基于回旋曲线（Clothoid）的横向控制器增强自动驾驶车辆导航
Enhancing Autonomous Vehicle Navigation with a Clothoid-Based Lateral Controller
AI总结：
本研究提出结合自适应预瞄机制的Clothoid横向控制器，通过准反馈算法与超前滤波器提升自动驾驶车辆路径跟踪精度与稳定性，经TruckSim-Simulink联合仿真验证性能显著改善。
AI中文摘要：
本研究提出一种采用基于回旋曲线（Clothoid）方法并结合自适应预瞄机制的自动驾驶车辆高级横向控制策略，核心目标是通过应用欧拉螺旋线实现平滑曲率过渡，提升横向稳定性与路径跟踪精度，从而降低乘客不适及车辆侧翻风险。本研究的创新点在于，可根据实时车辆动力学与道路几何自适应调整预瞄距离，确保在不同工况下实现最优路径跟随；准反馈控制算法在每个时间步构建最优回旋曲线，生成合适的转向输入；超前滤波器补偿车辆横向动力学滞后，提升控制响应性与稳定性。通过TruckSim与Simulink的联合仿真对所提出控制器的有效性进行验证，结果显示其在多种驾驶场景下的横向控制性能有显著提升。未来研究方向包括将该控制器扩展至更高车速应用场景，以及进一步优化以减少偏离路径误差，尤其针对铰接式车辆。
英文摘要：
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.
