Mitigating Causal Confusion in Vector-Based Behavior Cloning for Safer Autonomous Planning

Abstract

The utilization of vector-based deep learning techniques has great prospects in the realm of autonomous driving, particularly in the domains of prediction and planning tasks. However, the application of vector-based backbones for prediction and planning tasks may lead to the occurrence of causal confusion. Previous studies have explored the phenomenon of causal confusion, with a specific emphasis on the context of visual imitation learning. As for the vector-based model, we observe that the states of surrounding vehicles can be a nuisance shortcut. In our work, an off-policy approach is proposed to alleviate the issue by incorporating de-confounding supervision. Additionally, to better capture the environmental cues, such as route and traffic lights, in vectorized representation, a decoder utilizing iterative route fusion is devised. By incorporating auxiliary supervision and employing a dedicated decoder, we demonstrate the effectiveness of our methods in reducing causal confusion and improving performance in planning tasks through reactive and nonreactive closed-loop simulations on the nuPlan dataset.