在机器人赛车平台上搭建极限驾驶测试基准，提升轨迹规划与控制精度。
Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform
- 构建模块化框架，融合最优路径生成与在线速度重规划
- 神经网络模型使转向控制误差降低，且能减少方向盘振荡
- 适合自动驾驶、机器人控制研究者参考，支持开源复现
我们提出一个模块化框架，用于评估高加速度工况下轨迹规划与控制方法在极限自主驾驶中的表现。框架包含时间最优赛道生成、在线时间最优速度重规划、几何路径跟踪控制器，以及一种新型结构化神经网络（MS-NN）以学习转向控制的逆动力学。我们在1:10比例的RoboRacer平台部署该框架，使用两条赛道进行实验。通过对比保守与激进赛道策略的消融实验，分析各模块及其组合性能。结果表明，MS-NN显著提升跟踪精度，减少转向振荡，且具备物理可解释性；在线速度重规划可补偿执行误差，使车辆安全实现更高车速与加速度，缩短单圈时间。为促进后续研究，代码、数据集、视频与结果均公开于https://roboracer-benchmark.github.io/planning_control_benchmark/。
原文摘要 · Abstract (English)
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/.
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