動的交通環境における自動運転車のための二層型インタラクション意思決定アルゴリズム
BIDA: A Bi-level Interaction Decision-making Algorithm for Autonomous Vehicles in Dynamic Traffic Scenarios
自動運転車の意思決定において、対話型モンテカルロ木探索と深層強化学習を組み合わせた二層型アルゴリズムを提案し、動的な交通シナリオでの安全性と効率性を向上させた。
詳しい要約
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著者: Liyang Yu, Tianyi Wang, Junfeng Jiao, Fengwu Shan, Hongqing Chu, Bingzhao Gao
分類: cs.RO, cs.AI, cs.ET, cs.LG, cs.SY, eess.SY
原文アブストラクト
In complex real-world traffic environments, autonomous vehicles (AVs) need to interact with other traffic participants while making real-time and safety-critical decisions accordingly. The unpredictability of human behaviors poses significant challenges, particularly in dynamic scenarios, such as multi-lane highways and unsignalized T-intersections. To address this gap, we design a bi-level interaction decision-making algorithm (BIDA) that integrates interactive Monte Carlo tree search (MCTS) with deep reinforcement learning (DRL), aiming to enhance interaction rationality, efficiency and safety of AVs in dynamic key traffic scenarios. Specifically, we adopt three types of DRL algorithms to construct a reliable value network and policy network, which guide the online deduction process of interactive MCTS by assisting in value update and node selection. Then, a dynamic trajectory planner and a trajectory tracking controller are designed and implemented in CARLA to ensure smooth execution of planned maneuvers. Experimental evaluations demonstrate that our BIDA not only enhances interactive deduction and reduces computational costs, but also outperforms other latest benchmarks, which exhibits superior safety, efficiency and interaction rationality under varying traffic conditions.
