Trajectory Optimization Method For Automated Truck Platooning In The Connected And Automated Vehicle Environment

With the development of the logistics industry,the average daily operating mileage and operating time of freight vehicles on highways are increasing substantially in China.Considering the physical characteristics and dynamic characteristics of trucks,the growth of freight volume not only brings benefits to the society and economy,but also brings challenges to road capacity,traffic flow stability and the surrounding environment.Recently,the rapid development of connected and automated technologies enables the trucks to communicate with each other and travel with low headway known as the cooperative and automated truck(CAT)platooning.The reduced headways between the trucks potentially help to mitigate the negative effects on traffic flow,thus improving highway capacity,and reducing energy consumption and pollution emissions.The existing research mainly focuses on the platooning control algorithm.However,there is a lack of systematic research on the optimization of the trajectory from the formation to the decomposition process of the entire platooning.In addition,most research in the literature is only suitable to idea conditions and does not strictly cover various restrictions.Their proposed algorithms are too complex to be implemented,or their scenarios are limited to pure connected automated trucks.To fill the gaps,this paper proposes trajectory optimization methods for connected and automated trucks during the platooning process.The main contents of the paper include two parts.The first part is to study the truck platooning trajectory optimization problem in the fully connected and automated vehicle environment.By taking the platooning order and driving trajectory of automatic freight vehicles as decision variables,a two-stage mixed integer programming model is developed,where the optimization algorithm of both the platooning order and trajectory are designed to minimize the total energy consumption of vehicles in the system.Numerical simulation experiments are carried out to find out the applicable platooning sequence algorithm under different scenarios and initial states.The optimized trajectory results obtained by the two-stage algorithm are also compared with those obtained by the traditional algorithm.The results show that the effectiveness of the proposed two-stage algorithm can be guaranteed while the complexity of the algorithm is greatly reduced.The second part is to optimize the platooning trajectory of automated trucks in the mixed traffic with connected automated vehicles and connected human-driven vehicles.We propose a mixed integer programming model based on rolling horizon algorithm,where the trajectory of automated trucks can be iteratively optimized according to the real-time updated driving state.It is also expected that the controlled automated trucks can influence the behavior of human-driven trucks so as to achieve the optimal effect of the system.Through numerical simulation experiments,the impact of the number of humandriven trucks,the initial operation state of automated trucks,the uncertainty of driving behavior,the selection of the system objective function and the selection of rolling time-domain algorithm parameters on the efficiency of the mixed traffic system.