驾驶情境引导的模型预测规划与控制：极限及超越极限的自动驾驶赛车
Driving Context-guided Model Predictive Planning and Control for Autonomous Car Racing at the Limit and Beyond
- University of Modena and Reggio Emilia（摩德纳和雷焦艾米利亚大学）
- Hipert S.r.l.（Hipert 有限责任公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种基于模型预测控制的赛车运动规划与控制流水线，通过成本混合状态机适应超车、正常驾驶和反向转向等情境，并在Superformula EAV-25赛车上验证，圈速接近人类最佳成绩的2%以内。
AI中文摘要：
本文提出了一种基于模型预测控制的运动规划与控制流水线，用于自动驾驶赛车，能够适应不同的驾驶情境，如超车、正常驾驶和反向转向。一个成本混合状态机负责识别不同的驾驶情境，并选择预定义的权重应用于模型预测规划（MPP）和模型预测控制（MPC）模块。这两个基于优化的解决方案共享相同的问题表述和模型，仅在预测时域长度、更新率、调参以及开环与闭环方法上有所不同，以最大化它们交互的有效性。该工作在完全自主的开轮赛车Superformula EAV-25上进行了验证，实现的单圈时间在最佳人类驾驶员参考的2%以内。结果表明，该解决方案能够在操控极限下驾驶，平滑地执行超车操作，并快速响应高度过度转向条件以恢复车辆稳定性。
英文摘要：
This paper presents a Model Predictive Control-based motion planning and control pipeline for autonomous car racing capable of adapting to different driving contexts, such as overtaking, nominal driving, and countersteering. A Cost Blending state machine manages the identification of different driving contexts and the selection of their predefined weights to be applied to the Model Predictive Planning (MPP) and Control (MPC) modules. The two optimization-based solutions share the same problem formulation and model, differing only in horizon length, rate, tuning, and in their open-loop versus closed-loop approach to maximize the effectiveness of their interaction. The work is validated on the fully autonomous open-wheel racecar Superformula EAV-25, with a lap time achieved that is within 2% of the best human driver reference. The results demonstrate the capability of the solution in driving at the limit of handling, smoothly executing overtaking maneuvers, and quickly reacting to high oversteering conditions to recover the vehicle stability.
