CW-ERM: Improving Autonomous Driving Planning with Closed-loop Weighted Empirical Risk Minimization

TL;DR

This paper introduces CW-ERM, a novel training method for autonomous driving policies that uses closed-loop evaluation to select important training samples, significantly improving safety and performance in urban driving scenarios.

Contribution

The paper proposes CW-ERM, a simple and effective approach that incorporates closed-loop evaluation into the training process to enhance policy robustness.

Findings

Significant reduction in collision rates.

Improved closed-loop driving metrics.

Enhanced policy robustness in urban environments.

Abstract

The imitation learning of self-driving vehicle policies through behavioral cloning is often carried out in an open-loop fashion, ignoring the effect of actions to future states. Training such policies purely with Empirical Risk Minimization (ERM) can be detrimental to real-world performance, as it biases policy networks towards matching only open-loop behavior, showing poor results when evaluated in closed-loop. In this work, we develop an efficient and simple-to-implement principle called Closed-loop Weighted Empirical Risk Minimization (CW-ERM), in which a closed-loop evaluation procedure is first used to identify training data samples that are important for practical driving performance and then we these samples to help debias the policy network. We evaluate CW-ERM in a challenging urban driving dataset and show that this procedure yields a significant reduction in collisions as well as other non-differentiable closed-loop metrics.