Consideration of Vehicle Characteristics on the Motion Planner Algorithm
This paper addresses the problem of sub-optimal trajectory planning for autonomous vehicles, particularly for high center of gravity vehicles, which can lead to increased workload for tracking controllers and potential violations of handling and comfort constraints.
The authors developed a motion planner that incorporates vehicle characteristics like center of gravity height and load transfer, which existing planners often overlook. This new planner was tested against common particle and kinematic model planners in collision avoidance scenarios, demonstrating its performance across various vehicle heights and acceleration conditions.
Autonomous vehicle control is generally divided in two main areas; trajectory planning and tracking. Currently, the trajectory planning is mostly done by particle or kinematic model-based optimization controllers. The output of these planners, since they do not consider CG height and its effects, is not unique for different vehicle types, especially for high CG vehicles. As a result, the tracking controller may have to work hard to avoid vehicle handling and comfort constraints while trying to realize these sub-optimal trajectories. This paper tries to address this problem by considering a planner with simplified double track model with estimation of lateral and roll based load transfer using steady state equations and a simplified tire model to reduce solver workload. The developed planner is compared with the widely used particle and kinematic model planners in collision avoidance scenarios in both high and low acceleration conditions and with different vehicle heights.