Scenario-Based Hybrid Model Predictive Design for Cooperative Adaptive Cruise Control in Mixed-Autonomy Environments
Scenario-Based Hybrid Model Predictive Design for Cooperative Adaptive Cruise Control in Mixed-Autonomy Environments
复制标题
DOI：
10.1109/cdc49753.2023.10383460
复制
发表时间：
2023-12
期刊：
影响因子：
--
通讯作者：
Sahand Mosharafian;Yajie Bao;Javad Mohammadpour
中科院分区：
文献类型：
--
作者：
Sahand Mosharafian;Yajie Bao;Javad Mohammadpour
提出了一种基于情景的混合模型预测控制(MPC)设计方法，用于混合自主交通环境中的协同自适应巡航控制(CACC)。不同于以往只考虑一种可能的不确定性实现的方法，该方法基于随人驾驶车辆(HV)和互联自动车辆(CAV)的相对位置变化的不确定性描述来考虑多个场景，对于每个场景，建立了具有自由跟随、制动、危险和换道四种运行模式的CAV控制的混合整数二次规划问题。每辆CAV的运行模式是基于它从其前身接收到的预测信息以及周围HV的预期行为来确定的。使用基于场景的MPC方法同时处理所有场景，以实现强大的CACC。在双车道混合自主交通系统中的仿真实验表明，与以往的离散混合随机(DHSA)MPC方法相比，基于场景的混合MPC方法显著减少了意外人驾驶车辆机动时对期望间隔策略和期望速度的偏差。
This paper presents a scenario-based hybrid model predictive control (MPC) design approach for cooperative adaptive cruise control (CACC) in mixed-autonomy traffic environments with uncertainties stemming from unexpected maneuvers of human-driven vehicles. Different from the past works that consider one possible realization of uncertainty, the proposed approach here considers multiple scenarios based on the uncertainty description that varies with the relative location of the human-driven vehicles (HVs) and the connected and automated vehicles (CAVs), For each scenario, a mixed integer quadratic programming problem is formulated for the control of CAVs with four operating modes, namely free following, braking, danger, and lane change. Each CAV's operating mode is determined based on the predictive information it receives from its predecessors and the anticipated behaviors of surrounding HVs. All the scenarios are handled simultaneously using the scenario-based MPC approach for a robust CACC. Simulations in a mixed-autonomy traffic system including two lanes demonstrate that the proposed scenario-based hybrid MPC approach significantly reduces deviations from the desired spacing policy and the desired velocity in the platoon during unexpected human-driven vehicle maneuvers, compared with the past work, particularly, a discrete hybrid stochastic (DHSA) MPC approach.