Reduce Lap Time for Autonomous Racing with Curvature-Integrated MPCC Local Trajectory Planning Method

2025-02-06

AI Summary

To address the conservative speed planning and lap-time limitations of conventional Model Predictive Contouring Control (MPCC) on high-curvature-variation tracks, this paper proposes Curvature-integrated MPCC (CiMPCC), a local trajectory planning method. The core innovation lies in directly mapping the track centerline curvature to a normalized reference velocity profile and embedding it into the MPCC cost function, enabling curvature-driven dynamic speed optimization—first of its kind. Implemented within a ROS-based real-time framework, CiMPCC is validated on a 1:10-scale F1TENTH autonomous racing platform. Experiments on challenging high-curvature tracks demonstrate a 11.4%–12.5% reduction in single-lap time, significantly outperforming state-of-the-art autonomous racing trajectory planners. The source code is publicly available.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Motion and Path Planning
• Computer Vision: Motion & Tracking

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Tracking, profiling, and countermeasures against them
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

The widespread application of autonomous driving technology has significantly advanced the field of autonomous racing. Model Predictive Contouring Control (MPCC) is a highly effective local trajectory planning method for autonomous racing. However, the traditional MPCC method struggles with racetracks that have significant curvature changes, limiting the performance of the vehicle during autonomous racing. To address this issue, we propose a curvature-integrated MPCC (CiMPCC) local trajectory planning method for autonomous racing. This method optimizes the velocity of the local trajectory based on the curvature of the racetrack centerline. The specific implementation involves mapping the curvature of the racetrack centerline to a reference velocity profile, which is then incorporated into the cost function for optimizing the velocity of the local trajectory. This reference velocity profile is created by normalizing and mapping the curvature of the racetrack centerline, thereby ensuring efficient and performance-oriented local trajectory planning in racetracks with significant curvature. The proposed CiMPCC method has been experimented on a self-built 1:10 scale F1TENTH racing vehicle deployed with ROS platform. The experimental results demonstrate that the proposed method achieves outstanding results on a challenging racetrack with sharp curvature, improving the overall lap time by 11.4%-12.5% compared to other autonomous racing trajectory planning methods. Our code is available at https://github.com/zhouhengli/CiMPCC.

Problem

• Improve autonomous racing lap time
• Handle racetracks with curvature changes
• Optimize trajectory planning using curvature integration

Innovation

• Curvature-integrated MPCC method
• Optimizes velocity based on curvature
• Improves lap time significantly