Longitudinal vehicle control models, such as Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC), have been the core component of many Automated Driving Systems (ADS) or Advanced Driver Assistance System (ADAS). ACC and CACC systems make vehicles drive with faster reaction time and smaller following distance than manual vehicles, alleviating the congestions and attenuating traffic disturbances. Many existing ACC and CACC control models focus on three strategies that can indirectly mitigate congestions and damp traffic shockwaves: time headway suppression, stability improvement, and adaptive mode-switching control based on traffic conditions. Although congestion mitigation and shockwave damping are achievable with those control strategies, the improvement is often limited to the by-product effect of their main control objectives of headway suppression and system stability. Many adaptive models also need to calibrate many parameters for different road conditions.

This paper proposes a new ACC and CACC framework that directly integrates the congestion shockwave damping and traffic congestion mitigation into the objective function of a dynamic control framework. A Model Predictive Control (MPC) based dynamic optimization framework is proposed to balance the trade-offs among multiple objectives, including safety, efficiency, shockwave, elasticity, and driver’s comfort. The proposed framework is applied in both ACC and CACC platoons mixed with manual vehicles, and five different control models are explored. The stability conditions of the proposed models are derived analytically and analyzed numerically. Three experimental studies, including platoon, ring-road, and traffic simulation studies, indicate promising results of proposed models in reducing shockwave propagation speed and mitigating traffic congestion under different environments compared with existing ACC and CACC models.

纵向车辆控制模型，例如自适应巡航控制（ACC）和协同自适应巡航控制（CACC），已经成为许多自动驾驶系统（ADS）或高级驾驶员辅助系统（ADAS）的核心组件。ACC 和 CACC 系统使车辆以比手动车辆更快的反应时间和更小的跟车距离行驶，缓解拥堵并减弱交通干扰。许多现有的 ACC 和 CACC 控制模型侧重于三种可以间接缓解拥堵和阻尼交通冲击波的策略：时间间隔抑制、稳定性改进和基于交通状况的自适应模式切换控制。尽管通过这些控制策略可以实现拥堵缓解和冲击波阻尼，改进通常仅限于其主要控制目标（车头时距抑制和系统稳定性）的副产品效应。许多自适应模型还需要针对不同的路况标定许多参数。

本文提出了一种新的 ACC 和 CACC 框架，将拥堵冲击波阻尼和交通拥堵缓解直接集成到动态控制框架的目标函数中。提出了一种基于模型预测控制 (MPC) 的动态优化框架，以平衡多个目标之间的权衡，包括安全性、效率、冲击波、弹性和驾驶员舒适性。所提出的框架应用于 ACC 和 CACC 与手动车辆混合的排，并探索了五种不同的控制模型。对所提出模型的稳定性条件进行了分析推导和数值分析。三项实验研究，包括队列、环路和交通模拟研究，