (Road Side Unit) subsystem
  • FIG. 9 illustrates exemplary vehicle subsystem data flow
  • FIG. 10 illustrates an exemplary communication subsystem
  • FIG. 11 illustrates an exemplary point TCU
  • FIG. 12 illustrates an exemplary segment TCU
  • FIG. 13 illustrates an exemplary corridor TCC
  • FIG. 14 illustrates an exemplary regional TCC
  • FIG. 15 illustrates an exemplary macroscopic TCC (Traffic Control Center).
  • FIG. 16 illustrates an exemplary vehicle entering control
  • FIG. 17 illustrates an exemplary vehicle exit control.
  • FIG. 18 illustrates an exemplary RSU Module Design.
  • FIG. 19 illustrates distance between carriageway markings and vehicles.
  • FIG. 20 illustrates angle of vehicles and road central lines.
  • FIG. 21 illustrates an exemplary overall traffic state.
  • FIG. 22 illustrates installation angle of microwave radar.
  • FIG. 23 illustrates an exemplary OBU module design.
  • FIG. 24 illustrates an exemplary TCC/ TCU structure map.
  • FIG. 25 presents an exemplary definition of a 3D CAVH (Connected
  • TCC&TCU subsystem A hierarchy of traffic control centers (TCCs) and traffic control units (TCUs), which process information and give traffic
  • TCCs are automatic or semi-automated computational centers that focus on data gathering, information processing, network optimization, and traffic control signals for regions that are larger than a short road segment.
  • TCUs also referred to as point TCU
  • TCUs are smaller traffic control units with similar functions, but covering a small freeway area, ramp metering, or intersections.
  • RSU subsystem A network of Roadside Units (RSUs), which receive data flow from connected vehicles, detect traffic conditions, and send targeted instructions to vehicles.
  • RSU network focuses on data sensing, data processing, and control signal delivery. Physically, e.g. a point TCU or segment TCC can be combined or integrated with a RSU.
  • vehicle subsystem The vehicle subsystem, comprising a mixed traffic flow of vehicles at different levels of connectivity and automation.
  • Communication subsystem A system that provides wired / wireless communication services to some or all the entities in the systems.
  • Traffic data flow Data flow contains traffic condition and vehicle requests from the RSU subsystem to TCC & TCU subsystem, and processed by TCC & TCU subsystem.
  • Control instructions set flow Control instructions set calculated by TCC & TCU subsystem, which contains vehicle-based control instructions of certain scales. The control instructions set is sent to each targeted RSU in the RSU subsystem according to the RSU's location.
  • Vehicle data flow Vehicle state data and requests from vehicle subsystem to RSU subsystem.
  • Vehicle control instruction flow Flow contains different control instructions to each vehicle (e.g. advised speed, guidance info) in the vehicle subsystem by RSU subsystem.
  • Macroscopic Traffic Control Center Automatic or semi-automated computational center covering several regions and inter-regional traffic control that focus on data gathering, information processing, and large-scale network traffic optimization.
  • Regional Traffic Control Center Automatic or semi-automated computational center covering a city or urban area traffic control that focus on data gathering, information processing, urban network traffic and traffic control signals optimization.
  • Corridor Traffic Control Center Automatic or semi-automated computational center covering a corridor with connecting roads and ramps traffic control that focus on corridor data gathering, processing, traffic entering and exiting control, and dynamic traffic guidance on freeway.
  • Segment Traffic Control Unit Automatic or semi-automated computational center covering a short road segment Traffic control that focus on segment data gathering, processing and local traffic control.
  • Point Traffic Control Unit covering a small freeway area, ramp metering, or intersections that focus on data gathering, traffic signals control, and vehicle requests processing.
  • Road Side Unit receive data flow from connected vehicles, detect traffic conditions, and send targeted instructions to vehicles.
  • the RSU network focuses on data sensing, data processing, and control signal delivery.
  • Vehicle subsystem comprising a mixed traffic flow of vehicles at different levels of connectivity and automation.
  • 401 Macro control target, neighbor Regional TCC information.
  • Vehicle type info (including vehicle max speed, acceleration, and
  • Vehicle OBU info Software information, Hardware information
  • the OBU hardware contains DSRC radio communication (or other communication technology) capability as well as Global Positioning System technology as compared with the RSU, which only needs to have DSRC radio communication (or other communication technology) capability.
  • Timestamp 2. Vehicle lateral/longitudinal position;
  • Vehicle OD information (including origin information, destination information, route choice information);
  • CSP Communication Service Provider
  • 802 Processor: Processing the data received from RSUs.
  • the system contains TCC/TCU subsystem 101, RSU subsystem 102, vehicle subsystem 103, and communication subsystem 104.
  • TCC/TCU subsystem 101 is a hierarchical traffic control network of Traffic Control Centers (TCCs) and local traffic controller units (TCUs), which process traffic information from RSU subsystem 102 and give traffic operation instructions to RSU subsystem 102.
  • RSU subsystem 102 is a network of Roadside Units, which process traffic detection, communication, control instructions, and emissions.
  • Vehicle subsystem 103 is a mixed traffic flow of vehicles at different levels of connectivity and automation, which send static, dynamic information and requests of vehicles to RSU subsystem 102, and receive instructions from RSU subsystem.
  • RSU subsystem 102 transfers vehicle data and requests from vehicle subsystem 103 into traffic information, and sends it to TCC/TCU subsystem 101 by communication system 104.
  • TCC/TCU subsystem 101 processes the information in the proper layer and sends operation instructions back to RSU subsystem 102.
  • RSU subsystem 102 screens and catalogues the operation instructions and sends the instructions 108 to each vehicle (e.g. advised speed, guidance information).
  • Communication subsystem 104 is a wireless communication and security system with local and global connectivity, providing wired and wireless communication services to all the entities in the systems.
  • the attributes of such a system regarding levels of system integration, automation, and connectivity, is defined as:
  • Vehicle Automation Level uses the SAE definition.
  • Connectivity Level is defined based on information volume and content:
  • Vehicles and drivers can only access simple traffic information from the Internet, such as aggregated link traffic states.
  • Information is of certain accuracy, resolution, and of noticeable delay.
  • Vehicles and drivers can access live traffic information of high accuracy and unnoticeable delay, through connection with RSUs, neighbor vehicles, and other information providers. However, the information may not be complete.
  • Vehicles and drivers can connect with neighbor vehicles, traffic control device, live traffic condition map, and high-resolution infrastructure map. Information is with adequate accuracy and almost in real time, complete but redundant from multiple sources.
