用因果推理提升自动驾驶轨迹预测准确性和鲁棒性
Beyond Patterns: Harnessing Causal Logic for Autonomous Driving Trajectory Prediction
- 通过分解环境为时空成分，识别并消除虚假相关性
- 在五个真实数据集上实现RMSE和FDE指标显著优于现有方法
- 适合关注自动驾驶可靠性与可解释性的研究者和工程师
准确的轨迹预测长期以来是自动驾驶（AD）的一大挑战。传统数据驱动模型主要依赖统计相关性，常忽略交通行为背后的因果关系。本文提出一种新颖的轨迹预测框架，利用因果推断提升预测的鲁棒性、泛化能力和准确性。通过将环境分解为空间与时间组件，该方法识别并缓解虚假相关性，揭示真实的因果关系。同时采用渐进式融合策略整合多模态信息，模拟人类推理过程，支持实时推理。在五个真实世界数据集——ApolloScape、nuScenes、NGSIM、HighD 和 MoCAD——上的评估表明，本模型在关键指标如RMSE和FDE上均优于现有最先进（SOTA）方法。研究结果凸显了因果推理在转变轨迹预测中的潜力，为构建稳健的自动驾驶系统铺平道路。
原文摘要 · Abstract (English)
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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