Abstract
Vision-based sensors are widely used in lateral control of autonomous vehicles, but the large computational cost of the visual algorithms often induces uneven time delays. In this paper, a hierarchical vision-based lateral control scheme is proposed, where the upper controller is designed by robust H∞-based linear quadratic regulator (LQR) algorithm to compensate sensor-induced delays, and the lower controller is based on logic threshold method, in order to achieve strong convergence of the steering angle. Firstly, the vehicle lateral model is built, and the nonlinear uncertainties induced by time delays are linearized with Taylor expansion. Secondly, the state space of the system is augmented to describe such uncertainties with polytopic inclusions, which is controlled by an H∞-based LQR controller with a low cost of online computation. Then, a lower controller is designed for the control of the steering motor. According to the results of the vehicle experiment as well as the hardware-in-the-loop (HIL) experiment, the proposed control scheme shows good performance in vehicle’s lateral control task, and exhibits better robustness compared with a conventional LQR controller. The proposed control scheme provides a feasible solution for the lateral control of autonomous driving.
Highlights
Autonomous driving is an effective way to reduce traffic accidents and to enhance driving experience
Taking into account a simple case, lane tracking, the task can be roughly summarized as making the vehicle follow the lane centerline or any other planned path by determining and performing the desired input [1,2], and this is required by all of levels of autonomous vehicles according to the outline of SAE-J3016 [3]
Taking thewith step experiment as state an example, the steering motor was accurate trackinginofdynamic the steering wheel angle small steady error and small overshooting set to the process
Summary
Autonomous driving is an effective way to reduce traffic accidents and to enhance driving experience. The basic purpose of the autonomous vehicle is to automatically drive the vehicle along specific trajectories without the driver’s intervention [1]. Assuming that all localization information is available, the path tracking becomes a motion control problem of the vehicle, which mainly includes lateral control and longitudinal control [2]. The lateral control aims to track a planned trajectory [4] through steer-by-wire (SBW) system or differential braking system [5,6,7,8,9], while the longitudinal control is to achieve closed-loop velocity control through drive-by-wire (DBW) and brake-by-wire system (BBW) [10]. Considering that longitudinal control already has mature commercial applications, such as cruise control (CC) and adaptive cruise control (ACC) [10,11], this study will mainly focus on the lateral control strategies
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