  • SI Key Point System Integration (e.g., RSU based control for
  • S2 Segment System Integration (e.g., optimizing traffic on
  • Scope becomes larger and more RSUs and vehicles are involved in the coordination and optimization.
  • the traffic modes will remain the same.
  • any of the embodiments described herein may be configured to operate with one or more of the Connectivity Levels in each
  • a three-dimensional connected and automated vehicle-highway system see e.g., Fig. 25.
  • the exemplary system in Fig. 25 includes three dimensions: Dimension 1 (Dl): vehicle automation, defines the development stages of connected and automated vehicles, adopting the SAE vehicle automation definition (e.g., driver assistance, partial automation, conditional automation, high automation, and full automation).
  • Dimension 2 (D2): connectivity defines the development stages of communication technologies, is about the communication among human, vehicles, and the traffic environment (e.g., information assistance, limited connected sensing, redundant information sharing, and optimized connectivity).
  • Dimension 3 transportation system integration, defines the development stages of transportation system (e.g., key point system integration, segment system integration, corridor system integration, and macroscopic level system integration). This system provides a comprehensive system for the connected and automated vehicles and highways, by integrating, coordinating, controlling, managing, and optimizing all related vehicles, information services, facilities, and systems.
  • FIG. 3 shows (1) all the driving tasks among the originally defined three broad levels of performance: "Control”, “Guidance”, and “Navigation”, according to the original definition of driving task by Lunenfeld and Alexander in 1990 (A User's Guide to Positive Guidance (3rd Edition) FHWA SA-90-017, Federal Highway Administration, Washington, DC). Those driving tasks are essential for all vehicles to drive safely from origins to destinations, and (2) how those tasks are distributed into and covered by the Vehicle Subsystem 103 and TCC/TCU 101+ RSU 102
  • the TCC/TCU 101+ RSU 102 subsystems provide the instructions to the vehicles, including the "Pre-trip information" and “Route planning" needed for vehicles.
  • the TCC/TCU 101+ RSU 102 subsystems provide the instructions and information for the Guidance tasks: Traffic Control/Road Condition, and Special Information.
  • the Vehicle subsystem 103 fulfills the Vehicle Maneuver tasks, and monitors the Safety Maintenance tasks in addition to the operation of the TCC/TCU 101+ RSU 102.
  • the TCC/TCU 101+ RSU 102 subsystems provide data needs for the Information
  • the vehicle subsystem 103 fulfills Vehicle Control tasks, at the mechanic level, and monitors the surroundings, standing-by as the backup system.
  • FIG. 4 shows the driving tasks distribution for the typical traditional
  • V2I Automated Vehicle
  • DSRC Dedicated Short Range Communications
  • FIG. 5 shows the driving tasks distribution of embodiments of the present system.
  • the Vehicle Subsystem 103 together with the TCC /TCU 101 and RSU 102 Subsystem, takes over all the driving tasks among the three performance levels.
  • the sensing and communication technology is used both by Vehicle Subsystem 103 and the TCC /TCU 101 and RSU 102 Subsystem to support the present system.
  • the sensing serves in the level of both "Control” and "Guidance" while the
  • the Fully-Controlled Connected Automated Vehicle Highway System contains components listed as follows: The Macroscopic Traffic Control Center (Marco TCC) 301, which is automatic or semi-automated
  • the Regional Traffic Control Center (Regional TCC) 302 which is automatic or semi-automated computational center covering a city or urban area traffic control that focus on data gathering, information processing, urban network traffic control optimization.
  • the Corridor Traffic Control Center (Corridor TCC) 303 which is automatic or semi-automated computational center covering a corridor with connecting roads and ramps traffic control that focus on corridor data gathering, processing, traffic entering and exiting control, and dynamic traffic guidance on freeway.
  • the Segment Traffic Control Unit (Segment TCU) 304 which is a local automatic or semi-automated control unit covering a short road segment traffic control that focus on segment data gathering, processing and local traffic control.
  • Point Traffic Control Unit (Point TCU) 305 which is an automatic control unit covering a small freeway area, ramp metering, or intersections that focus on data gathering, traffic signals control, and vehicle requests processing.
  • the Marco TCC 301, Regional TCC 302, Corridor TCC 303, Segment TCU 304 and Point TCU 305 are the components of TCC/TCU subsystem 101.
  • the Road Side Units (RSU 306) which represents small control units that receive data and requests from connected vehicles, detect traffic state, and send instructions to targeted vehicles.
  • the network comprising RSUs 306 is the RSU subsystem 303, which focuses on data sensing, data processing, and control signal delivery.
  • the connected and automated vehicles 307 is the basic element of vehicle subsystem 304, including vehicles at different levels of connectivity and automation.
  • OBU On-Board Unit with sensor and V2I
  • the top level macroscopic traffic control center (TCC) 301 sends control target such as regional traffic control and boundary information 401 to second level regional TCC 302.
  • regional TCC 302 sends refined traffic conditions 402 such as congestion condition back to macroscopic TCC 301, which helps macroscopic TCC 301 to deal with large-scale network traffic
  • Regional TCC 302 sends control target and boundary information 403 to corridor TCC 303 and receives refined traffic condition 404.
  • Corridor TCC 303 sends control target and boundary information 405 to segment traffic control unit (TCU) 304 and receives refined traffic condition 406.
  • Segment TCU 304 sends control target and boundary information 407 to point TCUs 305 and receives point TCUs' 305 refined traffic conditions 408.
  • Road side unit group 306 receives data from CAV and Non-CAV and detects traffic conditions. Then, Road side unit group 306 sends data to point traffic control unit 305. After receiving all data from the Road side unit group 306 that is located in the covering area, point traffic control unit 305 optimizes traffic control strategy for all area and sends targeted instructions to Road side unit group 306.
  • road side unit group 306 receives data from connected vehicles 307, detects traffic conditions, and sends targeted instructions to vehicles 307.
  • the RSU network focuses on data sensing, data processing, and control signal delivering. Information is also shared by different vehicles 307 that have communication with each other. Vehicles 307 also is a subsystem that can comprise a mixed traffic flow of vehicles at different levels of connectivity and automation.
  • Department of Transportation 701 controls the communication information between traffic control centers (TCC) and traffic control units (TCU).
  • TCC traffic control centers
  • TCU traffic control units
  • RSU roadside units
