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.

翻译：自动驾驶车辆控制通常分为两个主要领域：轨迹规划与轨迹跟踪。目前轨迹规划多采用基于质点或运动学模型的优化控制器。由于这些规划器未考虑车辆质心高度及其影响，其输出轨迹对不同车型（尤其是高质心车辆）并非最优。因此，轨迹跟踪控制器在实现这些次优轨迹时，可能需要大幅调整以避免违反车辆操纵稳定性与乘坐舒适性约束。本文通过构建简化双轨模型规划器来解决该问题，该模型采用稳态方程估算侧向与侧倾载荷转移，并配合简化轮胎模型以降低求解计算量。在高低加速度工况及不同车辆高度条件下，将所开发规划器与广泛使用的质点模型和运动学模型规划器进行了避障场景对比验证。