The majority of the past research dealing with lane-changing controller design of autonomous vehicles (s) is based on the assumption of full knowledge of the model dynamics of the and the surrounding vehicles. However, in the real world, this is not a very realistic assumption as accurate dynamic models are difficult to obtain. Also, the dynamic model parameters might change over time due to various factors. Thus, there is a need for a learning-based lane change controller design methodology that can learn the optimal control policy in real time using sensor data. In this paper, we have addressed this need by introducing an optimal learning-based control methodology that can solve the real-time lane-changing problem of s, where the input-state data of the is utilized to generate a near-optimal lane-changing controller by approximate/adaptive dynamic programming (ADP) technique. In the case of this type of complex lane-changing maneuver, the lateral dynamics depend on the longitudinal velocity of the vehicle. If the longitudinal velocity is assumed constant, a linear parameter invariant model can be used. However, assuming constant velocity while performing a lane-changing maneuver is not a realistic assumption. This assumption might increase the risk of accidents, especially in the case of lane abortion when the surrounding vehicles are not cooperative. Thus, in this paper, the dynamics of the are assumed to be a linear parameter-varying system. Thus we have two challenges for the lane-changing controller design: parameter-varying, and unknown dynamics. With the help of both gain scheduling and ADP techniques combined, a learning-based control algorithm that can generate a near-optimal lane-changing controller without having to know the accurate dynamic model of the is proposed. The inclusion of a gain scheduling approach with ADP makes the controller applicable to non-linear and/or parameter-varying dynamics. The stability of the learning-based gain scheduling controller has also been rigorously proved. Moreover, a data-driven lane-changing decision-making algorithm is introduced that can make the perform a lane abortion if safety conditions are violated during a lane change. Finally, the proposed learning-based gain scheduling controller design algorithm and the lane-changing decision-making methodology are numerically validated using MATLAB, SUMO simulations, and the NGSIM dataset.

中文翻译：

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混合交通中的自动变道控制：自适应动态规划方法


过去大多数涉及自动驾驶车辆变道控制器设计的研究都是基于充分了解自动驾驶车辆和周围车辆的模型动力学的假设。然而，在现实世界中，这不是一个非常现实的假设，因为很难获得准确的动态模型。此外，由于各种因素，动态模型参数可能会随着时间而变化。因此，需要一种基于学习的车道变换控制器设计方法，可以使用传感器数据实时学习最优控制策略。在本文中，我们通过引入一种基于最优学习的控制方法来满足这一需求，该方法可以解决 s 的实时换道问题，其中 s 的输入状态数据用于生成接近最优的车道。通过近似/自适应动态规划（ADP）技术改变控制器。在这种复杂的变道操作中，横向动力学取决于车辆的纵向速度。如果假设纵向速度恒定，则可以使用线性参数不变模型。然而，在执行变道操作时假设速度恒定并不是一个现实的假设。这种假设可能会增加发生事故的风险，特别是在周围车辆不配合而导致车道中止的情况下。因此，在本文中，假设系统的动力学是一个线性参数变化系统。因此，我们的车道变换控制器设计面临两个挑战：参数变化和未知的动态。 借助增益调度和 ADP 技术相结合，提出了一种基于学习的控制算法，该算法可以生成接近最优的换道控制器，而无需知道其精确的动态模型。 ADP 中包含增益调度方法使得控制器适用于非线性和/或参数变化的动态。基于学习的增益调度控制器的稳定性也得到了严格的证明。此外，引入了数据驱动的换道决策算法，如果在换道过程中违反安全条件，则可以执行车道中止。最后，使用 MATLAB、SUMO 仿真和 NGSIM 数据集对所提出的基于学习的增益调度控制器设计算法和换道决策方法进行了数值验证。