Risk-aware stochastic vehicle trajectory prediction with spatial-temporal interaction
Author(s)
Type
Journal Article
Abstract
Autonomous vehicles need to continuously analyse the driving context and establish a comprehensive understanding of the dynamic traffic environment. To ensure the safety and efficiency of their operations, it would be beneficial to have accurate predictions of surrounding vehicles’ future trajectories. AVs can adjust their motions proactively to improve road safety and comfort with such information. This paper proposes a novel approach to predict the future trajectories of interacting vehicles, through a model of potential spatial-temporal interactions. A unique kernel function that emphasises risk-awareness was developed to extract spatial dependencies. The established model was trained and evaluated with the publicly available Highway Drone Dataset and Intersection Drone Dataset. The performance of the developed model was assessed with eight state-of-the-art methods. An ablation study and safety analysis were also conducted to evaluate the proposed risk-awareness kernel function. Results show that the proposed model’s inference speed is over eight times faster than the commonly used LSTM-based models. It also achieves an improvement of over 8% in prediction accuracy when compared with the state-of-the-art model.
Date Issued
2025-01-16
Date Acceptance
2025-01-13
Citation
IEEE Open Journal of Intelligent Transportation Systems, 2025, 6, pp.37-48
ISSN
2687-7813
Publisher
IEEE
Start Page
37
End Page
48
Journal / Book Title
IEEE Open Journal of Intelligent Transportation Systems
Volume
6
Copyright Statement
© 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License.
License URL
Sponsor
Innovate UK
Identifier
10.1109/OJITS.2025.3530268
Grant Number
10063539
Publication Status
Published
Date Publish Online
2025-01-16