BIDA让自动驾驶车在复杂路况下更安全高效地互动决策
BIDA: A Bi-level Interaction Decision-making Algorithm for Autonomous Vehicles in Dynamic Traffic Scenarios
- 分层交互决策框架融合蒙特卡洛树搜索与深度强化学习
- 实测在多种交通场景中提升安全性和决策效率
- 适合自动驾驶系统研发者和智能交通研究者参考
在复杂真实交通环境中，自动驾驶车辆需与其它交通参与者互动并实时做出安全关键决策。人类行为的不可预测性在多车道高速路和无信号灯十字路口等动态场景中带来显著挑战。为此，我们设计了双层交互决策算法（BIDA），将交互式蒙特卡洛树搜索（MCTS）与深度强化学习（DRL）结合，旨在提升自动驾驶车辆在动态关键交通场景中的交互合理性、效率与安全性。具体而言，采用三种DRL算法构建可靠的值网络与策略网络，指导交互MCTS的在线推演过程，辅助价值更新与节点选择。随后，在CARLA中设计并实现动态轨迹规划器与轨迹跟踪控制器，确保规划动作的平稳执行。实验评估表明，BIDA不仅提升了交互推理能力并降低了计算成本，还在多种交通条件下优于最新基准方法，展现出更优的安全性、效率与交互合理性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。