System and method for allocating driving intelligence between vehicles and highways - Patents.com

Abstract

To provide a system and a method for assigning, allocating, and distributing certain functions and intelligence for facilitating the operation and control of vehicles by a connected automated vehicle highway (CAVH) system, improving the general safety of an overall transportation system, and ensuring the efficiency, intelligence, reliability, and resilience of the CAVH system, and also provide a method for defining CAVH system intelligence and levels thereof based on two dimensions, which are vehicle intelligence and infrastructure intelligence.SOLUTION: A connected automated vehicle highway (CAVH) system includes components for sensing, communicating, and controlling. The components assign, allocate, and distribute functions and intelligence that facilitate vehicle operation and control.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] This application claims priority to U.S. Provisional Patent Application No. 62 / 669,215, filed May 9, 2018, the contents of which are incorporated herein by reference in their entirety.

[0002] The present invention relates to a system and method for allocating, locating and distributing specific functions and intelligence for a Connected Autonomous Vehicle Highway (CAVH) system, facilitating vehicle operation and control, improving the general safety of the overall transportation system, and ensuring the efficiency, intelligence, reliability and resilience of the CAVH system. The present invention also provides a method for defining CAVH system intelligence and its level based on two dimensions: vehicle intelligence and infrastructure intelligence. [Background technology]

[0003] Autonomous vehicles that can sense the environment, detect obstacles, and drive without human effort are in the development stage. Currently, autonomous vehicles are undergoing field testing but are not yet in widespread commercial deployment. Existing approaches to autonomous vehicles pose significant challenges in their implementation as they require expensive and complex on-board systems and multiple sensing systems, and rely heavily on vehicle sensors and controls.

[0004] Alternative systems and methods that address these issues are described in U.S. Patent Application No. 15 / 628,331, filed June 20, 2017, U.S. Provisional Patent Application No. 62 / 626,862, filed February 6, 2018, U.S. Provisional Patent Application No. 62 / 627,005, filed February 6, 2018, and U.S. Provisional Patent Application No. 62 / 655,651, filed April 10, 2018, the disclosures of which are incorporated by reference in their entireties into this application (hereinafter referred to as CAVH systems).

[0005] The invention described herein provides systems and intelligence allocation methods for various combinations of Intelligent Roadway Infrastructure Systems (IRIS) and vehicle automation to achieve transportation and vehicle system performance, facilitating vehicle operation and control for optimal and reliable operation of Connected Automated Vehicle Highway (CAVH) systems. The following description describes a general CAVH system and intelligence allocation method to achieve specific system performance, and provides detailed method examples of this integrated vehicle and transportation system. Summary of the Invention

[0006] The present invention relates to a system and method for allocating, locating and distributing specific functions and intelligence for a Connected Autonomous Vehicle Highway (CAVH) system, facilitating vehicle operation and control, improving the general safety of the entire transportation system and ensuring the efficiency, intelligence, reliability and resilience of the CAVH system. The present invention also provides a method for defining CAVH system intelligence and its level based on two dimensions: vehicle intelligence and infrastructure intelligence.

[0007] For example, provided herein in some embodiments is a Connected Autonomous Vehicle Highway (CAVH) system that includes sensing, communication, and control components connected via segments and nodes that manage an overall transportation system. In some embodiments, vehicles managed within the CAVH system include CAVH vehicles and non-CAVH vehicles. In some embodiments, the CAVH vehicles and non-CAVH vehicles include manual vehicles, automated vehicles, and connected vehicles.

[0008] In some embodiments, segments and nodes have sensing and control areas that overlap with adjacent segments and nodes for handover of CAVH vehicles between the adjacent segments and nodes.

[0009] In some embodiments, the CAVH system includes four levels of control: a) Vehicle b) Roadside Unit (RSU) c) Traffic Control Unit (TCU) d) Traffic Control Centre (TCC)

[0010] In some embodiments, the vehicle control level includes a vehicle with on-board systems or applications that operate the vehicle dynamics systems to implement road coordinate commands from the RSU.

[0011] In some embodiments, the RSU level involves a segment or node managed by an RSU responsible for sensing and control of the vehicle. In some embodiments, sensing includes information from LiDAR and / or radar, or sensing or using computer vision or other related systems deployed to fully capture information within the segment or node. In some embodiments, the RSU responds to sensing to manage collision avoidance, routing execution, lane change adjustments, and high resolution guidance commands for road coordinates to enable the vehicle to perform autonomous driving.