  • the communication service provider 702 also controls data between roadside units and connected automated vehicle(CAV).
  • CAV connected automated vehicle
  • RSU 306 collects traffic data from highway and passes the traffic information 502 to optimizer 801 and processor 802. After receiving data, processor 802 processes it and generates current traffic conditions 408, which is delivered to Segment TCU 304. Segment TCU 304 decides the control target 407 to be controlled and informs optimizer 801 about it. Optimizer 801 optimizes the plan based on traffic information 502 and control target 407 and returns the vehicle-based control instructions 501 to RSU 306.
  • Point TCU 305 generates current traffic conditions 408 and passes them to optimizer 801 and processor 802.
  • processor 802 processes it and generates current segment traffic conditions 406, which is delivered to Corridor TCC 303.
  • Corridor TCC 303 decides the control target 405 to be controlled and informs optimizer 801 about it.
  • Optimizer 801 optimizes the plan based on traffic conditions 408 and control target 405 and returns control target 407 for Point TCU 305.
  • Segment TCU 304 generates current segment traffic conditions 406 and passes them to optimizer 801 and processor 802. After receiving the condition information, processor 802 processes it and generates current corridor traffic conditions 404, which is delivered to Regional TCC 302. Regional TCC 302 decides the control target 403 to be controlled and informs optimizer 801 about it. Optimizer 801 optimizes the plan based on segment traffic conditions 406 and control target 403 and returns control target 405 for Segment TCU 304.
  • FIG. 14 shows the data and decision flow of Regional TCC 302.
  • Corridor TCC 303 collectively sends all the traffic data to the Regional TCC 302. After the data is received by the data center, all the data is processed by the information processor.
  • the information processor integrates traffic data and sends it to the control center.
  • the control center makes draft-decision by a preset algorithm and sends the result to strategy optimizer.
  • the optimizer simulates the decision and optimizes it and sends it to both Corridor TCC 303 and Macro TCC 301.
  • Macro TCC 301 shares traffic data from other Regional TCCs 302 nearby and system optimized decision back to the Regional TCC 302.
  • each Regional TCC 302 sends the traffic data and local optimized strategy to the Macro TCC 301.
  • An information processor integrates all optimized strategies and traffic data. After that, the control center makes a draft- decision based on the traffic data from Regional TCCs 303. The draft-decision is then processed by the strategy optimizer. A final system-optimized decision is made and sent back to the Regional TCCs 303.
  • FIG. 16 illustrates the process of vehicles 307 entering the fully-controlled system.
  • vehicles 307 send the entering requests to RSUs 306 after arriving at the boundary area of the system.
  • the boundary area refers to the area around the margin of a Segment TCU's 304 control range.
  • RSUs 306 provide the entering requests to Point TCUs 305 and detect the information of vehicles 307, including static and dynamic vehicle information 6.2, after Point TCUs 305 accept the entering requests.
  • Point TCUs 305 formulate the control instructions 6.1 (such as advised speed, entering time, entering position, etc.) for vehicles 307 to enter the fully-controlled system and attempt to take over the control of vehicles 307, based on the information detected by RSUs 306.
  • Vehicles 307 receive the control instructions 6.1 from RSUs 306 and process the instructions 6.1 with the inner subsystems to decide whether the instructions 6.1 can be confirmed. Vehicles 307 update and send the entering requests again if the control instructions 6.1 cannot be confirmed based on the judgment of the inner subsystems. Vehicles 307 drive following the control instructions 6.1 and enter the fully-control system if the control instructions 6.1 are confirmed. Point TCUs 305 take over the driving control of vehicles 307, and vehicles 307 keep driving based on the control instructions 6.1 provided from the fully - controlled system. Point TCUs 305 update the traffic condition and send the refined information 4.8 to the Segment TCU 304 after vehicles 307 enter the fully-controlled system. FIG.
  • FIG. 17 illustrates the process of vehicles 307 exiting the fully-controlled system.
  • vehicles 307 send the exiting requests to RSUs 306 after arriving at the boundary area of the system.
  • the boundary area refers to the area around the margin of a Segment TCU's 304 control range.
  • RSUs 306 provide the exiting requests to Point TCUs 305.
  • Point TCUs 305 formulate the exiting instructions 6.1 (such as advised speed, exiting time, exiting position, etc.) for vehicles 307 to exit the fully-controlled system based on the information detected by RSUs 306.
  • Vehicles 307 receive the exiting instructions 6.1 from RSUs 306 and process the instructions 6.1 with the inner subsystems to decide whether the instructions 6.1 can be confirmed.
  • Vehicles 307 update and send the entering requests again if the exiting instructions 6.1 can't be confirmed based on the judgment of the inner subsystems. Vehicles 307 drive following the exiting instructions 6.1 and exit the fully-control system if the exiting instructions 6.1 are confirmed. Point TCUs 305 terminate the driving control of vehicles 307, and vehicles 307 start the autonomous driving and follow their own drive strategies after conducting the exiting
  • Point TCUs 305 update the traffic condition and send the refined information 4.8 to the Segment TCU 304 after vehicles 307 exit the fully-controlled system.
  • a RSU has two primary functions: 1) communication with vehicles and point traffic control units (TCUs), and 2) collecting traffic and vehicle driving environmental information.
  • the sensing module (2) gathers information using various detectors described in detail in the following sections.
  • the data processing module (5) uses data fusion technology to obtain six major feature parameters, namely speed, headway, acceleration / deceleration rates, the distance between carriageway markings and vehicles, angle of vehicles and central lines, and overall traffic status. Meanwhile, the communication module (1) also sends information received from vehicles and point TCUs to the data processing module (5) to update the result of the module. After six feature parameters are generated, the communication module (1) sends driving instructions to the OBU system installed on an individual vehicle, and shares the information with point TCUs.
  • the interface module (4) will show the data that is sent to the OBU system.
  • the power supply unit (3) keeps the power to maintain the whole system working.
  • Exemplary on-market components that may be employed are:
  • Exemplary on-market components that may be employed are:
  • Exemplary on-market components that may be employed are: LIDAR in ArcGIS b. Camera
  • EyEQ4 from Mobileye http://www.mobileye.com/our-technology/
  • the Mobileye system has some basic functions: vehicle and pedestrian detection, traffic sign recognition, and lane markings identification (see e.g., barrier and guardrail detection, US20120105639A1, image processing system, EP2395472A1, and road vertical contour detection, US20130141580A1, each of which is herein incorporated reference in its entirety. See also US20170075195 Al and
