Distributed driving with flexible roadside resources

Patent metadata

Field	Value
Publication number	US-12046136-B2
Application number	US-202318221722-A
Country	US
Kind code	B2
Filing date	Jul 13, 2023
Priority date	Aug 31, 2019
Publication date	Jul 23, 2024
Grant date	Jul 23, 2024

Abstract

Provided herein is technology related to a distributed driving system (DDS) by using flexible, on-demand, and customized resources and functions from an intelligent roadside toolbox (IRT). These resources comprise computational resources, cloud resources, system security resources, backup and redundancy resources. The functions comprise sensing, transportation behavior prediction and management, planning and decision-making, and vehicle control functions. The DDS and IRT technologies described herein are vehicle oriented, modular, and customizable for each vehicle to meet the specific needs of each individual vehicle as an on-demand and dynamic service. The DDS is configured to provide customized, on-demand, and dynamic IRT resources and functions to individual CAVs to supplement the CAV's sensing, transportation behavior prediction and management, planning and decision-making, and/or vehicle control.

First claim

We claim:

1. A distributed driving system (DDS) comprising:
a) a plurality of connected and automated vehicles (CAVs), each one of the plurality of CAVs comprising a vehicle onboard system configured to generate control instructions for automated driving of the CAV;
b) an intelligent roadside toolbox (IRT), wherein said IRT provides customized, on demand, and dynamic IRT resources to the plurality of CAVs for dynamic utility management (DUM); and
c) a communications media for transmitting data between said plurality of CAVs and said IRT,
wherein said IRT resources and functions assembled are configured to comprise one or more of computational resources, cloud resources, system security resources, backup and redundancy resources, sensing functions, transportation behavior prediction and management functions, planning and decision making functions, and vehicle control functions;
wherein the dynamic utility management is provided by a DUM software module configured to optimize use of resources by the plurality of CAVs at more than one vehicle intelligence level by assembling IRT resources and functions provided to the plurality of CAVs and balancing CAV onboard system costs.

2. The DDS of claim 1 , wherein the IRT provides resources and functions to avoid trajectory conflicts with other vehicles and/or to adjust vehicle route and/or trajectory for driving environments including snow, sleet, fog or other adverse weather or road conditions.

3. The DDS of claim 1 , wherein said IRT resources and functions are provided by a cloud.

4. The DDS of claim 1 , wherein said CAV onboard system costs comprise computation ability cost (C), number of computational units cost (NU), fuel consumption cost (P), and climate control cost (V).

5. The DDS of claim 1 , wherein said DUM software module is configured to identify a minimum of a cost function describing a cost to implement an automated driving system as a sum of functions providing positive values for computation ability cost (C), number of computational units cost (NU), fuel consumption cost (P), and climate control cost (V).

6. The DDS of claim 1 , wherein the IRT resources and functions improve safety and stability of individual CAVs by assembling IRT resources and providing IRT resources and functions to individual CAVs.

7. The DDS of claim 1 , configured to measure a performance of one of the plurality of CAVs according to an index describing a computational ability, an emission output, an energy consumption, and/or a comfort of a driver.

8. The DDS of claim 7 , wherein the computational ability comprises computation speed for sensing, prediction, decision-making, and/or control; (New) wherein the energy consumption comprises fuel economy and/or electricity economy; and the comfort of said driver comprises climate control and/or acceleration/deceleration of said CAV.

9. The DDS of claim 1 , wherein the IRT resources and functions supplement an individual CAV according to vehicle manufacturer designs to improve CAV performance.

10. The DDS of claim 1 , wherein said DDS is configured to provide supplemental resources and functions to one of the plurality of CAVs in response to a value of a vehicle cost function exceeding a threshold and/or in response to detecting a component, function, and/or service failure.

11. The DDS of claim 1 , wherein said IRT is configured to provide a customized service for vehicle manufacturers and/or driving services providers, said customized service comprising functions for remote control service, pavement condition detection, and/or pedestrian prediction.

12. The DDS of claim 1 , wherein said IRT is configured to receive information from a vehicle OBU, electronic stability program (ESP), and/or vehicle control unit (VCU).

13. The DDS of claim 1 , configured to determine CAV information and/or functional requirements based on a cost function describing a total cost to implement an automated driving system as a sum of functions for computation ability cost (C), number of computational units cost (NU), fuel consumption cost (P), climate control cost (V), and IRT cost (I);
wherein the DDS is further configured to identify an optimal minimum of said cost function; and send said information and/or functional requirements to the IRT for providing supplemental information and/or functions to a CAV, wherein the cost function is:
U=f 1 ( C )+ f 2 ( NU )+ f 3 ( P )+ f 4 ( V )+ f 5 ( I )

where U represents the total cost, f 1 (C) is a function describing the computation ability cost, f 2 (NU) is a function describing the computational units cost, f 3 (P) is a function describing the fuel consumption cost, f 4 (V) is a function describing the climate control cost, and, f 5 (I) is a function describing the IRT cost.

14. The DDS of claim 1 , configured to integrate sensor and/or driving environment information from different resources to provide integrated sensor and/or driving environment information and pass said integrated sensor and/or driving environment information to a prediction module.

15. The DDS of claim 1 , wherein said sensing comprises providing information in real-time, short-term, and/or long-term for transportation behavior prediction and management, planning and decision-making, and/or vehicle control.

16. The DDS of claim 1 , wherein said transportation behavior prediction and management functions predict a behavior of surrounding vehicles, pedestrians, bicycles, and/or other moving objects.

17. The DDS of claim 1 , wherein said planning and decision-making functions provide:
i) path planning comprising identifying and/or providing a detailed driving path at a microscopic level for automated driving of one of the plurality of CAVs;
ii) route planning comprising identifying and/or providing a route for automated driving of one of the plurality of CAVs;
iii) special condition planning comprising identifying and/or providing a detailed driving path at a microscopic level and/or a route for automated driving of one of the plurality of CAVs during special weather conditions or event conditions; and/or
iv) disaster solutions comprising identifying and/or providing a detailed driving path at a microscopic level and/or a route for automated driving of one of the plurality of CAVs during a disaster.

18. The DDS of claim 1 , wherein said vehicle control functions are configured to determine a computation resource supporting automated driving of one of the plurality of CAVs and request and/or provide supplemental computation resources from said IRT.

19. The DDS of claim 1 , configured to provide system security and backup, vehicle performance optimization, computing and management, and dynamic utility management for one of the plurality of CAVs.

20. The DDS of claim 1 , configured to provide system backup and redundancy services for the plurality of CAV including:
a backup and/or supplemental sensing devices for the plurality of CAVs requiring sensing support; and/or
b) backup and/or supplemental computational resources for the plurality of CAVs to maintain CAV performance levels.

Assignees

• Cavh Llc

Inventors

• Ran Bin
• Cheng Yang
• Li Shen
• Tian Kexin
• Chen Tianyi
• Dong Shuoxuan
• Shi Kunsong
• Shi Haotian
• Li Xiaotian

Classifications

• G08G1/096708

  where the received information might be used to generate an automatic action on the vehicle control · CPC title

• G08G1/0145

  for active traffic flow control · CPC title

• G08G1/0116

  from roadside infrastructure, e.g. beacons · CPC title

• G08G1/096827

  where the route is computed onboard · CPC title

• G08G1/096811

  where the route is computed offboard · CPC title