Adaptive dynamic preview control for autonomous vehicle trajectory following with DDP based path planner

Wu, Ning; Huang, Weiwei; Song, Zhiwei; Wu, Xiaojun; Zhang, Qun; Yao, Susu

doi:10.1109/ivs.2015.7225817

Cited by 13 publications

(11 citation statements)

References 8 publications

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“…Xizheng Zhang et al studied LQR path tracking control strategy based on visual road detection [28]. 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].…”

Section: Lqr Optimal Control Methodsmentioning

confidence: 99%

“…However, when the vehicle behaves nonlinearly, with modeling errors and external disturbances, the effect of the controller decreases significantly due to linear feedback and model simplification. Therefore, some research introduced feedforward control based on road information [29][30], and feedback control based vehicle dynamics [26,[30][31] into controller design to compensate unmodeled vehicle dynamics and disturbance. However, this type of controllers need to be optimized online, which requires high computing power.…”

Section: Lqr Optimal Control Methodsmentioning

confidence: 99%

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

Yao

Tian

Wang³

et al. 2020

IEEE Access

123

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Autonomous vehicle technology aims to improve driving safety, driving comfort, and its economy, as well as reduce traffic accident rate. As the basic part of autonomous vehicle motion control module, path tracking aims to follow the reference path accurately, ensure vehicle stability and satisfy the robust performance of the control system. This paper introduces the representative control strategies, robust control strategies and parameter observation-based control strategies on path tracking for autonomous vehicle. Furthermore, the implementations and disadvantages are summarized. Most importantly, the critical review in this paper provides a list and discussion of the remaining challenges and unsolved problems on path tracking control.

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Section: Lqr Optimal Control Methodsmentioning

confidence: 99%

Section: Lqr Optimal Control Methodsmentioning

confidence: 99%

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

Yao

Tian

Wang³

et al. 2020

IEEE Access

123

View full text Add to dashboard Cite

show abstract

“…In the last decade, the development of fully automated vehicles seems to have been one of the most primary focuses of development for many automobile manufacturers. On the other hand, the control of normal driving scenarios such as in [4][5][6][7] have been at the center of their research in most cases. One drawback of these methods is that they are unable to stabilize the vehicle beyond the limit of handling.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Controller Design for Vehicle Motion Control at Handling Limits in Multiple Equilibria on Varying Road Surfaces

et al. 2021

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Electronic vehicle dynamics systems are expected to evolve in the future as more and more automobile manufacturers mark fully automated vehicles as their main path of development. State-of-the-art electronic stability control programs aim to limit the vehicle motion within the stable region of the vehicle dynamics, thereby preventing drifting. On the contrary, in this paper, the authors suggest its use as an optimal cornering technique in emergency situations and on certain road conditions. Achieving the automated initiation and stabilization of vehicle drift motion (also known as powerslide) on varying road surfaces means a high level of controllability over the vehicle. This article proposes a novel approach to realize automated vehicle drifting in multiple operation points on different road surfaces. A three-state nonlinear vehicle and tire model was selected for control-oriented purposes. Model predictive control (MPC) was chosen with an online updating strategy to initiate and maintain the drift even in changing conditions. Parameter identification was conducted on a test vehicle. Equilibrium analysis was a key tool to identify steady-state drift states, and successive linearization was used as an updating strategy. The authors show that the proposed controller is capable of initiating and maintaining steady-state drifting. In the first test scenario, the reaching of a single drifting equilibrium point with −27.5° sideslip angle and 10 m/s longitudinal speed is presented, which resulted in −20° roadwheel angle. In the second demonstration, the setpoints were altered across three different operating points with sideslip angles ranging from −27.5° to −35°. In the third test case, a wet to dry road transition is presented with 0.8 and 0.95 road grip values, respectively.

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“…An autonomous vehicle is a motor vehicle that uses artificial intelligence, sensors, and global positioning system coordinates to drive itself without the active intervention of a human operator (Anagnostopoulos, 2012). Autonomous cars can communicate with each other to reduce traffic congestion and offer greater ease of travel for the elderly and disabled (Zhang and Braun, 2017;Wu et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Review article: State-of-the-art trajectory tracking of autonomous vehicles

Zhang

2021

Mech. Sci.

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Abstract. Air pollution, energy consumption, and human safety issues have aroused people's concern around the world. This phenomenon could be significantly alleviated with the development of automatic driving techniques, artificial intelligence, and computer science. Autonomous vehicles can be generally modularized as environment perception, path planning, and trajectory tracking. Trajectory tracking is a fundamental part of autonomous vehicles which controls the autonomous vehicles effectively and stably to track the reference trajectory that is predetermined by the path planning module. In this paper, a review of the state-of-the-art trajectory tracking of autonomous vehicles is presented. Both the trajectory tracking methods and the most commonly used trajectory tracking controllers of autonomous vehicles, besides state-of-art research studies of these controllers, are described.

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Adaptive dynamic preview control for autonomous vehicle trajectory following with DDP based path planner

Cited by 13 publications

References 8 publications

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

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

Model Predictive Controller Design for Vehicle Motion Control at Handling Limits in Multiple Equilibria on Varying Road Surfaces

Review article: State-of-the-art trajectory tracking of autonomous vehicles

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