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.

翻译：自动驾驶车辆的鲁棒控制策略能够提升系统稳定性、增强乘坐舒适性并预防驾驶事故。本文提出了一种新颖的基于插值管道的约束迭代线性二次调节器（itube-CILQR）算法，用于基于计算机视觉的自动驾驶车辆车道保持。该算法的目标是在高速通过急弯时增强系统的鲁棒性。与标准的基于管道的方法相比，所提出的itube-CILQR算法降低了系统保守性，并展现出更高的计算速度。通过数值仿真和基于视觉的实验，检验了所提算法用于控制自动驾驶车辆的可行性。结果表明，所提算法在车辆车道保持性能上优于基于变分CILQR的方法以及使用经典内点优化器的模型预测控制（MPC）方法。具体而言，itube-CILQR平均需要3.45毫秒的运行时间来生成引导自动驾驶车辆的控制信号。相比之下，itube-MPC通常需要4.32倍更长的计算时间来完成相同任务。此外，通过探究在车道保持机动中使用所提itube-CILQR算法得到的插值变量的变化，研究了保守性对系统行为的影响。