[0012] In some embodiments, the TCU level involves multiple RSUs managed by the TCU. In some embodiments, the TCU is responsible for updating dynamic maps of moving objects and coordinated control among the RSUs for continuous autonomous driving. In some embodiments, multiple TCUs are connected via a TCC to cover an area or sub-network.

[0013] In some embodiments, the TCC level includes high performance computing and cloud services responsible for managing comprehensive routing plans and updating dynamic maps of congestion, incidents, severe weather, and events impacting the region. In some embodiments, the TCC level is further responsible for managing connections with other application services, including but not limited to payment and transaction systems, regional traffic management centers (TMCs), third party applications (e.g., government applications, private enterprise applications, etc.). In some embodiments, multiple TCCs are used to facilitate operation of CAVHs between regions of a metropolitan area or across a metropolitan area.

[0014] For example, provided herein in some embodiments is a Connected Autonomous Vehicle Highway (CAVH) system that includes sensing, communication, and control components that assign, allocate, and distribute functionality and intelligence to facilitate vehicle operation and control. In some embodiments, the components improve the safety of a transportation system that includes the components. In some embodiments, the components improve the efficiency, intelligence, reliability, and / or resilience of the CAVH system. In some embodiments, the assigned functionality includes sensing. In some embodiments, the assigned functionality includes transportation behavior prediction and management. In some embodiments, the assigned functionality includes planning and decision making. In some embodiments, the assigned functionality includes vehicle control.

[0015] In some embodiments, a CAVH system, including sensing, communication, and control components that allocate, deploy, and distribute functionality and intelligence that facilitates operation and control of the vehicle, includes one or more of the following subsystems: a) An Intelligent Road Infrastructure System (IRIS) including one or more of the following: Roadside Unit (RSU), Network, Traffic Control Unit (TCU), and Traffic Control Center (TCC); b) Vehicles equipped with an On-Board Unit (OBU)

[0016] In some embodiments, the CAVH system is supported by one or more of the following: a) Real-time communications over wired and wireless media b) Power supply network c) Cyber ​​safety and security systems

[0017] In some embodiments, the allocation of functionality and intelligence to facilitate operation and control of the vehicle is based on the following levels: a) Vehicle level b) Level of infrastructure c) System level

[0018] In some embodiments, the system is configured to manage functionality and intelligence at any one of a variety of automation level combinations at each level, hi some embodiments, the system is configured to evaluate the particular level of automation present at any level and select an appropriate allocation of functionality and intelligence to optimally manage infrastructure and vehicle operations under such conditions.

[0019] In some embodiments, the vehicle levels include the following automation levels: a) A0: No automation function b) A1: Basic functions to assist a human driver in controlling the vehicle c) A2: Assists a human driver in controlling the vehicle for simple tasks and has basic sensing capabilities d) A3: The ability to sense the environment in detail and in real time, enabling it to handle relatively complex driving tasks. e) A4: Capabilities that allow the vehicle to operate independently under limited conditions, possibly with the backup of a human driver; and f) A5: Ability to enable a vehicle to operate independently under all conditions without the back-up of a human driver

[0020] In some embodiments, the levels of infrastructure include the following levels of automation: a) I0: No function b) I1: Information Collection and Traffic Management. The infrastructure provides basic sensing capabilities in terms of aggregate traffic data collection and basic planning and decision making, supporting simple traffic management at low spatial and temporal resolution. c) I2: Vehicle guidance for I2X and driving assistance. In addition to the functions provided by I1, the infrastructure provides limited sensing capabilities for detection of pavement conditions and vehicle kinematic detection, such as longitudinal / lateral position / speed / acceleration, for a portion of the traffic, on a second or minute basis. The infrastructure also provides traffic information and vehicle control suggestions and instructions to vehicles via I2X communication. d) I3: Dedicated lane automation. The infrastructure provides individual vehicles with millisecond-by-millisecond information about the dynamics of surrounding vehicles and other objects to support fully automated driving in dedicated lanes for CAVH-compatible vehicles. The infrastructure has limited ability to predict vehicle behavior. e) I4: Scenario-Specific Automation. The infrastructure provides vehicles with detailed driving instructions to achieve fully automated driving in specific scenarios / areas, such as predefined geo-fenced areas in mixed traffic of CAVH-compatible and non-CAVH-compatible vehicles. Mandatory vehicle-based automation capabilities, such as emergency braking, stand by as a back-up system in case of infrastructure failure. f) I5: Infrastructure Fully Automated. The infrastructure provides full control and management to individual vehicles in all scenarios and optimizes the entire network in which it is deployed. No vehicle automation functions are needed as a back-up and full active safety features are available.

