Vertical Trajectory Planning for Autonomous Vehicles

Based on trajectory planning algorithms, a new, real time capable control algorithm for active suspension systems is introduced in this paper to face new requirements on vehicle body motion in low frequency range. The algorithm makes use of pre-view information about the road profile and the future trajectory of the vehicle to find an optimal sequence of control signals for a given path. Optimality is determined via a nonlinear cost function. In the first step, requirements on low frequency motion for autonomous vehicles to are derived from the expected usage scenarios, including side activities like working. Therefore, existing functions like curve tilting are reviewed and requirements are compared to comfort criteria for long-distance trains. This is followed by a detailed description of the proposed controller and two implementations for online and offline optimization. The performance of the controller is then evaluated using a simulation environment with a 10 DoF vehicle model, explaining the influence of different cost functions and investigating model accuracy. A simulation of different control algorithms including state of the art non-preview and preview controllers shows the advantages of the new method.