Lab of Intelligent and Connected Vehicles

Intelligent Decision - Prediction of Intention and Motion

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 a 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. Pedestrian Trajectory Prediction Based on Intention and Behavior Identification

  • Research Background

Pedestrian trajectory prediction plays an important role in vehicle anti-collision protection system and automatic driving. At present, the researches of pedestrian trajectory prediction are mainly divided into three categories, which are based on motion models, pedestrian intention identification and learning methods. However, these methods always neglect pedestrian behaviors, and the motion models are not adaptive to pedestrian's unique characteristics. Combined with the complex behaviors of pedestrian, a new pedestrian motion model is proposed to identify the pedestrian's intention, and finally integrate the behavior and intention to obtain good prediction results, which is of great significance to improve the safety of automatic driving.

  • Research Achievements

To get accurate prediction results, a pedestrian trajectory prediction method based on pedestrian's behavior and intention is proposed. Pedestrian’s behaviors are identified into standing, walking and running, through 18 captured key points. The result shows its adaptability for general pedestrian crossing scenarios. DBN is used to obtain pedestrian’s crossing intentions with all parameters obtained from empirical data and announced data for general crossing scenarios. The trajectory prediction method combines the behavior and the intention results together and performs well in defined 8 typical pedestrian crossing scenarios in our provided BPI dataset, especially for stopping scenarios. The results show that the method can accurately predict the pedestrian trajectory for general pedestrian crossing scenarios without training of pedestrian trajectory.

  • Leading research member: WU Haoran
Created: Mar 05, 2021 | 09:51