This paper studies cooperative adaptive cruise control (CACC) for vehicle platoons with consideration of the unknown nonlinear vehicle dynamics that are normally ignored in the literature. A unified data-driven CACC design is proposed for platoons of pure automated vehicles (AVs) or of mixed AVs and human-driven vehicles (HVs). The CACC leverages online-collected sufficient data samples of vehicle accelerations, spacing and relative velocities. The data-driven control design is formulated as a semidefinite program (SDP) that can be solved efficiently using off-the-shelf solvers. The efficacy and advantage of the proposed CACC are demonstrated through a comparison with the classic adaptive cruise control (ACC) method on a platoon of pure AVs and a mixed platoon under a representative aggressive driving profile.

翻译：本文研究考虑车辆队列协同自适应巡航控制（CACC）问题，重点关注现有文献通常忽略的未知非线性车辆动力学特性。针对纯自动驾驶车辆（AV）队列及自动驾驶与人类驾驶车辆（HV）混合队列，提出了一种统一的数据驱动CACC设计方案。该方案利用在线采集的充分车辆加速度、间距及相对速度数据样本，将数据驱动控制设计表述为半定规划（SDP）问题，可直接使用现成求解器高效求解。通过与经典自适应巡航控制（ACC）方法在纯自动驾驶队列及代表性激进驾驶场景下的混合队列对比，验证了所提CACC方法的有效性与优越性。