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Highly-precise ego-localization and mapping techniques from the road shape features are key elements in order to realize an autonomous driving system for vehicle in urban area which has complex environments. The objective of this study is to develop an autonomous driving system based on mapping and ego-localization using a LIDAR. To handle curved path tracking scenario, this paper proposes a desired steering angle generator considering a constructed map using the LIDAR in real time combined with the feedback control of the preview lateral deviation. The effectiveness of the proposed control method is verified by simulation and test drives using the autonomous path tracking control system.

Small size electric vehicles require active safety technology to prevent road accidents as well as protect drivers and passengers from severe damages. This paper proposes an intersection collision avoidance system by automatic electric braking utilizing in-wheel-motors of electric vehicles. The proposed vehicle control system focuses on the risk assessment in consideration of pedestrians. Consequently, a risk avoidance algorithm based on Potential Fields theory is described to handle the hazardous situations which change dynamically with pedestrian motion. Finally, the effectiveness of the autonomous driving system is verified by the experiments using the micro-scale electric vehicle. The results show that the proposed autonomous vehicle can accomplish the right turn with negotiating a moving pedestrian.