Human-Like Decision Making and Control for Autonomous Driving

Abstract

Predicting the behaviors of other road users is crucial to safe and intelligent decision-making for autonomous vehicles (AVs). However, most motion prediction models ignore the influence of the AV's actions and the planning module has to treat other agents as unalterable moving obstacles. To address this problem, this paper proposes an interactionaware motion prediction model that is able to predict other agents' future trajectories according to the ego agent's future plan, i.e., their reactions to the ego's actions. Specifically, we employ Transformers to effectively encode the driving scene and incorporate the AV's future plan in decoding the predicted trajectories. To train the model to accurately predict the reactions of other agents, we develop an online learning framework, where the ego agent explores the environment and collects other agents' reactions to itself. We validate the decision-making and learning framework in three highly interactive simulated driving scenarios. The results reveal that our decision-making method significantly outperforms the reinforcement learning methods in terms of data efficiency and performance. We also find that using the interaction-aware model can bring better performance than the non-interaction-aware model and the exploration process helps improve the success rate in testing.

BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving

“…When confronted with the prospect of another vehicle attempting to overtake, aggressive drivers may accelerate to impede the overtaking vehicle, while cautious drivers may reduce their speed slightly to facilitate safe passing. In addition, driver behavior on the road tends to exhibit a degree of predictability, persistence, and consistency (Hang, Lv, and Chen 2022;Schwarting et al 2019). For example, an individual who has recently violated the speed limit is likely to continue driving at high speeds as long as circumstances allow, while cautious drivers maintain their conservative driving strategy.…”