自動運転の意思決定のための相互作用を考慮した運動予測モデルの学習
Learning Interaction-aware Motion Prediction Model for Decision-making in Autonomous Driving
自車の行動に対する他車の反応を予測するTransformerベースの運動予測モデルを提案し、オンライン学習フレームワークで訓練することで、自動運転の意思決定性能を向上させた。
著者: Zhiyu Huang, Haochen Liu, Jingda Wu, Wenhui Huang, Chen Lv
分類: cs.RO
原文アブストラクト
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 interaction-aware 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 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.