  • Reinforcement Learning is a process of using rewards and punishments to help the machine learn how to negotiate the road with other drivers (e.g., Deep learning).
  • STJ1-3 Exemplary on-market components that may be employed are: STJ1-3 from
  • data fusion technology is used such as the product from DF Tech to obtain six feature parameters more accurately and efficiently, and to use a backup plan in case one type of detectors has functional problems.
  • the function of data processing module is to fuse data collected from multiple sensors to achieve the following goals.
  • Exemplary on-market components that may be employed are: External Object Calculating Module (EOCM) in Active safety systems of vehicle (Buick LaCrosse).
  • EOCM External Object Calculating Module
  • the EOCM system integrates data from different sources, including a megapixel front camera, all-new long-distance radars and sensors to ensure a faster and more precise decision-making process.
  • US8527139 Bl herein incorporated by reference in its entirety.
  • one RSU is installed every 50m along the connected automated highway for one direction.
  • the height is about 40 cm above the pavement.
  • a RSU should be perpendicular to the road during installation.
  • the installation angle of RSU is as shown in Fig. 22.
  • the communication module (1) is used to receive both information and command instruction from a RSU.
  • the data collection module (2) is used to monitor the operational state, and the vehicle control module (3) is used to execute control command.
  • Exemplary on-market components that may be employed are:
  • the data collection module is used to monitor the vehicle operation and diagnosis.
  • OBU TYPE A CAN BUS Analyzer
  • Exemplary on-market components that may be employed are:
  • Exemplary on-market components that may be employed are: CAN BUS
  • Exemplary on-market components that may be employed are: Toyota's remote controlled autonomous vehicle.
  • the captured data can be sent to a remote operator.
  • the remote operator can manually operate the vehicle remotely or issue commands to the autonomous vehicle to be executed by various vehicle systems. (See e.g., US9494935 B2, herein incorporated by reference in its entirety).
  • TCC/ TCU system is a hierarchy of traffic control centers (TCCs) and traffic control units (TCUs), which process information and give traffic operations instructions.
  • TCCs are automatic or semi -automated computational centers that focus on data gathering, information processing, network optimization, and traffic control signals for a region that is larger than short road segments.
  • TCUs are smaller traffic control units with similar functions, but covering a small freeway area, ramp metering, or intersections.
  • a point TCU collects and exchanges data from several RSUs.
  • a segment TCC collects data and exchanges data from multiple Point TCUs, optimizes the traffic flow, and controls Point TCU to provide control signal for vehicles.
  • a Corridor TCC collects data from multiple RSUs and optimizes the traffic in a corridor.
  • a Regional TCC collects data from multiple corridors and optimizes traffic flow and travel demand in a large area (e.g. a city is covered by one regional TCC).
  • a Macro TCC collects data from multiple Regional TCCs and optimizes the travel demand in a large-scale area.
  • Point TCU For each Point TCU, the data is collected from a RSU system (1).
  • a Point TCU (14) e.g. ATC -Model 2070L
  • a thunderstorm protection device protects the RSU and Road Controller system. The RSU unites are equipped at the road side.
  • a Point TCU (14) communicates with RSUs using wire cable (optical fiber). Point TCUs are equipped at the roadside, which are protected by the Thunderstorm protector (2). Each point TCU (14) is connected with 4 RSU unites.
  • a Point TCU contains the engineering server and data switching system (e.g. Cisco Nexus 7000). It uses data flow software.
  • Each Segment TCU (11) contains a LAN data switching system (e.g. Cisco Nexus 7000) and an engineering server (e.g. IBM engineering server Model 8203 and ORACL data base).
  • the Segment TCU communicates with the Point TCU using wired cable.
  • Each Segment TCU covers the area along 1 to 2 miles.
  • the Corridor TCC contains a calculation server, a data warehouse, and data transfer units, with image computing ability calculating the data collected from road controller (14).
  • the Corridor TCC controls segment TCU (e.g., the Corridor TCC covers a highway to city street and transition). Traffic control algorithms are used to control segment and point TCUs (e.g., adaptive predictive traffic control algorithm).
  • the data warehouse is a database, which is the backup of the corridor TCC (15).
  • the Corridor TCC (15) communicates with segment TCU (11) using wired cord.
  • the calculation work station (KZTs-Ml) calculates the data from segment TCU (15) and transfers the calculated data to Segment TCU (11). Each corridor TCC covers 5-20 miles.
  • Regional TCC (12). Each regional TCC (12) controls multiple Corridor TCCs in a region (e.g. covers the region of a city) (15). Regional TCCs communicate with corridor TCCs using wire cable (e.g. optical fiber). Macro TCC (13). Each Macro TCC (13) controls multiple regional TCCs in a large-scale area (e.g., each state will have one or two Macro TCCs) (12). Macro TCCs communicate with regional TCCs using wire cable (e.g. optical fiber).
  • wire cable e.g. optical fiber
  • Map error is less than 10 cm with 99% confidence.
  • Exemplary on-market components that may be employed are: A. HERE https://here.com/en/products-services/products/here-hd-live-map
  • the HD maps of HERE allow highly automated vehicles to precisely localize themselves on the road.
  • the autonomous highway system employs maps that can tell them where the curb is within a few centimeters.
  • the maps also are live and are updated second by second with information about accidents, traffic backups, and lane closures.
  • Exemplary on-market components that may be employed are:
  • a software module is implemented with a computer program product comprising a computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described.
  • Embodiments of the invention may also relate to an apparatus for performing the operations herein.
  • This apparatus may be specially constructed for the required purposes, and/or it may comprise a general-purpose computing device selectively activated or reconfigured by a computer program stored in the computer.
  • a computer program may be stored in a non-transitory, tangible computer readable storage medium, or any type of media suitable for storing electronic instructions, which may be coupled to a computer system bus.
  • any computing systems referred to in the specification may include a single processor or may be architectures employing multiple processor designs for increased computing capability.
  • Embodiments of the invention may also relate to a product that is produced by a computing process described herein.
  • a product may comprise information resulting from a computing process, where the information is stored on a non- transitory, tangible computer readable storage medium and may include any embodiment of a computer program product or other data combination described herein.

