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首页> 外文期刊>International Journal of Vehicle Information and Communication Systems >Reinforcement learning of dynamic collaborative driving. Part I: longitudinal adaptive control
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Reinforcement learning of dynamic collaborative driving. Part I: longitudinal adaptive control

机译：加强动态协作驾驶的学习。第一部分：纵向自适应控制

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Dynamic collaborative driving involves the motion coordination of multiple vehicles using shared information from vehicles instrumented to perceive their surroundings in order to improve road usage and safety. A basic requirement of any vehicle participating in dynamic collaborative driving is longitudinal control. Without this capability, higher-level coordination is not possible. Each vehicle involved is a composite non-linear system powered by an internal combustion engine, equipped with automatic transmission, rolling on rubber tyres with a hydraulic braking system. This paper focuses on the problem of longitudinal motion control. A longitudinal vehicle model is introduced which serves as the control system design platform. A longitudinal adaptive control system that uses Monte Carlo Reinforcement Learning (RL) is introduced. The results of the RL phase and the performance of the adaptive control system for a single automobile, as well as the performance in a multi-vehicle platoon, are presented.
机译：动态协作驾驶涉及多个车辆的运动协调，这些车辆使用来自被检测以感知周围环境的车辆的共享信息来改善道路使用和安全性。任何参与动态协作驾驶的车辆的基本要求是纵向控制。没有此功能，就无法进行更高级别的协调。涉及的每辆车都是由内燃机提供动力的复合非线性系统，配有自动变速器，可通过液压制动系统在橡胶轮胎上滚动。本文着重于纵向运动控制问题。引入了纵向车辆模型，该模型用作控制系统设计平台。介绍了一种使用蒙特卡洛强化学习（RL）的纵向自适应控制系统。给出了RL阶段的结果，单个汽车的自适应控制系统的性能以及多车排的性能。

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