Path Tracking with Dynamic Control Point Blending for Autonomous Vehicles: An Experimental Study

TL;DR

This paper introduces a dynamic control point blending approach for autonomous vehicle path tracking, improving stability and adaptability across various driving scenarios through experimental validation.

Contribution

It proposes a novel blending method for lateral control points and a curvature-aware longitudinal control strategy, enhancing tracking accuracy and smoothness.

Findings

Improved trajectory accuracy in experiments.

Smoother steering profiles observed.

Enhanced adaptability in diverse driving conditions.

Abstract

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.