Trajectory planning approach for autonomous electric bus in dynamic environment

Path planning is a challenging task to achieve vehicle autonomy, and it becomes even more difficult in handling dynamic situations and big vehicle sizes. Its performance is affected by the accuracy of mapping and vehicle localization. Many path planning algorithms have been developed to address static environments, however, these do not accurately reflect real-world scenarios which are dynamic in nature. This paper introduces a new planning architecture consisting of a global planner that plans a path in an occupancy grid map generated using LIDAR and odometry data. The local planner uses this global plan and plans a local plan on a section of the map that takes into consideration dynamic obstacles. Both the local and global plans are used to calculate the optimum velocity profile, providing a feasible and comfortable trajectory. The trajectory is then fed to a predictive Stanley controller, and both the calculated steering angle and the optimum velocity are achieved by low-level controllers. The performance of the proposed architecture was tested on a golf bus in a constrained environment and compared to different manual driving attitudes using a key performance indicator. The results of the proposed architecture show that it has a better KPI, with an average of 37% better than manual driving.