Consideration of Vehicle Characteristics on the Motion Planner Algorithm

TL;DR

This paper introduces a vehicle characteristic-aware motion planner that accounts for load transfer and vehicle height, improving trajectory planning for different vehicle types, especially high center of gravity vehicles.

Contribution

It proposes a simplified double track model with load transfer estimation for trajectory planning, addressing limitations of existing particle and kinematic model planners.

Findings

Improved trajectory planning accuracy for high CG vehicles.

Reduced solver workload with simplified tire and load transfer models.

Enhanced collision avoidance performance across vehicle types.

Abstract

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.