Related Experiment Video
Updated: May 6, 2026
A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
Published on: August 26, 2018
Incorporating safety field theory into interactive trajectory prediction between VRU and vehicle: An integrated
Dianchen Zhu1, Zheyan Fan2, Wei Ma3
1School of Automobile and Traffic Engineering, Hefei University of Technology, Hefei, Anhui, China.
Objectives:
Intersections represent critical points where conflicts between vulnerable road users (VRUs) and vehicles often occur, posing significant safety challenges globally. Despite efforts to mitigate heterogeneous traffic individual conflict, the accurate trajectory prediction of VRU-vehicle interactions remains elusive due to asymmetric information, unequal risks, and uncertainties in decision-making behaviors. Most existing trajectory prediction models predominantly focus on either VRUs or vehicles, neglecting the complex mechanisms of interactions between heterogeneous traffic agents. This article proposes a trajectory prediction model that incorporates spatial-temporal characteristics and security risk awareness as an alternative approach to these challenges.
Methods:
The proposed spatial-temporal risk network (STRN) combines the awareness of time, space, and quantified safety risk to improve the performance of this model. First, the safety potential field theory is used to quantify and label the risks in the VRU-vehicle interaction scenario, and the effect of conflict risk on the agents' motion trajectory is considered. Second, the spatial constraint features of agent movement are extracted from the spatial dimension. Third, the change characteristics of the agent's motion trajectory over time are extracted from time dimension.
Results:
The experimental results show that the model can effectively identify the motion trajectories under the complex interaction between VRUs and vehicles. The ablation experiments confirm that the integration of spatial-temporal risk dimensions positively impacts the accuracy of interaction prediction. The model shows great robustness in different scenario transferability tests.
Conclusions:
This study revisits the challenge of predicting interaction trajectories of heterogeneous objects in unsignalized intersection scenarios, a topic that has not been extensively explored. The proposed STRN model, with its performance and robustness, provides a new scheme for improving the level of traffic safety and promoting intelligent autonomous vehicle decision system.