[0021] In some embodiments, the levels of the system include the following levels of automation: a) S0: No function b) S1: The system maintains simple functions for individual vehicles, such as cruise control and passive safety functions. The system detects the speed and distance of the vehicle.

[0022] c) S2: The system operates with individual intelligence, detecting vehicle functional status, vehicle acceleration, traffic signs and signals. Each vehicle makes decisions based on its own information and provides complex functions of partial driving automation, such as adaptive cruise control, lane keeping, lane changing and automatic parking assistance.

[0023] d) S3: The system integrates information across vehicle groups and operates with ad-hoc intelligence with predictive capabilities. The system has the intelligence for vehicle group decision making and can handle complex conditional automated driving tasks such as cooperative cruise control, vehicle platooning, vehicle intersection negotiating, merging, and diverging.

[0024] e) S4: The system optimally integrates driving behaviors within the partial network. The system detects and communicates detailed information within the partial network, makes decisions based on both vehicle and traffic information within the network, handles advanced driving automation tasks such as passing through signal corridors, and provides optimal trajectories within the small transportation network.

[0025] f) S5: Vehicle automation and system traffic automation. The system works optimally within the entire transportation network. The system detects and communicates detailed information within the large transportation network and makes decisions based on all information available within the network. The system handles complete driving automation tasks, including individual vehicle tasks, transportation tasks, and coordinates all vehicles.

[0026] In some embodiments, the level of the system depends on two levels: 1) vehicle, 2) infrastructure, and is represented by the following equation (S = system automation, V = vehicle intelligence, I = infrastructure intelligence): S = f(V, I)

[0027] In some embodiments, the equation is a non-linear function and system automation level 2 includes, for example:

[0028] a) Sensing: Vehicle subsystems dominate. Infrastructure subsystems help complete the driving environment.

[0029] b) Prediction and management of transport behavior: The vehicle subsystem is dominant. The infrastructure subsystem mainly interfaces with the vehicle subsystem.

[0030] c) Planning and Decision-Making: The vehicle subsystem plays a key role, while the infrastructure subsystem optimizes the system from a global perspective.

[0031] d) Vehicle Control: The vehicle subsystem is dominant. The infrastructure subsystem supports the vehicle control commands.

[0032] The system may be implemented in a variety of different ways depending on the level of automation present at various levels. For example, in some embodiments (Method 1), a control component allocates, arranges, and distributes intelligence, allowing functions to be assigned to vehicles, the autonomous vehicles and the infrastructure do not have communication capabilities and function independently, and the infrastructure does not provide improvements to the vehicle intelligence, which is applicable to the S1 scenario.

[0033] In another embodiment (Method 2), the control component allocates, arranges, and distributes the intelligence such that most of the functions are assigned to the vehicle subsystems and the vehicle plays a dominant role. The roadside device subsystem plays a supplementary role only for simple tasks, helping the vehicle maintain a certain speed and providing collision warnings. In case of conflicting control decisions, the vehicle makes the decision. This is applicable to either S1 or S2 scenarios.

[0034] In another embodiment (Method 3), the control component allocates, deploys, and distributes intelligence such that functions can be flexibly allocated between vehicle and infrastructure subsystems, with either the infrastructure or vehicle subsystems taking the primary role in sensing and decision-making, where the roadside device subsystem allows the vehicle to make decisions based on the local environment and provides control suggestions for the vehicle to operate on:

[0035] a) Follow-up strategy b) Lane keeping strategy c) Lane-changing strategies d) Merging and Diverging Strategies e) Passing through an intersection

[0036] In case of conflicting control decisions, the vehicle can make control decisions on its own or use information from the infrastructure to make control decisions, which can be applied to S2 or S3 scenarios.

[0037] In another embodiment (Method 4), the control component allocates, arranges, and distributes intelligence such that most of the functionality is distributed to the roadside device subsystems and the infrastructure plays a dominant role in the control decisions. The vehicle subsystem also has basic functions such as collision avoidance, the vehicle follows all information provided by the infrastructure, and in case of conflicting control decisions, the vehicle takes the decision made by the infrastructure as the control decision. This is applicable to S3 or S4 scenarios.

[0038] In another embodiment (Method 5), the control component allocates, deploys, and distributes intelligence such that all functions depend on the roadside subsystem and the vehicles have the ability to communicate and follow commands, where all vehicles are controlled by the infrastructure system and decisions are made by and communicated to the systems through the roadside device network. This is applicable to S4 or S5 scenarios.

