In head-to-head racing, an accurate model of interactive behavior of the opposing target vehicle (TV) is required to perform tightly constrained, but highly rewarding maneuvers such as overtaking. However, such information is not typically made available in competitive scenarios, we therefore propose to construct a prediction and uncertainty model given data of the TV from previous races. In particular, a one-step Gaussian process (GP) model is trained on closed-loop interaction data to learn the behavior of a TV driven by an unknown policy. Predictions of the nominal trajectory and associated uncertainty are rolled out via a sampling-based approach and are used in a model predictive control (MPC) policy for the ego vehicle in order to intelligently trade-off between safety and performance when attempting overtaking maneuvers against a TV. We demonstrate the GP-based predictor in closed loop with the MPC policy in simulation races and compare its performance against several predictors from literature. In a Monte Carlo study, we observe that the GP-based predictor achieves similar win rates while maintaining safety in up to 3x more races. We finally demonstrate the prediction and control framework in real-time in a experimental study on a 1/10th scale racecar platform operating at speeds of around 2.8 m/s, and show a significant level of improvement when using the GP-based predictor over a baseline MPC predictor. Videos of the hardware experiments can be found at https://youtu.be/KMSs4ofDfIs.

翻译：在头对头竞速中，需要对对手目标车辆（TV）的交互行为进行精确建模，以执行紧约束但高回报的机动操作（如超车）。然而在竞争场景下此类信息通常不可获取，因此我们提出利用过往比赛中TV的数据构建预测与不确定度模型。具体而言，基于闭环交互数据训练单步高斯过程（GP）模型，学习由未知策略驱动的TV行为。通过采样方法展开对名义轨迹及相关不确定度的预测，并将其用于自车的模型预测控制（MPC）策略，从而在试图超越TV时智能权衡安全性与性能。我们在仿真竞速中将基于GP的预测器与MPC策略在闭环环境下进行验证，并与文献中的多种预测器进行性能比较。蒙特卡洛研究表明，基于GP的预测器在维持相同胜率的同时，可在最多3倍数量的比赛中保持安全性。最后，我们在一台运行速度约2.8米/秒的1/10比例赛车平台上进行实时预测试验，实验结果显示基于GP的预测器相较于基线MPC预测器具有显著改进。硬件实验视频见https://youtu.be/KMSs4ofDfIs。