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

2026-02-02

AI Summary

This work addresses the limited path-tracking accuracy and stability in autonomous driving under low-speed and reverse maneuvers caused by fixed control points (front or rear axle). To overcome this, the authors propose a dynamic control-point fusion framework that continuously interpolates between the outputs of a front-axle Stanley controller and a rear-axle curvature-based geometric controller based on the vehicle’s center of gravity. This lateral control strategy is synergistically integrated with a longitudinal velocity modulation scheme that leverages curvature-aware virtual track boundaries and ray-casting techniques. The key innovation lies in the continuous interpolation mechanism, enabling smooth transitions between front- and rear-axle control modes. Experimental results from both simulation and real-world vehicle tests demonstrate that the proposed approach significantly improves trajectory tracking accuracy, steering smoothness, and adaptability in complex maneuvers such as closed-loop tracking and reversing, outperforming conventional fixed-control-point methods.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Motion & Tracking

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• path tracking
• autonomous vehicles
• dynamic control point
• lateral control
• steering adaptability

Innovation

• dynamic control point blending
• barycentric interpolation
• path tracking
• curvature-aware longitudinal control
• autonomous vehicle