[0039] In some embodiments, the control component manages mixed traffic of vehicles with different levels of connectivity and automation. In some embodiments, the control component collects vehicle-generated data such as vehicle movement and status, transmits the collected data to the RSU, and receives input data from the IRIS. Here, the OBU facilitates vehicle control based on the input data from the IRIS. Here, in case of a failure in the vehicle control system, the OBU can take over in a short time and safely stop the vehicle.

[0040] In some embodiments, IRIS facilitates vehicle operation and control for CAVH systems. The IRIS provides individual vehicles with customized detailed information and time-sensitive control commands for the vehicles to perform driving tasks such as vehicle following, lane changing, route guidance, etc. The IRIS provides operation and maintenance services for both highway and urban arterial vehicles.

[0041] In some embodiments, IRIS is built and managed as an open platform, with proprietary subsystems owned and / or operated by different entities and shared physically and / or logically among various CAVH systems, including one or more or all of the following physical subsystems, as described below:

[0042] a. Roadside unit (RSU) network, whose functions include sensing, communication, control (fast / simple), and driving range calculation. b. Traffic Control Unit (TCU) and Traffic Control Center (TCC) Network c. On-Board Units (OBUs) and associated vehicle interfaces d. Transportation Operations Center; and e. Cloud-based platforms for information and computing services. In some embodiments, the system provides one or more of the following functional categories:

[0043] i. Sensing ii. Predicting and managing transport behaviour iii. Planning and decision-making iv. Vehicle control

[0044] The systems and methods may include and be integrated with the features and components described in U.S. Patent Application No. 15 / 628,331, filed June 20, 2017, U.S. Provisional Patent Application No. 62 / 626,862, filed February 6, 2018, U.S. Provisional Patent Application No. 62 / 627,005, filed February 6, 2018, and U.S. Provisional Patent Application No. 62 / 655,651, filed April 10, 2018, the contents of which are incorporated herein by reference in their entireties.

[0045] Also provided herein are methods of utilizing any of the systems described herein to manage one or more aspects of traffic control, including processes undertaken by individual participants of the system (e.g., drivers, public or private local, regional or national traffic facilitators, government agencies, etc.) and the collective activities of one or more participants working in conjunction with or independently of each other.

[0046] Some portions of this specification describe embodiments of the invention in terms of algorithms and symbolic representations of operations on information. These algorithmic descriptions and representations are commonly used by those skilled in the data processing arts to effectively convey the substance of their work to others skilled in the art. While these operations are described functionally, computationally, or logically, they are understood to be implemented by computer programs or equivalent electrical circuits, microcode, or the like. Further, without loss of generality, it is sometimes convenient to refer to arrangements of these operations as modules. The described operations and their associated modules may be embodied in software, firmware, hardware, or any combination thereof.

[0047] Certain steps, operations, or processes described herein may be performed or implemented by one or more hardware or software modules, alone or in combination with other devices. In one embodiment, a software module is implemented in a computer program product comprising a computer readable medium containing computer program code, which can be executed by a computer processor to perform any or all of the steps, operations, or processes described.

[0048] An embodiment of the present invention may also relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes and / or may comprise a general-purpose computing device selectively activated or reconfigured by a computer program stored in a computer. Such a computer program may be stored in a non-transitory, tangible computer-readable storage medium that may be coupled to a computer system bus, or any type of medium suitable for storing electronic instructions. Furthermore, any computing system referred to herein may include a single processor, or may be an architecture that utilizes multiple processor designs to increase computing power.

[0049] Embodiments of the invention may also relate to products produced by the computing processes described herein. Such products may include information resulting from the computing processes, the information being stored on a non-transitory, tangible, computer-readable storage medium, and may include any of the embodiments of a computer program product or other data combinations described herein. [Brief description of the drawings]

[0050] [Figure 1] provides a graph illustrating the non-linear combined levels of system automation and intelligence. [Diagram 2] 1 shows two-dimensional and three-dimensional graphs of system intelligence level plotting system automation level, vehicle automation level, and infrastructure automation level. [Diagram 3] 1 illustrates an embodiment of a vehicle subsystem. [Figure 4] 1 shows an example of an IRIS configuration. [Diagram 5] Illustrates an embodiment of an AV-specific approach. [Figure 6] shows an example of a V2V- and V2I-based approach. [Figure 7] shows an example of the CAVH approach. [Figure 8] provides an example of an intelligence allocation approach for Level 2 systems intelligence. [Figure 9] 1 shows an embodiment of a vehicle control flowchart. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0051] Examples of this technique are described below, and it should be understood that these are exemplary embodiments and that the invention is not limited to these specific examples.

