Real‐time control of connected vehicles in signalized corridors using pseudospectral convex optimization

Yang Shi, Zhenbo Wang, T. Laclair, C. Wang, Y. Shao
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引用次数: 2

Abstract

Recent advances in Connected and Automated Vehicle (CAV) technologies have opened up new opportunities to enable safe, efficient, and sustainable transportation systems. However, developing reliable and rapid speed control algorithms in highly dynamic environments with complex inter‐vehicle interactions and nonlinear vehicle dynamics is still a daunting task. In this paper, we develop a novel speed control method for CAVs to produce optimal speed profiles that minimize the fuel consumption and avoid idling at signalized intersections. To this end, an optimal control problem is formulated using the information of the upcoming traffic signal to adapt vehicles' speeds to avoid frequent stop‐and‐go driving patterns. By applying the pseudospectral discretization method and the sequential convex programming method, the computational efficiency is greatly improved, enabling potential real‐time on‐vehicle applications. In addition, the algorithm is implemented under a model predictive control framework to ensure online control with instant response for collision avoidance and robust vehicle coordination. The proposed algorithm is verified through numerical simulations of three different traffic scenarios. The convergence and accuracy of the proposed approach are demonstrated by comparing with a popular nonlinear solver. Furthermore, the benefit of the proposed method in both traffic mobility and fuel efficiency is validated using the speed profile determined from a traffic following model in a simulation software as the baseline.
基于伪谱凸优化的信号通道互联车辆实时控制
互联和自动驾驶汽车(CAV)技术的最新进展为实现安全、高效和可持续的交通系统开辟了新的机遇。然而,在具有复杂的车辆间相互作用和非线性车辆动力学的高动态环境中开发可靠和快速的速度控制算法仍然是一项艰巨的任务。在本文中,我们开发了一种新的自动驾驶汽车速度控制方法,以产生最优的速度曲线,使燃油消耗最小化,并避免在信号交叉口空转。为此,利用即将到来的交通信号信息制定了一个最优控制问题,以适应车辆的速度,以避免频繁的走走停停的驾驶模式。通过应用伪谱离散化方法和序列凸规划方法,大大提高了计算效率,实现了潜在的车载实时应用。此外,该算法在模型预测控制框架下实现,以确保在线控制具有避碰即时响应和鲁棒车辆协调。通过三种不同交通场景的数值模拟验证了该算法的有效性。通过与常用的非线性解算器的比较,证明了该方法的收敛性和准确性。此外,利用仿真软件中的交通跟随模型确定的速度曲线作为基线,验证了所提出方法在交通机动性和燃油效率方面的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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