Review of Motion Planning Methods of Intelligent Connected Vehicles

Recent studies on motion planning methods of intelligent connected vehicle (ICV) are analyzed in this paper. In terms of working space, time, and objective, ICV's motion planning is divided into four subtasks: route planning, path planning, maneuver planning, and trajectory planning. Past research and applications of the techniques of vehicle intelligence and connection in each subtask are reviewed. Behavioral characteristics of the ICV driver and their impact on the outcome of ICV motion planning are discussed. Four aspects of the current trend in ICV motion planning research are discussed: technical background, research scenario, algorithm flow and applied theory. As an ICV mainly depends on vehicle connecting information to plan travelling route, this survey finds that the difficulty of ICV route planning increases when ICV's with different connecting functions coexist in the road network. Dynamics of multiple surrounding vehicles and lane configuration are rarely considered in ICV's path planning. This is likely to be addressed by integrating the existing path planning algorithms with microscopic traffic flow models. The issues of human-machine cooperation and task transfer in ICV have recently become hot topics of research. These issues include lane changing and turning maneuver planning in urban arterial roads, maneuver guidance of ICV for non-connecting vehicle and others. There is academic consensus that the behavior of the driver in an ICV should be considered in trajectory planning. However, there is limited application of vehicle-to-vehicle and vehicle-to-infrastructure connecting information. We propose that applying feedback-iteration to coordinate ICV's path and maneuver planning as well as its motion planning and trajectory tracking control could help in globally optimized motion planning and vehicle control. Furthermore, formulating a model for ICV's motion planning on a theoretical foundation that is appropriate for the specific motion-planning task could not only take advantage of the merits of the theory but also increase flexibility and adaptability of the motion planning algorithms. © 2019, Editorial Department of China Journal of Highway and Transport. All right reserved.