用数学曲线提升自动驾驶车辆路径追踪精度与稳定性
Enhancing Autonomous Vehicle Navigation with a Clothoid-Based Lateral Controller
- 采用欧拉螺旋曲线实现平滑曲率过渡，减少转向突变
- 实时动态调整前视距离，适应不同路况与车速
- 适合对舒适性与安全性要求高的自动驾驶系统
本研究提出一种基于欧拉螺旋（clothoid）的横向控制策略，结合自适应前视距离机制，以提升自动驾驶车辆的横向稳定性和路径跟踪精度。通过使用欧拉螺旋实现平滑曲率过渡，有效降低乘客不适感和侧翻风险。创新之处在于根据实时车辆动力学和道路几何信息动态调整前视距离，确保复杂工况下的最优路径跟随。采用准反馈控制算法每一步生成最优欧拉螺旋，并通过前置滤波器补偿车辆横向动态滞后，提升控制响应速度与稳定性。通过TruckSim与Simulink联合仿真验证了该控制器在多种驾驶场景下的显著性能提升。未来工作将拓展至高速场景，并优化铰接式车辆的轨迹偏离最小化问题。
原文摘要 · Abstract (English)
This study introduces an advanced lateral control strategy for autonomous vehicles using a clothoid-based approach integrated with an adaptive lookahead mechanism. The primary focus is on enhancing lateral stability and path-tracking accuracy through the application of Euler spirals for smooth curvature transitions, thereby reducing passenger discomfort and the risk of vehicle rollover. An innovative aspect of our work is the adaptive adjustment of lookahead distance based on real-time vehicle dynamics and road geometry, which ensures optimal path following under varying conditions. A quasi-feedback control algorithm constructs optimal clothoids at each time step, generating the appropriate steering input. A lead filter compensates for the vehicle's lateral dynamics lag, improving control responsiveness and stability. The effectiveness of the proposed controller is validated through a comprehensive co-simulation using TruckSim and Simulink, demonstrating significant improvements in lateral control performance across diverse driving scenarios. Future directions include scaling the controller for higher-speed applications and further optimization to minimize off-track errors, particularly for articulated vehicles.
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