Title: BIDA: A Bi-level Interaction Decision-making Algorithm for Autonomous Vehicles in Dynamic Traffic Scenarios Show Chinese title

Title: BIDA：动态交通场景下自动驾驶车辆的双层交互决策算法

Abstract: 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. Show Chinese abstract

Abstract: 在复杂的现实交通环境中，自动驾驶车辆（AVs）需要在实时做出安全关键决策的同时与其它交通参与者进行交互。 人类行为的不可预测性带来了重大挑战，特别是在动态场景下，比如多车道高速公路和无信号灯控制的T字路口。 为了解决这一问题，我们设计了一种双层交互决策算法（BIDA），该算法结合了交互式蒙特卡洛树搜索（MCTS）和深度强化学习（DRL），旨在提高自动驾驶车辆在动态关键交通场景中的交互合理性、效率和安全性。 具体来说，我们采用了三种类型的DRL算法来构建可靠的值网络和策略网络，这些网络通过辅助价值更新和节点选择来指导交互式MCTS的在线推理过程。 然后，在CARLA中设计并实现了动态轨迹规划器和轨迹跟踪控制器，以确保计划操作的平稳执行。 实验评估表明，我们的BIDA不仅增强了交互推理能力并减少了计算成本，而且在各种交通条件下表现出比其他最新基准更高的安全性、效率和交互合理性。