Lab of Intelligent and Connected Vehicles

Intelligent Decision - Autonomous Decision Making

Autonomous vehicles play a positive role in traffic safety, traffic efficiency, environmental protection and energy-saving. With the development of intelligent vehicles, how to interact with these dynamic, random and diversified traffic participants is potential challenge to realize high-level automatic driving in mixed traffic. Therefore, research on human-like decision-making system of autonomous driving is necessary. However, there remains some challenges as follows: 1) how to identify the intentions of surrounding traffic participants, instead of passively accelerating or braking according to the current state of the surrounding vehicles; 2) how to quantify the driving risks caused by different traffic factors in complex environment; 3) how to plan specific driving strategies and improve the decision-making ability in complex environments.

1. Decision-making and Motion Planning for Autonomous Vehicles

  • Research Achievements

In view of the problem that the existing intelligent decision-making system is difficult to adapt to the drivers in complex traffic environment and the integration of various driving assistance functions, an anthropomorphic decision-making strategy for the decision-making mechanism of self-learning is proposed based on the concept of "learner, simulator and transcendent". Through the analysis of the relationship of various influence factors of risk assessment, together with the function relationship among traffic elements and characteristics of mechanical driving system, the optimal solution to the action of the mechanical system is found enabling intelligent vehicles to adapt to the human personality without limitations in specific scenarios. A unified intelligent driving decision-making model is established by combining the physical mechanism of the driver's decision-making with the artificial intelligence method, and personalized driving decision-making and multi-objective collaborative control are realized based on the driver's feature recognition.

Leading Research Member: HUANG Heye

Representative Papers:

  1. Heye Huang, Yang Li, Xunjia Zheng, Jianqiang Wang*, Qing Xu, Sifa Zheng. Objective and Subjective Analysis to Quantify Influence Factors of Driving Risk. In IEEE Intelligent Transportation Systems Conference (ITSC), 2019.
  2. Heye Huang, Wenjun Liu, Xunjia Zheng, Qing Xu, Jianqiang Wang*.  Path Planning for Vehicle Obstacle Avoidance Based on Collaborative Perception. In International Conference on Green Intelligent Transportation Systems and Safety (GITSS), 2019.
  3. Xunjia Zheng, Heye Huang, Jianqiang Wang*, Xiaocong Zhao, Qing Xu*. Behavioral decision-making model of the intelligent vehicle based on driving risk assessment. Computer-aided Civil and Infrastructure Engineering, 2019.
  4. Jianqiang Wang, Xunjia Zheng, Heye Huang. Least Action Principle Followed by Driver’s Decision-making Mechanism [J]. China Journal of Highway and Transport, 2019.

 

2. Proactive Decision-Making based on Prediction and Interaction of Traffic Participants in Dynamic Environment

  • Research Background

Automated driving technologies have been successfully applied to multiple scenarios. However, while facing the challenges of urban conditions, traditional decision-making methods fall short in guaranteeing safety and efficiency in dynamic environment with other road users. This research targets at proactive decision-making in dynamic environment while considering the prediction and interaction with other traffic participants. It will contribute to the promotion of automated vehicle's intelligence as well as its application in urban area.

  • Research Achievements

Based on risk assessment with Safety-field and receding horizon planner, a proactive motion planner is firstly proposed considering intentions of other road users. Comparing with other proactive methods, this planner is more considerate in terms of the uncertainty of intention-awareness as well as other risk factors, generating a more human-like and safe trajectory. Current research continues to consider the interaction among agents. A novel motion planning method is under development based on game theory, and inverse reinforcement learning is also applied to study human’s behavior from naturalistic driving data.

With the algorithms getting more and more complicated, on-board processor develops higher requirement for computing power, energy consumption and volume. The research designs algorithm based on FPGA, taking its advantage of parallel computation, and tests its performance based on on-board application with PCI-e designed on Altera Stratix V.

 

Leading Research Member: CUI Mingyang

 

Created: Mar 05, 2021 | 10:17