A distributionally robust stochastic optimization-based model predictive control with distributionally robust chance constraints for cooperative adaptive cruise control under uncertain traffic conditions
A distributionally robust stochastic optimization-based model predictive control with distributionally robust chance constraints for cooperative adaptive cruise control under uncertain traffic conditions
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DOI：
10.1016/j.trb.2020.05.001
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发表时间：
2020-08
影响因子：
6.8
通讯作者：
Shuaidong Zhao;Kuilin Zhang
中科院分区：
文献类型：
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作者：
Shuaidong Zhao;Kuilin Zhang
受互联自动车辆（CAV）技术的推动，本文提出了一种基于数据驱动优化的模型预测控制（MPC）建模框架，用于不确定交通条件下一系列 CAV 的协作自适应巡航控制（CACC）。所提出的基于数据驱动优化的 MPC 建模框架旨在使用车对车（V2V）数据提高不确定交通条件下涉及一系列 CAV 的纵向协作自动驾驶的稳定性、鲁棒性和安全性。基于基于在线学习的驾驶动态预测模型，我们预测受控 CAV 之前车辆的不确定驾驶状态。通过预测前车的驾驶状态，我们解决了约束有限视野最优控制问题，以预测受控 CAV 的不确定驾驶状态。为了在不确定性下获得 CAV 的最佳加速或减速命令，我们制定了具有分布鲁棒机会约束（DRCC）的分布鲁棒随机优化（DRSO）模型（即力矩边界下数据驱动优化模型的特殊情况）。预测的前车和受控 CAV 的不确定驾驶状态将用于 DRSO-DRCC 模型的安全约束和参考驾驶状态。为了解决DRSO-DRCC模型的极小极大规划，我们基于强对偶理论和半定松弛技术，将松弛对偶问题重新表述为原始DRSO-DRCC模型的半定规划（SDP）。此外，我们提出了两种解决松弛 SDP 问题的方法。我们使用下一代仿真（NGSIM）数据在数值实验中演示所提出的模型。实验结果和分析表明，通过适当的设置，包括驾驶动力学预测模型、预测范围长度和车头时距，该模型可以获得稳定、鲁棒、安全的CAV纵向协同自动驾驶控制。进行计算分析以验证所提出的方法在适当设置下求解实时自动驾驶应用的 DRSO-DRCC 模型的效率。
Motivated by connected and automated vehicle (CAV) technologies, this paper proposes a data-driven optimization-based Model Predictive Control (MPC) modeling framework for the Cooperative Adaptive Cruise Control (CACC) of a string of CAVs under uncertain traffic conditions. The proposed data-driven optimization-based MPC modeling framework aims to improve the stability, robustness, and safety of longitudinal cooperative automated driving involving a string of CAVs under uncertain traffic conditions using Vehicle-to-Vehicle (V2V) data. Based on an online learning-based driving dynamics prediction model, we predict the uncertain driving states of the vehicles preceding the controlled CAVs. With the predicted driving states of the preceding vehicles, we solve a constrained Finite-Horizon Optimal Control problem to predict the uncertain driving states of the controlled CAVs. To obtain the optimal acceleration or deceleration commands for the CAVs under uncertainties, we formulate a Distributionally Robust Stochastic Optimization (DRSO) model (i.e. a special case of data-driven optimization models under moment bounds) with a Distributionally Robust Chance Constraint (DRCC). The predicted uncertain driving states of the immediately preceding vehicles and the controlled CAVs will be utilized in the safety constraint and the reference driving states of the DRSO-DRCC model. To solve the minimax program of the DRSO-DRCC model, we reformulate the relaxed dual problem as a Semidefinite Program (SDP) of the original DRSO-DRCC model based on the strong duality theory and the Semidefinite Relaxation technique. In addition, we propose two methods for solving the relaxed SDP problem. We use Next Generation Simulation (NGSIM) data to demonstrate the proposed model in numerical experiments. The experimental results and analyses demonstrate that the proposed model can obtain string-stable, robust, and safe longitudinal cooperative automated driving control of CAVs by proper settings, including the driving-dynamics prediction model, prediction horizon lengths, and time headways. Computational analyses are conducted to validate the efficiency of the proposed methods for solving the DRSO-DRCC model for real-time automated driving applications within proper settings.