[0052] Figure 1 provides a graph showing that the automation level of the system is a combination of the vehicle automation level and the infrastructure automation level. The level of the global system is not limited to a direct combination of the degrees of both subsystems. The functions of the global system are distributed to the vehicle subsystem and the infrastructure subsystem.

[0053] The system provides the following functional categories: a) Sensing b) Traffic prediction and management c) Planning and decision-making d) Vehicle Control

[0054] Figure 2 shows two-dimensional and three-dimensional graphs showing the relationship between the system automation level and the infrastructure automation level with respect to the vehicle automation level. For further explanation, Table 1 below shows the system level for each combination of vehicle and infrastructure automation level with numbers in each row and column.

[0055] Table 1: Example of system intelligence level determination JPEG0007676042000001.jpg45170

[0056] FIG. 3 shows an example of a vehicle subsystem having the following components: 301-Vehicle 302-OBU: On-board unit that controls the vehicle and collects and transmits data 303-Communication module: Transfers data between RSU and OBU 304-Data Collection Module: Collects human-generated dynamic and static vehicle state data. 305-Vehicle control module: Executes control commands from the RSU. In case the vehicle's control system is damaged, it can take over control and safely stop the vehicle. 306-RSU: Roadside unit that collects and transmits data

[0057] As shown in Fig. 3, the vehicle subsystem includes all vehicles 301 in the CAVH system. The OBU 302 includes, for each vehicle, a communication module 303, a data collection module 304, and a vehicle control module 305. The data collection module collects data from the vehicle and input data from the human driver and transmits it to the RSU 306 via the communication module. The OBU also receives data from the RSU via the communication module. The vehicle control module assists in vehicle control based on the data from the RSU.

[0058] FIG. 1 shows an example of an Intelligent Road Infrastructure System (IRIS) having the following components: 401-Macro-TCC / TOC: The highest level TCC / TOC that manages the Regional TCCs 402-Regional TCC: A high-level TCC that manages the corridor TCCs 403-Corridor TCC: Mid-level TCC that manages segment TCUs 404-Segment TCU: A low-level TCU that manages point TCUs 405-point TCU: The lowest level TCU that manages RSUs

[0059] 4 shows the structure of an IRIS embodiment. A macro TCC 401, which may or may not cooperate with an external TOC 401, manages a certain number of regional TCCs 402 within its coverage area. In turn, the regional TCC manages a certain number of corridor TCCs 403, which in turn manage a certain number of segment TCUs 404, which in turn manage a certain number of point TCUs 405, which in turn manage a certain number of RSUs 306. The RSUs transmit customized traffic information, control instructions to vehicles 301, and receive information provided by vehicles. Furthermore, in an embodiment, the IRIS is supported by cloud services.

[0060] Three example approaches include: 1. Approach of autonomous vehicles 2. Connected and autonomous vehicle approach using V2I and V2V technologies 3. CAVH-IRIS, an infrastructure-based approach using sensing, prediction, and decision-making from roadside systems

[0061] Approach 1 has a history of decades. There are several method examples to support this approach, such as those described in U.S. Pat. No. 9,120,485 (An autonomous vehicle is configured to follow a baseline trajectory. A computer system in the vehicle receives changes to the trajectory and optimizes a new trajectory for the vehicle), U.S. Pat. No. 9,665,101 (The system determines a route for the vehicle from a current location to a destination), U.S. Pat. No. 9,349,055 (A Google autonomous vehicle is used to detect other vehicles when attempting to sense the environment), and U.S. Publication No. 20170039435 (A Google autonomous vehicle is used to detect traffic signals when sensing the environment), the contents of which are incorporated by reference in their entirety. Products and techniques developed by vehicle manufacturers and AI research groups have been implemented so far. However, this approach lacks planning and decision-making from the perspective of global optimization. Although autonomous driving AI can replace human drivers, human drivers cannot achieve better performance on transportation systems. This approach also lacks the sensing range and computational power of vehicles to address the complexities and limitations that will be faced in the future.

[0062] FIG. 5 shows an embodiment of an AV-specific approach with components. 501 Vehicle Sensor 502 Pedestrians on the Street 503 - Roadside Infrastructure

[0063] Figure 5 shows how an autonomous vehicle 301 operates in this approach. When the AV is on the road, it is continuously sensing its environment with multiple sensors 501. The environment includes other vehicles 301 around it, pedestrians 502, road infrastructure 503, etc. In this example, the AV detects two pedestrians ahead of it, three vehicles around it, and a stop sign at an intersection. The AV uses the information it has to make decisions and operate appropriately and safely on the road.

