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

TL;DR

The paper addresses mapless autonomous racing by proposing a local planner-based control that operates directly on LiDAR data to generate a centerline and control commands. It integrates an adaptive track-width estimation, a Stanley-based lateral controller with adaptive lookahead, and a minimum-time velocity profile for longitudinal control, all tuned on a 1/10 RC F1Tenth platform. The main contributions are a complete local planning-and-control pipeline, adaptive width handling, and an adaptive lookahead strategy, demonstrated to achieve lap-time performance within 8% of map-based methods while offering higher realism. This work suggests that carefully tuned local planning can deliver competitive performance in autonomous racing scenarios closer to real-world conditions, with potential industrial relevance.

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.

Figures (10)

• Figure 1: Block diagram of the autonomous system architecture and illustration of the connection of the examined control methods
• Figure 2: Segmentation of the raw point cloud measured by the LiDAR
• Figure 3: Track width estimation of the actual measurement
• Figure 4: Example Track of an F1Tenth race
• Figure 5: Illustration of the main error component of the Stanley-controller stanley_error