Evaluation of Local Planner-Based Stanley Control in Autonomous RC Car Racing Series

TL;DR

This paper presents a local path planning control method for autonomous RC car racing that achieves near-optimal lap times without prior map building, demonstrating its practical relevance and competitive performance.

Contribution

It introduces a map-free, local planning-based control technique with adaptive lookahead and stabilized Stanley controller, achieving performance close to complex optimization methods.

Findings

Proposed method has only 8% lower average speed than global optimization-based approach.

The control technique effectively stabilizes movement across different speeds.

The approach is highly relevant to real-world automotive scenarios.

Abstract

This paper proposes a control technique for autonomous RC car racing. The presented method does not require any map-building phase beforehand since it operates only local path planning on the actual LiDAR point cloud. Racing control algorithms must have the capability to be optimized to the actual track layout for minimization of lap time. In the examined one, it is guaranteed with the improvement of the Stanley controller with additive control components to stabilize the movement in both low and high-speed ranges, and with the integration of an adaptive lookahead point to induce sharp and dynamic cornering for traveled distance reduction. The developed method is tested on a 1/10-sized RC car, and the tuning procedure from a base solution to the optimal setting in a real F1Tenth race is presented. Furthermore, the proposed method is evaluated with a comparison to a more simple reactive method, and in parallel to a more complex optimization-based technique that involves offline map building the global optimal trajectory calculation. The performance of the proposed method compared to the latter, referring to the lap time, is that the proposed one has only 8% lower average speed. This demonstrates that with appropriate tuning, a local planning-based method can be comparable with a more complex optimization-based one. Thus, the performance gap is lower than 10% from the state-of-the-art method. Moreover, the proposed technique has significantly higher similarity to real scenarios, therefore the results can be interesting in the context of automotive industry.