[0064] A connected, autonomous vehicle approach that leverages communications. This approach has been attempted for several years. Several prototypes have already been developed, such as those described in US 2012 / 0059574 (A vehicle unit transmits its vehicle speed to a roadside unit when in wireless communication range. The roadside unit transmits the vehicle speed to a traffic controller. The traffic controller receives vehicle speed data from multiple vehicles and determines a recommended speed for each vehicle.) and US Patent No. 7,425,903 (In this grid system, a car is equipped with a transmitter, a receiver, a computer, and a selection of sensors. Other adjacent vehicles also include devices for sending and receiving similar signals. If sensors in the vehicle detect a change, such as hard braking or slow speed, the vehicle automatically transmits via a transmitter on a wireless communication channel to any other receivers in the vicinity.) the contents of which are incorporated herein by reference in their entirety. Using V2V and V2I communication technologies, the system can perform relatively better than individual autonomous vehicles. However, without system-level intervention, the system cannot achieve overall system or global optimization, and the sensing, storage, and computation capabilities of this approach are limited.

[0065] FIG. 6 illustrates an example V2V and V2I based approach including component 601: roadside infrastructure facilitating communication. FIG. 6 illustrates how a V2V and V2I based approach works. This approach has been available for several years. Several prototypes have been developed so far. By using V2V and V2I communication technology, the system can perform relatively better than individual autonomous vehicles. Each vehicle 301 receives information detected by the surrounding infrastructure 601 and other vehicles 301. The information includes vehicles, passengers, traffic conditions, etc. With the information provided, the vehicle can enhance its awareness of its surroundings and make decisions. However, without system-level intervention, the system cannot achieve overall system or global optimization. Also, the sensing, storage, and computation capabilities of this approach are limited.

[0066] Figure 7 shows an example of the CAVH-IRIS approach. This system has the ability to make system-level optimal decisions and maneuvers for individual vehicles, which is beneficial for integrated transportation systems. This system is configured with more powerful computational and storage capabilities, but may be limited in communication. The embodiment of Figure 7 includes the following components: 701-Roadside Sensor 702-High Level IRIS 703-Cloud: Supporting data storage and computation

[0067] FIG. 7 is a demonstration of the CAVH-IRIS approach. The RSU 306 in FIG. 7 senses the road, the vehicle 301, and the driving environment using on-road sensors 701. This information is sent to a higher level IRIS 702. Using the data from the sensors, the system can make system-level optimal decisions and can perform steering for individual vehicles, which is beneficial for integrated transportation systems. The system communicates with the OBU 302 to control the vehicles. As shown in FIG. 7, the system can be configured with more powerful computation and storage capabilities by communicating with the cloud 703.

[0068] Figure 8 shows an example of an intelligence allocation for System Intelligence Level 2, which includes the following components: 801: Vehicle-mounted ultrasonic sensor 802: Car camera 803: Automotive LiDAR 804: Vehicle-mounted long-range radar 805: Vehicle-mounted RSU detection area 806: Roadside unit 807: Communication between RSU and vehicle 808: CAVH system vehicle

[0069] Figure 8 shows an example of combining intelligence distributed between vehicles and infrastructure.

[0070] a) Sensing: The vehicle subsystem plays a central role, which means that the driving environment is mainly sensed by sensors such as ultrasonic sensors 801, cameras 802, LiDAR 803, long-range radar 804, etc. mounted on the vehicle 808. Meanwhile, the infrastructure subsystem 806 detects the traffic in its coverage area 805 and keeps communicating with the vehicle subsystem 807 to send traffic information to complete the driving environment.

[0071] b) Traffic behavior prediction and management: The vehicle subsystem plays a central role. The infrastructure subsystem mainly cooperates with the vehicle subsystem. This allows predicting events from a macroscopic level, such as long-distance traffic jams. c) Planning and Decision Making: The vehicle subsystem is the main component, but the infrastructure subsystem can suggest optimization of the system from a global perspective.

[0072] d) Vehicle Control: The vehicle subsystem is the main component. The infrastructure subsystem only gives simple control commands that are interpreted by the vehicle subsystem. In case of conflicting control commands of two subsystems, the vehicle follows the instructions sent by the vehicle subsystem. The system reports and memorizes the conflict event.

[0073] Figure 9 shows that in the above intelligence allocation method 2, the vehicle subsystem plays a central role. In this situation, the vehicle subsystem provides a safety margin to control the vehicle, and the IRIS subsystem provides control commands from a global perspective. The instructions from the IRIS must satisfy the safety margin provided by the vehicle. If the command from the IRIS does not satisfy the safety margin, the vehicle will follow the instructions sent from the vehicle subsystem. Records of conflicts are stored and reported.

