汽车电子研究中心

Research Direction

Based on the development of the intelligent driving vehicles and the related key technologies, systematic interdisciplinary research has been carried out from the communications security, laser radar, machine vision, active safety, energy saving such aspects.
1.Perception
Research on the information fusion technology of the vehicle radar and machine vision and new technology of space 3D information construction in visual detection and measurement technology
2.Autonomous driving decision
Based on the framework of deep reinforcement learning, to construct the automatic driving strategy learning model and to improve the training efficiency using parallel computing.
3.Car network communication security
Research on the encryption and decryption, identity authentication and key distribution of vehicle network access, safe driving, billing management, traffic management and data communication.
4.Energy-saving driving
Through the path planning and speed adjustment to achieve energy-saving, combined with large traffic data, so as to achieve energy conservation from the city level.

Evolution towards Optimal Driving Strategies for Large-scale Autonomous Vehicles

With the popularity of intelligent connected vehicles, the ability of single vehicle intelligence to optimize the overall road network is limited. Moreover, the urban traffic system is faced with problems of large volume of traffic, complex information interaction and many traffic objects, so it is necessary to improve and coordinate the driving strategies of group vehicles. With the vehicle-to-vehicle communication and the cooperative vehicle infrastructure system, the Nash equilibrium among vehicles in the neighborhood is studied by combining game theory and the multi-objective optimization algorithm, aiming at the process of multi-vehicle interactive decision-making. At the same time, the idea of co-evolution is introduced to explore the interaction between different strategies, coordinate multiple optimization objectives, observe the evolution process of different population distribution, and obtain the optimal group strategy, so as to solve the problem of multi-objective optimization of urban traffic.

Intellectual Properties

Selected Papers

Runsong Jiang; Zhangjie Liu; Huiyun Li. Evolution towards Optimal Driving Strategies for Large-scale Autonomous Vehicles, IET Intelligent Transport Systems, 2021, SCI二区.
Huang, Hui; Li, Huiyun; Shao, Cuiping; Sun, Tianfu; Fang, Wenqi; Dang, Shaobo. Data Redundancy Mitigation in V2X Based Collective Perceptions, IEEE ACCESS, 2020, SCI一区.
刘章杰;李慧云.基于多目标协同演化算法的大规模自动驾驶策略,集成技术,2020.
Zhangjie Liu; Huiyun Li. Optimal Driving Policies Emergency for Large-scale Autonomous Vehicles based on Multi-objective Co-evolutionary Algorithms, Journal of Integration Technology, 2020.

Selected Patents

A Decision Emergence Method for Autonomous Vehicles Based on Co-evolution, patent for invention, 2020-01-20.
The invention relates to a method and device for generating decision network model for vehicle autonomous driving, patent for invention, 2017-03-30.

Shenzhen Institutes of Advanced Technology , Chinese Academy of Sciences —— Center for Automotive Electronics

Shenzhen Institutes of Advanced Technology , Chinese Academy of Sciences
—— Center for Automotive Electronics