The optimal path planning for EVs (electric vehicles) has gained great attention during the last decade due to the zero pollution emission characteristics and limited power capacity of EV batteries. In this paper, an optimal route search is proposed considering multiple charging stations in a dynamic urban environment, while it is still applicable when the initial available amount of the battery fails to cover a certain travel range. The TRDP (transit route design problem) and TNDP (transit node design problem) are used to search for the most feasible routes based on time and driving range via the improved route-assisted rapid random tree (RA-RRT*) algorithm. Considering the status of charge of an EV’s battery during optimal routes search, three states are investigated between the destination and the aggregators: (1) bypassing the aggregators, (2) stopping over a single aggregator, and (3) stopping over multiple aggregators. During the states (2) and (3), it is required that the EVs be charged at the charging stations obtained by the RA-RRT* algorithm while approaching the destination. The proposed algorithm is tested on a random dataset under certain conditions, that is, traffic flow with congestion and assigned target locations from a given map data, with comparison experiments for efficacy verification.

In order to detect the object and inspect the road conditions in real-time, the 2-dimensional (2D) and 3dimensional (3D) data obtained from the onboard sensors, LiDAR and digital cameras are analyzed for object recognition to assist driving. Due to the uncertainties of the dynamic objects, such as pedestrians, animals or vibrated vehicles, extraction of complete and clear objects from LiDARs datasets requires complex post-processing since LiDAR data can be used for scanning at long distances, i.e., 300m, which can alarm the driver timely to take necessary actions. The dynamic and static objects from the LiDARs point clouds can be detected with the teacher-student framework algorithm along with the KITTI dataset. Furthermore, a semi-supervised theory is utilized to improve detection performance.