Claims

1. A connected autonomous vehicle highway (CAVH) system comprising a vehicle subsystem and an intelligent roadway infrastructure system (IRIS), The vehicle subsystem comprises a vehicle having an on-board unit (OBU) and operating at vehicle automation level V; The IRIS comprises a cloud platform and operates at infrastructure automation level I; The CAVH system provides sensing, transportation behavior prediction, management, planning / decision support, and vehicle control; The cloud platform includes a control component (cloud platform control component) that generates and provides time-sensitive vehicle control commands to the OBU, the vehicle control commands including indications of vehicle longitudinal and lateral positions; The CAVH system operates at system intelligence levels S=1, 2, 3, 4, and 5 during vehicle operation; the CAVH system identifies a vehicle automation level V of the vehicle subsystem while driving a vehicle, and identifies an infrastructure automation level I of the IRIS while driving a vehicle; using the cloud platform control component to allocate functionality and intelligence to the vehicle subsystems and the IRIS and provide system intelligence S to the CAVH system, whereby the CAVH system manages the IRIS and the vehicle to facilitate operation and control of the vehicle during vehicle operation; The vehicle control command generated by the cloud platform includes one or more information items of sensing, transportation behavior prediction and management, planning and decision-making, and vehicle control; The system intelligence level S includes the following automation levels: S=0: No function S=1: The system maintains simple functions for individual vehicles, such as cruise control and passive safety functions. The system detects the speed and distance of the vehicle. S=2: The system operates with individual intelligence, detecting the vehicle's functional state, vehicle acceleration, traffic signs and signals, and each vehicle makes decisions based on its own information, and has complex functions of partial driving automation, such as adaptive cruise control, lane keeping, lane changing, and automatic parking assistance. S=3: The system integrates information across vehicle groups and operates with ad-hoc intelligence with predictive capabilities, the system has decision-making intelligence for vehicle groups and can handle complex conditional automated driving tasks such as cooperative cruise control, vehicle platooning, vehicle through intersections, merging, and splitting; S=4: The system optimally integrates driving behaviors within the partial network, the system detects and conveys detailed information within the partial network, makes decisions based on both vehicle and transportation information within the network, handles advanced driving automation tasks such as passing through signal corridors, and provides optimal trajectories within small transportation networks; S=5: Vehicle automation and system traffic automation. The system operates optimally within the entire transportation network, the system detects and communicates detailed information within the large transportation network, makes decisions based on all information available within the network, the system handles individual vehicle tasks, full driving automation tasks including transportation tasks, and coordinates all vehicles. A connected autonomous vehicle highway (CAVH) system comprising:

2. The CAVH system includes: Identifying the system intelligence level S to be achieved; Identifying a current automation level of the infrastructure automation level I and the vehicle automation level V during vehicle operation; Using the cloud platform control component to allocate driving functions and driving intelligence to the vehicle subsystems and the IRIS to achieve system intelligence S for the CAVH system, thereby providing vehicle operation and control over the vehicle during vehicle operation.

2. The system of claim 1 .

3. Allocating functionality and intelligence to the vehicle subsystems and the IRIS includes allocating sensing functionality to the vehicle subsystems and the IRIS.

2. The system of claim 1 .

4. Allocating functionality and intelligence to the vehicle subsystems and the IRIS includes allocating transportation behavior prediction and management functionality to the vehicle subsystems and the IRIS.

2. The system of claim 1 .

5. allocating functionality and intelligence to the vehicle subsystems and the IRIS includes allocating planning and decision-making functionality to the vehicle subsystems and the IRIS.

2. The system of claim 1 .

6. allocating functionality and intelligence to the vehicle subsystems and the IRIS includes allocating vehicle control functions to the vehicle subsystems and the IRIS.

2. The system of claim 1 .

7. The CAVH system is supported by real-time communications over wired and / or wireless media, a power network, and a cyber safety and security system.

2. The system of claim 1 .

8. Identifying the vehicle automation level V of the vehicle subsystem while driving a vehicle includes: Identifying a vehicle automation level V=0 for a vehicle that does not provide automation functionality while driving; Identifying a vehicle automation level V=1 of a vehicle for which assistance for controlling the vehicle while driving is to be provided; Identifying a vehicle automation level V=2 for a vehicle that assists in controlling the vehicle and has sensing capabilities during operation; Identifying a vehicle automation level V=3 for a vehicle that provides detailed, real-time sensing of the environment and provides management of the driving task while driving; Identifying a vehicle automation level V=4 for a vehicle that drives autonomously under certain conditions during driving, and / or Identifying a vehicle automation level V=5 for a vehicle that drives autonomously in all driving conditions; 2. The system of claim 1 .

