An interpretation framework for autonomous vehicles decision-making via SHAP and RF

Abstract

Decision-making for autonomous vehicles is critical to achieving safe and efficient autonomous driving. In recent years, deep reinforcement learning (DRL) techniques have emerged as the most promising way to enable intelligent decision-making. However, DRL with ‘black box’ nature is not widely understood by humans, thus hindering their social acceptance. In this paper, we combine SHapley Additive exPlanation (SHAP) and random forest (RF) techniques to bring transparency to decision-making obtained by DRL. Specifically, we first implement decision-making of autonomous vehicles following in discrete action space based on DRL algorithm with the goal of safety and efficiency. Then we use the SHAP technique to simplify the feature space, which shows that relative distance, longitudinal speed of the ego vehicle, and longitudinal speed of the proceeding vehicle have a critical impact on vehicle following task. Finally, we collect the state-action pairs generated by the DRL model and perform feature filtering, and fit the decision model with an interpretable RF model. The simulation results show that the RF model achieves the behavioral explanation of autonomous vehicle following.