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

TL;DR

CiMPCC addresses curvature-induced performance gaps in online local trajectory planning by embedding curvature information of the racetrack centerline into the MPCC cost via a curvature-driven velocity reference. The method computes a normalized smooth curvature (NSC) from the centerline, maps NSC to a reference velocity using an exponential mapping g(K^n), and incorporates this into an augmented MPC cost J_Ci alongside the traditional MPCC terms. Offline NSC generation and online velocity reference mapping enable deceleration in sharp turns and acceleration on straights, improving lap times. Hardware validation on a 1:10 DDRA vehicle demonstrates substantial lap-time reductions (≈11–13%) and meaningful mean-velocity gains, with computation times suitable for real-time operation; the authors provide open-source code at GitHub.

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.

Figures (9)

• Figure 1: The curvature-integrated MPCC (CiMPCC) trajectory planning method is illustrated in comparison with the traditional MPCC method. The CiMPCC method maps the curvature of the racetrack centerline into the optimization problem to optimize the velocity of the planned local trajectory, thereby reducing lap time.
• Figure 2: Schematic representation of the approximation of the contour error and lag error of MPCC.
• Figure 3: The schematic diagram illustrates the implementation of the CiMPCC method, which is divided into two main sections: offline and online. The offline section generates the normalized smooth curvature (NSC) of the racetrack centerline, while the online section maps the reference overall velocity (OV) based on this curvature.
• Figure 4: Fig. \ref{['fig:smooth_orig']} and Fig. \ref{['fig:norm']} visualize the process of computing the NSC. Fig. \ref{['fig:mapping']} demonstrates the NSC-velocity mapping function.
• Figure 5: The hardware and software architecture of the DDRA vehicle are used to validate the CiMPCC method, and the racetrack setting is demonstrated.