Localized artificial intelligence for autonomous driving

Patent metadata

Field	Value
Publication number	US-12333944-B2
Application number	US-202418672739-A
Country	US
Kind code	B2
Filing date	May 23, 2024
Priority date	Jul 3, 2019
Publication date	Jun 17, 2025
Grant date	Jun 17, 2025

Abstract

The invention provides an autonomous vehicle (AV) system with an artificial intelligence (AI) system for automated vehicle control and traffic operations. This AI system comprises a computation component configured to provide sensing, behavior prediction and management, decision making, and vehicle control for the vehicle. This AI system is configured to receive local knowledge, information, data, and models from a roadside unit (RSU) or a cloud to improve performance and efficiency of the vehicle. The AI system is configured to train models with heuristic parameters obtained from a local traffic control center/traffic control unit (TCC/TCU) or the cloud to provide an improved model. The AI system is configured to provide intelligence coordination to distribute intelligence among vehicles, RSUs and cloud. The system also provides localized self-evolving artificial intelligence.

First claim

We claim:

1. An autonomous vehicle (AV) comprising an onboard unit (OBU), wherein said OBU comprises:
an artificial intelligence (AI) system for automated vehicle control and traffic operations, wherein said AI system comprises:
a) a database of accumulated historical data comprising background, vehicle, traffic, object, and/or environmental data for a localized area;
b) sensors configured to provide real-time data comprising background, vehicle, traffic, object, and/or environmental data for said localized area; and
c) a computation component configured to compare said real-time data and said accumulated historical data to provide sensing, behavior prediction and management, decision making, and vehicle control for the vehicle;
wherein the AI system is configured to provide proactive safety methods by predicting incidents and estimating risk; and
a data processing module configured to fuse data from data sources comprising vehicle sensors and/or roadside sensors.

2. The AV of claim 1 , wherein said AI system is configured to receive local knowledge, information, and data from a roadside unit (RSU) to improve performance and efficiency of the vehicle.

3. The AV of claim 2 , wherein said local information and data comprises local hardware and/or software configuration, learned algorithms, algorithm parameters, raw data, aggregated data, and data patterns.

4. The AV of claim 2 , wherein said RSU is configured to transmit learning methods for model localization to the OBU.

5. The AV of claim 4 , wherein said AI system is configured to train models with heuristic parameters obtained from a local traffic control center/traffic control unit (TCC/TCU) to provide an improved model.

6. The AV of claim 5 wherein the system trains models to provide improved models for a related task.

7. The AV of claim 5 wherein the system updates a previously trained model with heuristic parameters to provide an updated trained model.

8. The AV of claim 1 , wherein said AI system is configured to provide intelligence coordination to:
a) distribute intelligence among a plurality of RSUs and connected and automated vehicles to improve performance and robustness of automated vehicle control and traffic operations;
b) decentralize system control with self-organized control; and
c) divide labor and distribute tasks.

9. The AV of claim 8 wherein said intelligence coordination comprises use of swarm intelligence models.

10. The AV of claim 8 wherein said intelligence coordination is provided by direct interactions and indirect interactions among components of an Intelligent Road Infrastructure System (IRIS).

11. The AV of claim 1 , wherein said computation component is configured to implement a self-evolving algorithm.

12. The AV of claim 1 , wherein said localized area comprises a coverage area served by a roadside unit (RSU).

13. The AV of claim 1 , wherein said AI system is further configured to identify a plurality of high-risk locations, wherein a high-risk location is a location comprising an animal, a pedestrian, a traffic accident, unsafe pavement, and/or adverse weather.

14. The AV of claim 1 , wherein said AI system is configured to sense an environment and road in real time to acquire environmental and/or road data.

15. The AV of claim 1 , wherein said AI system is configured to predict a plurality of road and environmental conditions using said database of accumulated historical data and said real-time data, wherein said real-time data is provided by one or more AV sensors.

16. The AV of claim 1 , wherein said AI system is configured to detect objects on a road.

17. The AV of claim 1 , wherein said AI system is configured to detect objects on a roadside.

18. The AV of claim 1 , wherein said AI system is configured to predict object behavior.

19. The AV of claim 1 , wherein said AI system further comprises safety hardware and safety software to reduce a crash frequency and a crash severity.

20. The AV of claim 1 , wherein said AI system is configured to collect and share data from a plurality sources and provide data to RSUs and/or a cloud.

Assignees

• Cavh Llc
• Cavh

Inventors

• Ran Bin
• You Junwei
• Wu Keshu
• Cheng Yang
• Tang Weizhe
• Zheng Yuan
• Li Shen
• Dong Shuoxuan
• Chen Tianyi
• Li Xiaotian
• Zhang Zhen
• Zhou Yang

Classifications

• G08G1/165

  for passive traffic, e.g. including static obstacles, trees · CPC title

• G08G1/0141

  for traffic information dissemination · CPC title

• G08G1/0116 Primary

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

• G08G1/096783

  where the origin of the information is a roadside individual element · CPC title

• G08G1/166

  for active traffic, e.g. moving vehicles, pedestrians, bikes · CPC title