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. The advantages of itube-CILQR over the standard tube-approach include reduced system conservatism and increased computational speed. Numerical and vision-based experiments were conducted to examine the feasibility of the proposed algorithm. The proposed itube-CILQR algorithm is better suited to vehicle lane-keeping than variational CILQR-based methods and model predictive control (MPC) approaches using a classical interior-point solver. Specifically, in evaluation experiments, itube-CILQR achieved an average runtime of 3.16 ms to generate a control signal to guide a self-driving vehicle; itube-MPC typically required a 4.67-times longer computation time to complete the same task. Moreover, the influence of conservatism on system behavior was investigated by exploring the interpolation variable trajectories derived from 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。与标准管方法相比，itube-CILQR 的优势在于降低了系统保守性并提高了计算速度。通过数值实验和基于视觉的实验验证了所提算法的可行性。所提出的 itube-CILQR 算法相较于基于变分 CILQR 的方法以及使用经典内点求解器的模型预测控制（MPC）方法，更适用于车辆车道保持任务。具体而言，在评估实验中，itube-CILQR 生成引导自动驾驶车辆控制信号的平均运行时间为 3.16 毫秒；而 itube-MPC 完成相同任务通常需要 4.67 倍的计算时间。此外，通过分析车道保持机动中由所提 itube-CILQR 算法导出的插值变量轨迹，研究了保守性对系统行为的影响。