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This paper presents a methodology of trajectory planning for the surrounding-aware lane change maneuver of autonomous vehicles based on a data-driven method. The lateral motion is planned by sampling candidate patterns which are defined based on quintic polynomial functions over time. Based on the cost evaluation among the sampled candidates, the optimal lateral motion pattern is selected as a reference and tracked by the controller. The longitudinal motion is planned and controlled using Model Predictive Control (MPC) which is an optimal control method designed considering the surrounding traffic information. To realize the lane change motion similar to the human driving behavior in the surrounding traffic situation, the human driving pattern is modeled in the form of motion parameters and considered in planning the lateral and longitudinal motion.

This paper presents motion planning and control algorithm for urban automated driving using high-definition(HD) map. Many automakers have developed and commercialized advanced driver assistance system(ADAS) based on vision-only lane extraction in motorway environments. Compared to the motorway environments where the lane is continuous and clearly visible, however, in urban roads, degradation of the lane quality such as lane occlusion and lane loss occurs frequently. This leads to the poor quality of the local guide path for the autonomous vehicles with vision-only lane extraction. Global HD map is used to provide the lane information continuously instead of vision-only lane extraction. With the existence of global position of host vehicle and the HD map, the proposed sequential algorithm performs the lane keeping and lane changing decision and control with safety margin in multi-vehicle situation.