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Atmospheric Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Engineering & Computer Science (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Automation & Control Theory (AREA)
  • Traffic Control Systems (AREA)

Abstract

La présente invention concerne un système pour véhicule automatisé connecté orienté système et totalement commandé sur autoroute pour divers niveaux de véhicules connectés et automatisés et d'autoroutes. Le système comprend un ou plusieurs éléments parmi : 1) un réseau de régulation du trafic hiérarchique de centres de régulation du trafic (TCC), des unités de régulateurs du trafic local (TCU), 2) un réseau d'UBR (unité de bord de route) (avec des fonctionnalités intégrées de capteurs de véhicule, une communication infrastructure vers véhicule (I2V) pour délivrer des instructions de commande), 3) un réseau d'OBU (unité embarquée avec capteur et unités de communication véhicule vers infrastructure (V2I)) intégré dans des véhicules connectés et automatisés, et 4) un système de communication et de sécurité sans fil avec une connectivité locale et globale. Ce système fournit une solution plus sûre, plus fiable et plus économique en redistribuant les tâches de conduite de véhicule vers le réseau de régulation du trafic hiérarchique et le réseau d'UBR.
PCT/US2018/012961 2017-01-10 2018-01-09 Systèmes et procédés pour véhicules automatisés connectés sur autoroute WO2018132378A2 (fr)

Priority Applications (6)

Application Number Priority Date Filing Date Title
KR1020197023379A KR102386960B1 (ko) 2017-01-10 2018-01-09 커넥티드 자동화 차량 도로 시스템 및 방법
AU2018208404A AU2018208404B2 (en) 2017-01-10 2018-01-09 Connected automated vehicle highway systems and methods
CA3049019A CA3049019A1 (fr) 2017-01-10 2018-01-09 Systemes et procedes pour vehicules automatises connectes sur autoroute
EP18738971.3A EP3568843A4 (fr) 2017-01-10 2018-01-09 Systèmes et procédés pour véhicules automatisés connectés sur autoroute
JP2019557535A JP6994203B2 (ja) 2017-01-10 2018-01-09 コネクテッド自動運転車のハイウエイシステムとそれを用いた方法
JP2021195103A JP7207670B2 (ja) 2017-01-10 2021-12-01 コネクテッド自動運転車のハイウエイシステムとそれを用いた方法

Applications Claiming Priority (6)

Application Number Priority Date Filing Date Title
CN201710014787.0 2017-01-10
CN201710014787.0A CN106710203A (zh) 2017-01-10 2017-01-10 一种多维智能网联交通***
US201762507453P 2017-05-17 2017-05-17
US62/507,453 2017-05-17
US15/628,331 US10380886B2 (en) 2017-05-17 2017-06-20 Connected automated vehicle highway systems and methods
US15/628,331 2017-06-20

Publications (2)

Publication Number Publication Date
WO2018132378A2 true WO2018132378A2 (fr) 2018-07-19
WO2018132378A3 WO2018132378A3 (fr) 2020-02-06

Family

ID=62840244

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2018/012961 WO2018132378A2 (fr) 2017-01-10 2018-01-09 Systèmes et procédés pour véhicules automatisés connectés sur autoroute

Country Status (1)

Country Link
WO (1) WO2018132378A2 (fr)

