Optimal Path Tracking Control of Autonomous Vehicle: Adaptive Full-State Linear Quadratic Gaussian (LQG) Control

Abstract

In practice, many autonomous vehicle developers put a tremendous amount of time and efforts in tuning and calibrating the path tracking controllers in order to achieve robust tracking performance and smooth steering actions over a wide range of vehicle speed and road curvature changes. This design process becomes tiresome when the target vehicle changes frequently. In this study, a model-based Linear Quadratic Gaussian (LQG) Control with adaptive Q-matrix is proposed to efficiently and systematically design the path tracking controller for any given target vehicle while effectively handling the noise and error problems arise from the localization and path planning algorithms. The regulator, in turn, is automatically designed, without additional efforts for tuning at various speeds. The performance of the proposed algorithm is validated based on KAIST autonomous vehicle. The experimental results show that the proposed LQG with adaptive Q-matrix has tracking performance in both low (15kph) and high (45kph) speed driving conditions better than those of other conventional tracking methods like the Stanley and Pure-pursuit methods. INDEX TERMS Autonomous vehicle, intelligent vehicle, linear quadratic Gaussian (LQG) control, lookahead distance, path tracking.

Are Self-Driving Vehicles Ready to Launch? An Insight into Steering Control in Autonomous Self-Driving Vehicles

“…In the last couple of years, many researchers put a significant amount of time and effort into optimizing and calibrating the steering controller to obtain consistent steering operation and reliable steering performance in high speed and road curvature deviations. In this regard, Lee et al [49] presented a linear-quadratic Gaussian (LQG) model-based controller design which systematically addresses the additive bias factor caused by path planning and localization. Additionally, the authors have presented a regulator designed for automated steering system tuning for different acceleration patterns of vehicles.…”

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

“…The preview distance LQR controllers are designed to deal with road curvature and control error [29][30][31]. In addition, considering the noise in the localization and planning stage, a modelbased linear quadratic gaussian control method with adaptive Q-matrix is proposed for tracking controller design [32]. The above research have proposed control strategies based on LQR for path tracking control, and established a simplified vehicle dynamics model and system state space model to obtained the optimal control input.…”

Hybrid Tracker Based Optimal Path Tracking System of Autonomous Driving for Complex Road Environments

“…For this reason, combining the two fields of perception and control algorithm is emerging as a very important area in autonomous driving. Hence, There are several prominent researches about path tracking on autonomous driving [2], [3]. Recently, end-to-end deep learning tracking algorithms [4]- [6] or methods using reinforcement learning [7] have been studied, but these methods are difficult to response flexible changes in the surrounding environment.…”