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/.

翻译：我们提出了一个模块化框架，用于评估在高加速度机动中推动自动驾驶接近极限的新方法与现有方法在轨迹规划与控制方面的性能。该框架包括时间最优赛道线生成、在线时间最优速度重规划、几何路径跟踪控制器，以及一种用于学习转向控制逆动力学的新型模型结构化神经网络(MS-NN)。我们在两个赛道上，将所提框架部署于1:10比例的RoboRacer平台上。通过采用谨慎型与激进型赛道线的多次消融实验，我们研究了单个模块及其组合的性能。实验表明，MS-NN显著提高了跟踪精度，减少了转向振荡，并具有物理可解释性。此外，在线速度重规划通过补偿执行误差改善了单圈时间，使车辆能够安全地达到更高速度和加速度。为支持未来研究，我们的代码、数据集、视频及结果已在 https://roboracer-benchmark.github.io/planning_control_benchmark/ 上公开。