9. Identifying the infrastructure automation level I of the IRIS while driving a vehicle includes: Identifying an infrastructure automation level I=0 for IRIS that does not provide any functionality; Identifying infrastructure automation level I=1 for IRIS, which provides aggregated traffic data collection and planning and decision making to support traffic management; Identifying infrastructure automation level I=2 for IRIS, which provides sensing capabilities for pavement condition detection and vehicle kinematics detection for a portion of the traffic and provides traffic information and vehicle control suggestions and instructions to vehicles via I2X communication; Identifying an infrastructure automation level I=3 for said IRIS, which provides individual vehicles with information describing the dynamics of surrounding vehicles and other objects, provides fully automated driving in lanes dedicated to CAVH-compatible vehicles, and provides predictions of transport behavior; Identifying infrastructure automation level I=4 to IRIS, which provides detailed driving instructions for controlling the vehicle; Identifying said infrastructure automation level I=5 in said IRIS, which provides complete control and management for individual vehicles, manages the traffic network that constitutes the infrastructure, and provides vehicles with complete active safety functions; 2. The system of claim 1 .

10. The system intelligence S depends on the vehicle automation level V and the infrastructure automation level I during vehicle operation.

2. The system of claim 1 .

11. the CAVH system operates at the system intelligence level S=2, and the cloud platform control component of the CAVH system is used to provide intelligence to individual vehicles; The CAVH system detects the functional state of the vehicle, the acceleration of the vehicle, the traffic signs, and the traffic lights, and each vehicle makes decisions based on its own information, and has driving automation; The function is a) Sensing: the vehicle subsystem provides more sensing than the IRIS; b) Traffic prediction and management: The vehicle subsystem provides more traffic prediction and management than the IRIS. c) Planning and Decision-Making: The vehicle subsystem provides more planning and decision-making than the IRIS; d) vehicle control, the vehicle subsystem including providing more vehicle control than the IRIS; 2. The system of claim 1 .

12. the CAVH system operates at the system intelligence level S=2; the cloud platform control component of the CAVH system is used by the CAVH system to assign functions to vehicles; The IRIS assists the vehicle in maintaining speed and provides collision warning; The vehicle resolves conflicting control decisions between the vehicle and the IRIS.

2. The system of claim 1 .

13. The CAVH system operates at the system intelligence level S=3, integrating information collected from multiple vehicles, providing ad-hoc prediction and decision-making to vehicles, and managing conditional automated driving tasks. a cloud platform control component is used by the CAVH system to allocate functions to the vehicle subsystems and / or the IRIS; The IRIS assists the vehicle in making decisions based on the local environment; Assisting the vehicle in driving to: a) following strategy, b) lane keeping strategy, c) lane changing strategy, d) merging and splitting strategy, and e) intersection traversal. The vehicle resolves conflicting control decisions between the vehicle and the IRIS.

2. The system of claim 1 .

14. the CAVH system operates at the system intelligence level S=4 and manages driving behavior within a road network; The system detects and communicates detailed information within a road network, makes vehicle-based decisions, manages traffic information within the road network, driving automation tasks, and provides vehicle trajectories within the road network; the cloud platform control component is used by the CAVH system to allocate functions for sensing, decision-making, and vehicle control to the vehicle subsystems and the IRIS; The vehicle resolves conflicting control decisions between the vehicle and the IRIS.

2. The system of claim 1 .

15. the CAVH system operates at the system intelligence level S=5; The CAVH system provides vehicle automation and system traffic automation, the system managing a traffic network; The system detects and communicates with detailed information within the transportation facility, builds a network, and makes decisions based on the information within the transportation network, the system manages the complete driving automation task and coordinates the control of all vehicles, the cloud platform control component is used by the CAVH system to allocate functions to the vehicle subsystems and the IRIS; The vehicle is controlled by the IRIS, and control decisions are made by the CAVH system and communicated to the vehicle through the IRIS.

2. The system of claim 1 .

16. The CAVH system manages traffic including CAVH compatible vehicles.

2. The system of claim 1 .

17. The IRIS facilitates vehicle operation and control of the CAVH system; The IRIS provides individual vehicles with detailed customized information and time-bound control instructions to accomplish driving tasks, and provides vehicle operation and maintenance services.

2. The system of claim 1 .

18. The IRIS is built and managed as an open platform, including subsystems owned and / or operated by different entities and physically and / or logically shared between different CAVH systems; 20. The system of claim 17 .

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