Autonomous navigation at unsignalized intersections: A coupled reinforcement learning and model predictive control approach
Autonomous navigation at unsignalized intersections: A coupled reinforcement learning and model predictive control approach
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DOI：
10.1016/j.trc.2022.103662
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发表时间：
2022-04-21
影响因子：
8.3
通讯作者：
Di, Xuan
中科院分区：
文献类型：
--
作者：
Bautista-Montesano, Rolando;Galluzzi, Renato;Di, Xuan
本文提出了一种集成的安全增强强化学习（RL）和模型预测控制（MPC）框架，用于自动驾驶汽车（AV）在无信号交叉口导航。研究人员已经广泛研究了自动驾驶汽车如何在沿着高速公路行驶。尽管如此，无人驾驶汽车如何在城市环境中导航十字路口仍然是一项具有挑战性的任务，因为不断存在移动的道路使用者，包括转弯车辆，穿越或乱穿马路的行人和骑自行车的人。因此，自动驾驶汽车需要学习和适应动态变化的城市交通环境。本文提出了一个设计基准，允许自动驾驶汽车感测实时交通环境和执行路径规划。代理动态生成可行路径的曲线。自我载体试图在特定的约束下遵循这些路径。RL和MPC导航算法并行运行，并适当选择以增强自我车辆安全性。自我AV建模与横向和纵向动态和训练在T形交叉口使用双延迟深度确定性策略梯度（TD3）算法在各种交通场景下。然后在直道和单车道或多车道交叉口进行测试。所有这些实验在避免碰撞、驾驶效率、舒适性和跟踪精度方面都取得了理想的结果。开发的AV导航系统提供了一个自适应AV，可以导航无信号交叉口的设计基准。
This paper develops an integrated safety-enhanced reinforcement learning (RL) and model predictive control (MPC) framework for autonomous vehicles (AVs) to navigate unsignalized intersections. Researchers have extensively studied how AVs drive along highways. Nonetheless, how AVs navigate intersections in urban environments remains a challenging task due to the constant presence of moving road users, including turning vehicles, crossing or jaywalking pedestrians, and cyclists. AVs are thus required to learn and adapt to a dynamically evolving urban traffic environment. This paper proposes a design benchmark that allows AVs to sense the real-time traffic environment and perform path planning. The agent dynamically generates curves for feasible paths. The ego vehicle attempts to follow these paths under specific constraints. RL and MPC navigation algorithms run in parallel and are suitably selected to enhance ego vehicle safety. The ego AV is modeled with lateral and longitudinal dynamics and trained in a T-intersection using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm under various traffic scenarios. It is then tested on a straight road and a single or multi-lane intersections. All these experiments achieve desirable outcomes in terms of crash avoidance, driving efficiency, comfort, and tracking accuracy. The developed AV navigation system provides a design benchmark for an adaptive AV that can navigate unsignalized intersections.