Beyond Patterns: Harnessing Causal Logic for Autonomous Driving Trajectory Prediction

TL;DR

This paper presents a causal inference-based framework for autonomous driving trajectory prediction that improves robustness, accuracy, and generalization by uncovering genuine causal relationships and integrating multimodal data.

Contribution

It introduces a novel causal inference approach that decomposes environment data and employs progressive fusion, surpassing traditional correlation-based models in trajectory prediction.

Findings

Outperforms state-of-the-art methods on five real-world datasets.

Achieves significant improvements in RMSE and FDE metrics.

Demonstrates the effectiveness of causal reasoning in autonomous driving.

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.