Cited By (39)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109285373A (zh) * 2018-08-31 2019-01-29 南京锦和佳鑫信息科技有限公司 一种面向整体道路网的智能网联交通***
CN109410601A (zh) * 2018-12-04 2019-03-01 北京英泰智科技股份有限公司 交通信号灯控制方法、装置、电子设备及存储介质
US10380886B2 (en) 2017-05-17 2019-08-13 Cavh Llc Connected automated vehicle highway systems and methods
CN110351341A (zh) * 2019-06-19 2019-10-18 武汉科技大学 车联网中预算有限条件下满足车流覆盖需求的RSUs部署方法
CN110782684A (zh) * 2018-07-31 2020-02-11 本田技研工业株式会社 用于通过合作感测实现共享自主性的***和方法
CN110895877A (zh) * 2018-08-24 2020-03-20 南京锦和佳鑫信息科技有限公司 一种车路驾驶任务智能化分配***和方法
CN110969833A (zh) * 2018-09-30 2020-04-07 南京锦和佳鑫信息科技有限公司 一种智能网联交通***的固定路径服务***
CN111047890A (zh) * 2019-11-13 2020-04-21 腾讯科技（深圳）有限公司 用于智能驾驶的车辆行驶决策方法及装置、介质、设备
CN111093155A (zh) * 2019-11-04 2020-05-01 上海六联智能科技有限公司 一种5g组网***
CN111179617A (zh) * 2018-11-09 2020-05-19 南京锦和佳鑫信息科技有限公司 一种智能网联车的车载单元
US10692365B2 (en) 2017-06-20 2020-06-23 Cavh Llc Intelligent road infrastructure system (IRIS): systems and methods
EP3673472A4 (fr) * 2018-10-16 2020-07-01 Beijing Didi Infinity Technology And Development Co., Ltd. Système d'optimisation de système de signaux adaptatifs de scats utilisant des données de trajectoires
CN111383456A (zh) * 2020-04-16 2020-07-07 上海丰豹商务咨询有限公司 一种用于智能道路基础设施***的本地化人工智能***
CN111768639A (zh) * 2020-05-30 2020-10-13 同济大学 一种网联交通环境下的多交叉口信号配时***及其方法
CN112053562A (zh) * 2020-09-15 2020-12-08 黑龙江省交投千方科技有限公司 一种基于边缘计算的智能服务开放平台
US10867512B2 (en) 2018-02-06 2020-12-15 Cavh Llc Intelligent road infrastructure system (IRIS): systems and methods
CN112208539A (zh) * 2019-07-09 2021-01-12 奥迪股份公司 用于自动驾驶车辆的***、车辆、方法和介质
CN112349088A (zh) * 2019-08-09 2021-02-09 上海丰豹商务咨询有限公司 自动驾驶专用车道上故障及事故类型识别和道路救援的方法
FR3102966A1 (fr) * 2019-11-08 2021-05-14 Psa Automobiles Sa Procédé de suivi d’un véhicule autonome
CN112868031A (zh) * 2018-08-31 2021-05-28 北美日产公司 具有视觉显著性感知控制的自主运载工具操作管理
CN113095126A (zh) * 2021-03-01 2021-07-09 武汉理工大学 道路交通态势识别方法、***和存储介质
WO2021184133A1 (fr) * 2020-03-19 2021-09-23 Axion Spa Système de surveillance et d'acheminement en temps réel d'équipements, qui permet la détection de situations de risque augmentant la sécurité du fonctionnement d'équipements et de personnes impliquées
DE102020204308A1 (de) 2020-04-02 2021-10-07 Zf Friedrichshafen Ag Autonome Umgebung, die für zumindest teilautonome Fahrzeuge Informationen zum lokalen Berechnen ihres Verhaltens bereitstellt
CN113689723A (zh) * 2021-09-02 2021-11-23 长沙理工大学 不同路侧单元部署特性下的混合交通速度控制方法
CN114182596A (zh) * 2021-09-27 2022-03-15 同济大学 一种可转化储存能量的混凝土路面结构***
CN114365160A (zh) * 2019-09-04 2022-04-15 北京图森智途科技有限公司 一种枢纽服务区需求解决方法和***
CN114585876A (zh) * 2019-08-31 2022-06-03 智能网联交通有限责任公司 一种自动驾驶车辆的分布式驾驶***和方法
US11373122B2 (en) 2018-07-10 2022-06-28 Cavh Llc Fixed-route service system for CAVH systems
US20220237571A1 (en) * 2019-06-28 2022-07-28 Railpros Field Services, Inc. Board Signage Safety System and Method for Use of Same
US11495126B2 (en) 2018-05-09 2022-11-08 Cavh Llc Systems and methods for driving intelligence allocation between vehicles and highways
US11626012B2 (en) 2019-10-11 2023-04-11 Toyota Motor Engineering & Manufacturing North America, Inc. Hierarchical integrated traffic management system for managing vehicles
CN116252626A (zh) * 2023-05-10 2023-06-13 成都壹为新能源汽车有限公司 新能源车辆控制***、方法、装置、控制器、车辆及介质
US11727797B2 (en) 2021-10-28 2023-08-15 Toyota Motor Engineering & Manufacturing North America, Inc. Communicating a traffic condition to an upstream vehicle
US11735041B2 (en) 2018-07-10 2023-08-22 Cavh Llc Route-specific services for connected automated vehicle highway systems
US11735035B2 (en) 2017-05-17 2023-08-22 Cavh Llc Autonomous vehicle and cloud control (AVCC) system with roadside unit (RSU) network
US11842642B2 (en) 2018-06-20 2023-12-12 Cavh Llc Connected automated vehicle highway systems and methods related to heavy vehicles
US11852496B2 (en) 2021-01-08 2023-12-26 Toyota Motor Engineering & Manufacturing North America, Inc. Predictable and delay tolerant traffic management system
US11995990B2 (en) 2020-10-13 2024-05-28 Toyota Motor Engineering & Manufacturing North America, Inc. Methods and systems for managing connected vehicles in mixed traffic
US12020563B2 (en) 2022-06-14 2024-06-25 Cavh Llc Autonomous vehicle and cloud control system

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2395472A1 (fr) 2010-06-11 2011-12-14 MobilEye Technologies, Ltd. Système de traitement d'images et son générateur d'adresses
US20120105639A1 (en) 2010-10-31 2012-05-03 Mobileye Technologies Ltd. Bundling night vision and other driver assistance systems (das) using near infra red (nir) illumination and a rolling shutter
US20130141580A1 (en) 2011-12-06 2013-06-06 Mobileye Technologies Limited Road vertical contour detection
US8527139B1 (en) 2012-08-28 2013-09-03 GM Global Technology Operations LLC Security systems and methods with random and multiple change-response testing
WO2015180090A1 (fr) 2014-05-29 2015-12-03 Empire Technology Development Llc Aide à la conduite à distance
US20160232788A1 (en) 2015-02-06 2016-08-11 Jung H BYUN Method and server for traffic signal regulation based on crowdsourcing data
US9478129B1 (en) 2013-11-22 2016-10-25 Vaibhavi Kothari Vehicle monitoring and control system
US20160325753A1 (en) 2015-05-10 2016-11-10 Mobileye Vision Technologies Ltd. Road profile along a predicted path
US9494935B2 (en) 2014-11-13 2016-11-15 Toyota Motor Engineering & Manufacturing North America, Inc. Remote operation of autonomous vehicle in unexpected environment
US20170075195A1 (en) 2008-12-05 2017-03-16 Mobileye Vision Technologies Ltd. Adjustable camera mount for a vehicle windshield

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7979173B2 (en) * 1997-10-22 2011-07-12 Intelligent Technologies International, Inc. Autonomous vehicle travel control systems and methods
US8352112B2 (en) * 2009-04-06 2013-01-08 GM Global Technology Operations LLC Autonomous vehicle management
US9495874B1 (en) * 2012-04-13 2016-11-15 Google Inc. Automated system and method for modeling the behavior of vehicles and other agents
US20160231746A1 (en) * 2015-02-06 2016-08-11 Delphi Technologies, Inc. System And Method To Operate An Automated Vehicle

