Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform
For researchers in autonomous racing and aggressive driving, this provides an open benchmark and a novel neural network approach that improves control performance, though it is incremental in the context of existing methods.
The paper presents a modular benchmarking framework for trajectory planning and control in high-acceleration autonomous driving, demonstrating that a model-structured neural network improves tracking accuracy and reduces steering oscillations, while online velocity replanning enhances lap times and enables safer high-speed maneuvers on a 1:10-scale RoboRacer platform.
We present a modular framework to benchmark new and existing methods for trajectory planning and control in high-acceleration maneuvers that push autonomous driving to the limits. Our framework includes time-optimal raceline generation, online time-optimal velocity replanning, geometric path tracking controllers, and a new model-structured neural network (MS-NN) to learn the inverse dynamics for steering control. We deploy our framework on a 1:10-scale RoboRacer platform, using two circuits. Through several ablations with cautious and aggressive racelines, we study the performance of single modules and their combinations. We show that our MS-NN significantly improves tracking accuracy, decreases steering oscillations, and is physically interpretable. Moreover, online velocity replanning improves lap times by compensating for execution errors, and enables the vehicle to safely reach higher speeds and accelerations. To support future research, our code, datasets, videos and results are publicly available at https://roboracer-benchmark.github.io/planning_control_benchmark/.