提出新算法提升自动驾驶车辆急弯高速行驶时的稳定性与控制效率。
Tube-Based Robust Control Strategy for Vision-Guided Autonomous Vehicles
- 基于插值管设计约束迭代线性二次调节器，降低系统保守性。
- 平均控制生成时间仅3.45毫秒，比传统方法快4.32倍。
- 适合对实时性与鲁棒性要求高的自动驾驶视觉控制系统。
针对自动驾驶车辆在高速急弯行驶时的稳定性问题，本文提出一种基于插值管的约束迭代线性二次调节器（itube-CILQR）算法，用于计算机视觉引导的车道保持控制。相比标准管式方法，该算法降低了系统保守性并提升了计算速度。通过数值仿真和基于视觉的实验验证，结果表明itube-CILQR在车道保持性能上优于变分CILQR及采用经典内点优化器的模型预测控制（MPC）方法。具体而言，itube-CILQR生成控制信号的平均耗时为3.45毫秒，而itube-MPC需耗时4.32倍以上。同时，通过分析算法中插值变量在车道保持操作中的变化，揭示了保守性对系统行为的影响。
原文摘要 · Abstract (English)
A robust control strategy for autonomous vehicles can improve system stability, enhance riding comfort, and prevent driving accidents. This paper presents a novel interpolation-tube-based constrained iterative linear quadratic regulator (itube-CILQR) algorithm for autonomous computer-vision-based vehicle lane-keeping. The goal of the algorithm is to enhance robustness during high-speed cornering on tight turns. Compared with standard tube-based approaches, the proposed itube-CILQR algorithm reduces system conservatism and exhibits higher computational speed. Numerical simulations and vision-based experiments were conducted to examine the feasibility of using the proposed algorithm for controlling autonomous vehicles. The results indicated that the proposed algorithm achieved superior vehicle lane-keeping performance to variational CILQR-based methods and model predictive control (MPC) approaches involving the use of a classical interior-point optimizer. Specifically, itube-CILQR required an average runtime of 3.45 ms to generate a control signal for guiding a self-driving vehicle. By comparison, itube-MPC typically required a 4.32 times longer computation time to complete the same task. Moreover, the influence of conservatism on system behavior was investigated by exploring the variations in the interpolation variables derived using the proposed itube-CILQR algorithm during lane-keeping maneuvers.
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