智能网联车辆交通流优化对交通安全的改善

为改善常规驾驶车辆交通流追尾碰撞交通安全状况，提出智能网联车辆（Connected and Automated Vehicles，CAV）与常规车辆构成的混合交通流车队稳定性优化控制方法。基于全速度差模型，应用集成速度与加速度的多前车反馈构建CAV跟驰模型，考虑CAV混合交通流车辆空间分布的随机性，将各类型局部车队稳定性作为优化目标，以局部车队头车速度扰动为系统输入，以尾车速度扰动为系统输出，应用经典控制理论领域的传递函数法推导局部车队稳定性约束条件；分析关于平衡态速度与CAV反馈系数的车队稳定域，以各类型局部车队能够在任意平衡态速度下均稳定为控制目标，对CAV反馈系数输出进行优化控制；设计高速公路上匝道交通瓶颈数值仿真试验，在不同CAV比例等多种条件下，分析CAV混合交通流优化控制对交通流车辆追尾碰撞风险的影响。研究结果表明：CAV混合交通流优化控制可降低车辆追尾碰撞风险，在碰撞时间阈值小于2 s时，100%比例的CAV交通流可将交通流的车辆追尾碰撞风险降低85.81%以上；在碰撞时间阈值大于2 s时，追尾碰撞风险可降低48.22%～78.80%。所提优化控制方法可有效降低CAV车队优化控制的复杂性，为大规模CAV背景下的混合交通流优化控制以及车辆追尾碰撞交通安全提升策略提供直接理论参考。

Improving Traffic Safety via Traffic Flow Optimization of Connected and Automated Vehicles

To improve the traffic flow safety of regular driven vehicles in terms of the rear-end collision, a stability optimal control method for the mixed traffic flow consisting of connected and automated vehicles (CAVs) and regular vehicles is proposed. The car-following model of the CAV was built using multiple vehicle feedbacks integrating speeds and accelerations, based on the full velocity difference model. Considering the random spatial distribution of vehicles in the CAV mixed traffic flow, the stability of various types of local platoons was considered as the optimization objective. The speed disturbance of the head vehicle of a local platoon was considered as the system input, while the speed disturbance of the tail vehicle was considered to be the system output. The stability constraints of local platoons were derived using the transfer function method of a classical control theory. In addition, stability charts with respect to the equilibrium speeds and CAV feedback coefficients were analyzed. Subsequently, the outputs of the CAV feedback coefficients were optimized, with each type of the local platoons being stable for any equilibrium speed as the control target. Numerical simulations were designed at the bottleneck of the freeway with an on-ramp. Subsequently, the impacts of optimal control for the CAV mixed traffic flow on vehicle rear-end collision risks were analyzed, under various conditions such as different CAV proportions. Our results show that the optimal control for the CAV mixed traffic flow can decrease vehicle rear-end collision risks. For the case of traffic flow with 100% CAV, the rear-end collision risks of regular vehicles flow can be reduced by more than 85.81% when the time-to-collision threshold is less than 2 s, while it can be decreased by 48.22%-78.80% if the time-to-collision threshold is more than 2 s. The optimal control method presented herein can effectively reduce the complexity of optimal control for large-scale CAV platoons. The research results can provide direct and theoretical references for the mixed traffic flow optimal control and strategies for improving vehicle rear-end collision safety under the background of a large-scale CAV flow.