This paper presents an experimental study of a path-tracking framework for autonomous vehicles in which the lateral control command is applied to a dynamic control point along the wheelbase. Instead of enforcing a fixed reference at either the front or rear axle, the proposed method continuously interpolates between both, enabling smooth adaptation across driving contexts, including low-speed maneuvers and reverse motion. The lateral steering command is obtained by barycentric blending of two complementary controllers: a front-axle Stanley formulation and a rear-axle curvature-based geometric controller, yielding continuous transitions in steering behavior and improved tracking stability. In addition, we introduce a curvature-aware longitudinal control strategy based on virtual track borders and ray-tracing, which converts upcoming geometric constraints into a virtual obstacle distance and regulates speed accordingly. The complete approach is implemented in a unified control stack and validated in simulation and on a real autonomous vehicle equipped with GPS-RTK, radar, odometry, and IMU. The results in closed-loop tracking and backward maneuvers show improved trajectory accuracy, smoother steering profiles, and increased adaptability compared to fixed control-point baselines.

翻译：本文提出一种自动驾驶车辆路径跟踪框架的实验研究，其中横向控制指令作用于轴距上的动态控制点。该方法并非强制固定前轴或后轴作为参考点，而是通过两者间的连续插值实现动态调整，从而在包括低速机动和倒车运动在内的多种驾驶场景中实现平滑适应。横向转向指令通过两个互补控制器的重心融合获得：前轴Stanley公式控制器与后轴基于曲率的几何控制器，由此产生转向行为的连续过渡并提升跟踪稳定性。此外，我们提出基于虚拟轨道边界和光线追踪的曲率感知纵向控制策略，该策略将即将到来的几何约束转换为虚拟障碍物距离，并据此调节车速。完整方法在统一控制栈中实现，并通过仿真和配备GPS-RTK、雷达、里程计及IMU的真实自动驾驶车辆进行验证。闭环跟踪与倒车机动实验结果表明，相较于固定控制点基线方法，本方案在轨迹精度、转向平滑度及环境适应性方面均有显著提升。