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170075195A1 (en) 2008-12-05 2017-03-16 Mobileye Vision Technologies Ltd. Adjustable camera mount for a vehicle windshield
EP2395472A1 (fr) 2010-06-11 2011-12-14 MobilEye Technologies, Ltd. Système de traitement d'images et son générateur d'adresses
US20120105639A1 (en) 2010-10-31 2012-05-03 Mobileye Technologies Ltd. Bundling night vision and other driver assistance systems (das) using near infra red (nir) illumination and a rolling shutter
US20130141580A1 (en) 2011-12-06 2013-06-06 Mobileye Technologies Limited Road vertical contour detection
US8527139B1 (en) 2012-08-28 2013-09-03 GM Global Technology Operations LLC Security systems and methods with random and multiple change-response testing
US9478129B1 (en) 2013-11-22 2016-10-25 Vaibhavi Kothari Vehicle monitoring and control system
WO2015180090A1 (fr) 2014-05-29 2015-12-03 Empire Technology Development Llc Aide à la conduite à distance
US9494935B2 (en) 2014-11-13 2016-11-15 Toyota Motor Engineering & Manufacturing North America, Inc. Remote operation of autonomous vehicle in unexpected environment
US20160232788A1 (en) 2015-02-06 2016-08-11 Jung H BYUN Method and server for traffic signal regulation based on crowdsourcing data
US20160325753A1 (en) 2015-05-10 2016-11-10 Mobileye Vision Technologies Ltd. Road profile along a predicted path

Cited By (58)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US12008893B2 (en) 2017-05-17 2024-06-11 Cavh Llc Autonomous vehicle (AV) control system with roadside unit (RSU) network
US11482102B2 (en) 2017-05-17 2022-10-25 Cavh Llc Connected automated vehicle highway systems and methods
US10380886B2 (en) 2017-05-17 2019-08-13 Cavh Llc Connected automated vehicle highway systems and methods
US11735035B2 (en) 2017-05-17 2023-08-22 Cavh Llc Autonomous vehicle and cloud control (AVCC) system with roadside unit (RSU) network
US11935402B2 (en) 2017-05-17 2024-03-19 Cavh Llc Autonomous vehicle and center control system
US11955002B2 (en) 2017-05-17 2024-04-09 Cavh Llc Autonomous vehicle control system with roadside unit (RSU) network's global sensing
US11990034B2 (en) 2017-05-17 2024-05-21 Cavh Llc Autonomous vehicle control system with traffic control center/traffic control unit (TCC/TCU) and RoadSide Unit (RSU) network
US11881101B2 (en) 2017-06-20 2024-01-23 Cavh Llc Intelligent road side unit (RSU) network for automated driving
US11430328B2 (en) 2017-06-20 2022-08-30 Cavh Llc Intelligent road infrastructure system (IRIS): systems and methods
US10692365B2 (en) 2017-06-20 2020-06-23 Cavh Llc Intelligent road infrastructure system (IRIS): systems and methods
US11854391B2 (en) 2018-02-06 2023-12-26 Cavh Llc Intelligent road infrastructure system (IRIS): systems and methods
US10867512B2 (en) 2018-02-06 2020-12-15 Cavh Llc Intelligent road infrastructure system (IRIS): systems and methods
US11495126B2 (en) 2018-05-09 2022-11-08 Cavh Llc Systems and methods for driving intelligence allocation between vehicles and highways
US11842642B2 (en) 2018-06-20 2023-12-12 Cavh Llc Connected automated vehicle highway systems and methods related to heavy vehicles
US11373122B2 (en) 2018-07-10 2022-06-28 Cavh Llc Fixed-route service system for CAVH systems
US11735041B2 (en) 2018-07-10 2023-08-22 Cavh Llc Route-specific services for connected automated vehicle highway systems
CN110782684B (zh) * 2018-07-31 2023-03-17 本田技研工业株式会社 用于通过合作感测实现共享自主性的***和方法
CN110782684A (zh) * 2018-07-31 2020-02-11 本田技研工业株式会社 用于通过合作感测实现共享自主性的***和方法
CN110895877A (zh) * 2018-08-24 2020-03-20 南京锦和佳鑫信息科技有限公司 一种车路驾驶任务智能化分配***和方法
CN112868031B (zh) * 2018-08-31 2021-10-15 北美日产公司 具有视觉显著性感知控制的自主运载工具操作管理
CN109285373A (zh) * 2018-08-31 2019-01-29 南京锦和佳鑫信息科技有限公司 一种面向整体道路网的智能网联交通***
CN112868031A (zh) * 2018-08-31 2021-05-28 北美日产公司 具有视觉显著性感知控制的自主运载工具操作管理
CN109285373B (zh) * 2018-08-31 2020-08-14 南京锦和佳鑫信息科技有限公司 一种面向整体道路网的智能网联交通***
CN110969833A (zh) * 2018-09-30 2020-04-07 南京锦和佳鑫信息科技有限公司 一种智能网联交通***的固定路径服务***
US10755564B2 (en) 2018-10-16 2020-08-25 Beijing Didi Infinity Technology And Development Co., Ltd. System to optimize SCATS adaptive signal system using trajectory data
US11210942B2 (en) 2018-10-16 2021-12-28 Beijing Didi Infinity Technology And Development Co., Ltd. System to optimize SCATS adaptive signal system using trajectory data
EP3673472A4 (fr) * 2018-10-16 2020-07-01 Beijing Didi Infinity Technology And Development Co., Ltd. Système d'optimisation de système de signaux adaptatifs de scats utilisant des données de trajectoires
CN111179617A (zh) * 2018-11-09 2020-05-19 南京锦和佳鑫信息科技有限公司 一种智能网联车的车载单元
CN109410601A (zh) * 2018-12-04 2019-03-01 北京英泰智科技股份有限公司 交通信号灯控制方法、装置、电子设备及存储介质
CN110351341A (zh) * 2019-06-19 2019-10-18 武汉科技大学 车联网中预算有限条件下满足车流覆盖需求的RSUs部署方法
US20220237571A1 (en) * 2019-06-28 2022-07-28 Railpros Field Services, Inc. Board Signage Safety System and Method for Use of Same
CN112208539A (zh) * 2019-07-09 2021-01-12 奥迪股份公司 用于自动驾驶车辆的***、车辆、方法和介质
CN112349088A (zh) * 2019-08-09 2021-02-09 上海丰豹商务咨询有限公司 自动驾驶专用车道上故障及事故类型识别和道路救援的方法
CN114585876A (zh) * 2019-08-31 2022-06-03 智能网联交通有限责任公司 一种自动驾驶车辆的分布式驾驶***和方法
CN114365160A (zh) * 2019-09-04 2022-04-15 北京图森智途科技有限公司 一种枢纽服务区需求解决方法和***
CN114365160B (zh) * 2019-09-04 2023-10-03 北京图森智途科技有限公司 一种枢纽服务区需求解决方法和***
US11626012B2 (en) 2019-10-11 2023-04-11 Toyota Motor Engineering & Manufacturing North America, Inc. Hierarchical integrated traffic management system for managing vehicles
CN111093155A (zh) * 2019-11-04 2020-05-01 上海六联智能科技有限公司 一种5g组网***
FR3102966A1 (fr) * 2019-11-08 2021-05-14 Psa Automobiles Sa Procédé de suivi d’un véhicule autonome
CN111047890A (zh) * 2019-11-13 2020-04-21 腾讯科技（深圳）有限公司 用于智能驾驶的车辆行驶决策方法及装置、介质、设备
WO2021184133A1 (fr) * 2020-03-19 2021-09-23 Axion Spa Système de surveillance et d'acheminement en temps réel d'équipements, qui permet la détection de situations de risque augmentant la sécurité du fonctionnement d'équipements et de personnes impliquées
DE102020204308A1 (de) 2020-04-02 2021-10-07 Zf Friedrichshafen Ag Autonome Umgebung, die für zumindest teilautonome Fahrzeuge Informationen zum lokalen Berechnen ihres Verhaltens bereitstellt
CN111383456B (zh) * 2020-04-16 2022-09-27 上海丰豹商务咨询有限公司 一种用于智能道路基础设施***的本地化人工智能***
CN111383456A (zh) * 2020-04-16 2020-07-07 上海丰豹商务咨询有限公司 一种用于智能道路基础设施***的本地化人工智能***
CN111768639B (zh) * 2020-05-30 2022-09-20 同济大学 一种网联交通环境下的多交叉口信号配时***及其方法
CN111768639A (zh) * 2020-05-30 2020-10-13 同济大学 一种网联交通环境下的多交叉口信号配时***及其方法
CN112053562A (zh) * 2020-09-15 2020-12-08 黑龙江省交投千方科技有限公司 一种基于边缘计算的智能服务开放平台
US11995990B2 (en) 2020-10-13 2024-05-28 Toyota Motor Engineering & Manufacturing North America, Inc. Methods and systems for managing connected vehicles in mixed traffic
US11852496B2 (en) 2021-01-08 2023-12-26 Toyota Motor Engineering & Manufacturing North America, Inc. Predictable and delay tolerant traffic management system
CN113095126A (zh) * 2021-03-01 2021-07-09 武汉理工大学 道路交通态势识别方法、***和存储介质
CN113095126B (zh) * 2021-03-01 2022-07-05 武汉理工大学 道路交通态势识别方法、***和存储介质
CN113689723A (zh) * 2021-09-02 2021-11-23 长沙理工大学 不同路侧单元部署特性下的混合交通速度控制方法
CN113689723B (zh) * 2021-09-02 2022-10-18 长沙理工大学 不同路侧单元部署特性下的混合交通速度控制方法
CN114182596A (zh) * 2021-09-27 2022-03-15 同济大学 一种可转化储存能量的混凝土路面结构***
US11727797B2 (en) 2021-10-28 2023-08-15 Toyota Motor Engineering & Manufacturing North America, Inc. Communicating a traffic condition to an upstream vehicle
US12020563B2 (en) 2022-06-14 2024-06-25 Cavh Llc Autonomous vehicle and cloud control system
CN116252626A (zh) * 2023-05-10 2023-06-13 成都壹为新能源汽车有限公司 新能源车辆控制***、方法、装置、控制器、车辆及介质
CN116252626B (zh) * 2023-05-10 2023-08-04 成都壹为新能源汽车有限公司 新能源车辆控制***、方法、装置、控制器、车辆及介质

