Intersection trajectory prediction by integrating graph neural networks and candidate lane intent probabilities

Chuanying Zhang; Guoyan Xu; Zhifa Chen; Lei Li; Bin Zhou

doi:10.1117/12.3031085

19 July 2024 Intersection trajectory prediction by integrating graph neural networks and candidate lane intent probabilities

Chuanying Zhang, Guoyan Xu, Zhifa Chen, Lei Li, Bin Zhou

Author Affiliations +

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131812Z (2024) https://doi.org/10.1117/12.3031085
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

In intersection scenarios, agents exhibit diverse intent choices, making trajectory prediction problems fraught with significant uncertainty. This study proposes a cross-intersection trajectory prediction method that considers the probability of agent intent. It integrates a vehicle speed model, an intent predictor based on agent kinematics, and a trajectory prediction method based on graph neural networks to enhance the accuracy of vehicle agent trajectory predictions by precisely capturing the intent of vehicle agents at intersections. Through training and validation on a large dataset of real-world driving data, experiments have demonstrated the method's capability to predict the behavior of traffic agents in intersection scenarios accurately. Specific experimental results on the nuScenes dataset show MinADE_5, MinADE_10, MissRate_5,2, and MissRate_10,2 values of 1.70, 1.45, 0.63, and 0.48, respectively.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chuanying Zhang, Guoyan Xu, Zhifa Chen, Lei Li, and Bin Zhou "Intersection trajectory prediction by integrating graph neural networks and candidate lane intent probabilities", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131812Z (19 July 2024); https://doi.org/10.1117/12.3031085

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