让虚拟赛车手实时调整驾驶风格，实现接近最优圈速的快速决策。
Biasing the Driving Style of an Artificial Race Driver for Online Time-Optimal Maneuver Planning
- 基于前一时刻轨迹设计终端代价，动态调节驾驶风格。
- 在线规划结果接近离线最优圈速，且快于传统最小时间控制方法。
- 适用于需要实时自适应驾驶策略的智能赛车系统研究。
本文提出一种新型方法，用于在在线时间最优轨迹规划中调控人工赛车驾驶员（ARD）的驾驶风格。该方法采用非线性模型预测控制（MPC）框架，结合时间最小化与规划终点速度最大化目标。通过引入基于前一时刻所规划轨迹的新型终端代价函数，使ARD能够实时从早切弯转向调整为晚切弯转向。该方法计算效率高，支持短重规划周期和长规划时域。仿真验证表明，新终端代价使ARD可有效偏移驾驶风格，其在线圈速接近离线最小圈速（MLT）最优解，且优于最小时间MPC方案。本工作为理解人类车手选择早切或晚切弯的原因提供了新思路。
原文摘要 · Abstract (English)
In this work, we present a novel approach to bias the driving style of an artificial race driver (ARD) for online time-optimal trajectory planning. Our method leverages a nonlinear model predictive control (MPC) framework that combines time minimization with exit speed maximization at the end of the planning horizon. We introduce a new MPC terminal cost formulation based on the trajectory planned in the previous MPC step, enabling ARD to adapt its driving style from early to late apex maneuvers in real-time. Our approach is computationally efficient, allowing for low replan times and long planning horizons. We validate our method through simulations, comparing the results against offline minimum-lap-time (MLT) optimal control and online minimum-time MPC solutions. The results demonstrate that our new terminal cost enables ARD to bias its driving style, and achieve online lap times close to the MLT solution and faster than the minimum-time MPC solution. Our approach paves the way for a better understanding of the reasons behind human drivers' choice of early or late apex maneuvers.
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