On XYZ-Motion Planning Using a Full Car Model

This paper studies XYZ-motion planning using a full car model with active suspension components. The proposed approach involves harnessing perception data to create a two-tier representation of the road surface, consisting of the encoded road and the estimated road. The encoded road isolates specific road events, while the estimated road provides a holistic road profile. Leveraging on the information of the estimated road, a generic nonlinear optimization problem involving a full car model is formulated to craft an XYZ-motion plan. To ensure practical in-vehicle implementation, the optimization problem is decomposed into to two distinct phases. First, an XY-path is optimized by leveraging information from the isolated road events. Second, the generated XY-path is used to devise an XYZ-trajectory, including a vertical motion plan and an optimal speed profile. Finally, numerical results obtained by using both synthetic and real-world road surface data are provided to illustrate the effectiveness of the proposed methodology.