基于虚拟车队的自动交叉路口车辆时序优化模型

智能网联汽车可通过彼此交互协同安全地通过交叉路口,自动交叉路口控制已成为未来发展趋势。为解决现有基于预约的自动交叉路口控制模型未全局优化车辆通过顺序及模型非线性导致求解效率低等问题,提出一种基于虚拟车队的自动交叉路口车辆时序优化模型,实现车辆通过时序的高效全局优化。首先,为构建到达安全时间间隔约束,基于车辆冲突分析计算交叉口进口道停车线到各相互作用点的距离。其次,为便于建模和求解,基于时间维度构建虚拟车队并形成车辆索引序列。然后,以交叉口车辆总延误最小为优化目标,车辆通过控制区段的最小行程时间和到达冲突区域边界的安全间隔为约束条件,构建自动交叉路口车辆通过时序非线性优化模型。在此基础上,引入0-1变量将该模型转化为混合整数线性规划模型,并基于开源求解器CBC对模型进行求解。最后,设计数值仿真试验验证模型的有效性并进行了模型的参数敏感性分析。研究结果表明:所构建模型在不同交通需求下优化效果均优于基于"先到先服务"规则的模型,车均延误和最大单车延误能够减少61.50%和39.73%;当安全间距和优化周期较大时,构建模型的延误控制效果更为显著;模型和算法为未来智能网联环境下自动交叉路口控制提供了一种可选的方法。

Vehicle Schedule Optimization Model for Autonomous Intersection Based on Virtual Platoon

Connected and autonomous vehicles can mutually interact to safely pass through intersections.Therefore,autonomous intersection control is a future development trend.Existing reservation-based autonomous intersection control models do not globally optimize the vehicle passing sequence and are nonlinear,thereby leading to low-efficiency solutions.To solve this problem,a vehicle schedule optimization model of autonomous intersections based on a virtual platoon is proposed.This model achieves efficient global optimization of the vehicle passing sequence.First,to construct the safe arrival time interval constraint,the distance from the stop line to the interaction points was calculated based on vehicle conflict analysis.Second,for the convenience of modeling and solving,a virtual platoon was constructed based on the time dimension,and a vehicle index sequence was formed.Then,setting the minimum total vehicle delay at the intersection as the optimization objective,and the minimum travel time of vehicles passing through the control area and arrival time interval of vehicles reaching the boundary of the conflict area as constraints,a nonlinear optimization model of the vehicle passing sequence at an autonomous intersection was constructed.On this basis,0-1 variables were introduced to transform the model into a mixed-integer linear programming model,and the model was solved based on the open-source solver CBC.Finally,a numerical simulation experiment was conducted to verify the effectiveness of the model,and the sensitivity of the model parameters was analyzed.The results lead to the following conclusions.① The optimization effect of the proposed model is better than that of the model based on the first-come-first-serve rule under different traffic demands.The average vehicle delay and maximum single-vehicle delay are reduced by 61.50% and 39.73%,respectively.② When the safety distance and optimization period are large,the delay control effect of the model is more significant.The proposed model and algorithm jointly provide an alternative method for autonomous intersection control in future intelligent network environments.