Lab of Intelligent and Connected Vehicles

Intelligent Decision - Driving Risk Assessment

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. Comprehensive Assessment of Driving Risks Based on Driver-vehicle-road Characteristics

  • Research Achievements

Traffic accident statistics have shown the necessity of risk assessment when driving in a dynamic traffic environment. If the risk associated with different traffic elements (i.e., road, environment and vehicles) could be evaluated accurately, potential accidents could be significantly avoided or mitigated. Therefore, in view of the problem that it is difficult to assess the situation of complex road traffic environment, a unified driving environment situation assessment method of subjective/objective risks under the combined action of driver-vehicle-road is proposed, by referring to the equivalent force and field theory.

2. Behavioral Probabilistic Situation Awareness Based on Intention Identification

  • Research Achievements

Understanding the dynamic characteristics of surrounding vehicles and estimating the potential risk of mix traffic is of great help to safe and efficient autonomous driving. However, the existing risk assessment methods are insufficient in detecting dangerous situations in advance and tackling the uncertainty of mixed traffic. The research is to evaluate the risk of dynamic driving in consideration of the intention of surrounding vehicles, and output the probabilistic risk map to support the anthropomorphic decision-making of vehicles. Specifically, the intention recognition module can identify the intention possibility of surrounding vehicles, and the human-vehicle-road coupling model can reflect the coupling relationship among drivers, vehicles and roads by analyzing the interactions among them. Finally, a potential risk graph based on multi-vehicle interaction is formed and output to the autonomous decision module for real-time behavior decision under security constraints.

3. Risk Assessment Based on Vehicle Intention and Trajectory Prediction

  • Research Background

The traditional risk assessment method only considers the current driving conditions and environmental factors, and lacks consideration of the possible change of surrounding moving objects’ behaviors. If we can accurately identify the intentions of surrounding moving objects and predict their possible trajectories, then the risk assessment model based on the prediction information can more accurately represent the distribution of risk, thereby providing a better support for the decision after the risk assessment.

  • Research Achievements

In the acquisition process of prediction information, the method of deep learning is adopted. Compared with the traditional method of directly using vehicle position information and speed information as training data, the research first uses field model to describe this information and obtain a matrix that can reflect the whole information as input. This method improves the accuracy of prediction and adaptability to the scenario. Finally, a risk assessment model is established for the environment based on predicted information.


Leading Research Member: HUANG Heye ,TU Maoran

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
Created: Mar 05, 2021 | 10:03