Also Published As

Publication number Publication date
WO2018132378A3 (fr) 2020-02-06

Similar Documents

Publication Publication Date Title
US11990034B2 (en) Autonomous vehicle control system with traffic control center/traffic control unit (TCC/TCU) and RoadSide Unit (RSU) network
WO2018132378A2 (fr) Systèmes et procédés pour véhicules automatisés connectés sur autoroute
US11881101B2 (en) Intelligent road side unit (RSU) network for automated driving
US11964674B2 (en) Autonomous vehicle with partially instrumened roadside unit network
US11854391B2 (en) Intelligent road infrastructure system (IRIS): systems and methods
AU2018208404B2 (en) Connected automated vehicle highway systems and methods
CN114360269A (zh) 一种智能网联道路支持下的自动驾驶协同控制***和方法
US12008893B2 (en) Autonomous vehicle (AV) control system with roadside unit (RSU) network
JP7508134B2 (ja) コネクテッド自動運転車のハイウエイシステムとそれを用いた方法
US12020563B2 (en) Autonomous vehicle and cloud control system

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: 18738971

Country of ref document: EP

Kind code of ref document: A2

ENP Entry into the national phase

Ref document number: 3049019

Country of ref document: CA

ENP Entry into the national phase

Ref document number: 2019557535

Country of ref document: JP

Kind code of ref document: A

NENP Non-entry into the national phase

Ref country code: DE

ENP Entry into the national phase

Ref document number: 2018208404

Country of ref document: AU

Date of ref document: 20180109

Kind code of ref document: A

ENP Entry into the national phase

Ref document number: 20197023379

Country of ref document: KR

Kind code of ref document: A

ENP Entry into the national phase

Ref document number: 2018738971

Country of ref document: EP

Effective date: 20190812