Beyond Patterns: Harnessing Causal Logic for Autonomous Driving Trajectory Prediction
This addresses the problem of accurate trajectory prediction for autonomous driving systems, representing a novel method for a known bottleneck rather than an incremental improvement.
The paper tackled the challenge of trajectory prediction in autonomous driving by introducing a causal inference framework to enhance robustness, generalization, and accuracy, demonstrating superiority over SOTA methods with improvements in metrics like RMSE and FDE across five real-world datasets.
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.