Beyond Patterns: Harnessing Causal Logic for Autonomous Driving Trajectory Prediction

• URL: http://arxiv.org/abs/2505.06856v1
• Date: Sun, 11 May 2025 05:56:07 GMT
• Title: Beyond Patterns: Harnessing Causal Logic for Autonomous Driving Trajectory Prediction
• Authors: Bonan Wang, Haicheng Liao, Chengyue Wang, Bin Rao, Yanchen Guan, Guyang Yu, Jiaxun Zhang, Songning Lai, Chengzhong Xu, Zhenning Li,
• Abstract summary: We introduce a novel trajectory prediction framework that leverages causal inference to enhance predictive robustness, generalization, and accuracy.<n>Our findings highlight the potential of causal reasoning to transform trajectory prediction, paving the way for robust autonomous driving systems.
• Score: 10.21659221112514
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate trajectory prediction has long been a major challenge for autonomous driving (AD). Traditional data-driven models predominantly rely on statistical correlations, often overlooking the causal relationships that govern traffic behavior. In this paper, we introduce a novel trajectory prediction framework that leverages causal inference to enhance predictive robustness, generalization, and accuracy. By decomposing the environment into spatial and temporal components, our approach identifies and mitigates spurious correlations, uncovering genuine causal relationships. We also employ a progressive fusion strategy to integrate multimodal information, simulating human-like reasoning processes and enabling real-time inference. Evaluations on five real-world datasets--ApolloScape, nuScenes, NGSIM, HighD, and MoCAD--demonstrate our model's superiority over existing state-of-the-art (SOTA) methods, with improvements in key metrics such as RMSE and FDE. Our findings highlight the potential of causal reasoning to transform trajectory prediction, paving the way for robust AD systems.

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