Causal-Planner: Causal Interaction Disentangling with Episodic Memory Gating for Autonomous Planning

Abstract

Autonomous vehicle trajectory planning faces significant challenges in dynamic traffic environments due to the complex and mixed causal relationships between critical scene elements (e.g., pedestrians, vehicles, road markings) and safe decision-making. To identify the causal factors influencing planning outcomes, we propose Causal-Planner, which disentangles the scene interaction graph into causal and confounding components via attention-based adversarial graph learning. Additionally, we introduce a long-short-term episodic memory gating (LSTEM) module that enhances causal interaction disentangling by adaptively capturing evolving causal relationships in dynamic scenarios through bidirectional gated memory fusion. Extensive experiments on the nuPlan dataset suggest that Causal-Planner achieves competitive performance, performing well in both Test-random and Test-hard scenarios under open-loop and closed-loop evaluations. The code will be publicly available at https://github.com/Yyb-XJTU/Causal-Planner.