基于行车安全场模型的交叉口车辆控制算法

为了提高网联环境无信号交叉口自动驾驶车辆的行车安全与通行效率问题，首先，建立无信号交叉口的行车安全场模型，构建包括车辆动力性能、制动性能以及通行交叉口所有车辆行车风险的目标函数，并设定相应的约束条件；然后，采用模型预测控制方法优化驶向交叉口车辆的行车策略；最后，基于VISSIM、MATLAB和NS3构建联合仿真试验平台，分别以车辆碰撞冲突类型、行车风险改善和道路拥堵程度验证并分析算法性能. 试验结果表明:在车流量和流量容积比大于1.0时，相比于传统的感应控制系统，本文提出的算法在延误时间、行程时间、冲突数目和通行能力的收益率分别大于90%、10%、10%和5%；在通信延迟低于100 ms，数据丢包在35%内，仍能够保证交叉口内车辆的通行效率. 

Cooperative Control Algorithm for Vehicle at Intersection Based on Driving Safety Field Model

In order to improve the driving safety and traffic efficiency of connected and automated vehicle (CAV) at non-signalized intersection, firstly, a driving safety field model of non-signalized intersection is established, the objective function considering vehicle performance and traffic risk of all vehicles at intersection is constructed, and the corresponding constraints are also proposed. The model predictive control is used to optimize driving strategy for all vehicles at the intersection. Co-simulation platform is built based on VISSIM, MATLAB and NS3, which verifies and analyzes the performance of the proposed algorithm based on vehicle collision type, driving risk improvement and traffic congestion level, respectively. The experimental results show that when the ratio of traffic flow to traffic volume is greater than 1.0, compared with the traditional actuated control system, the gain of the proposed algorithm is greater than 90%, 10%, 10% and 5% in delay time, travel time, number of conflicts and traffic capacity, respectively. When the communication delay is less than 100 ms and the data packet loss is within 35%, the vehicle traffic efficiency at the intersection can still be guaranteed.