Investigation of Intelligent Vehicle Path Tracking Based on Longitudinal and Lateral Coordinated Control

Sun, Zeyu; Wang, Ruochen; Ye, Qing; Wei, Zhendong; Yan, Bingqing

doi:10.1109/access.2020.2994437

Cited by 25 publications

(10 citation statements)

References 21 publications

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“…Sun Z et al proposed a novel coordinated longitudinal and lateral control algorithm for the intelligent vehicle to address the problem of the imbalance of tracking accuracy and vehicle stability during the trajectory tracking process. This makes the controller maintain high trajectory tracking accuracy and good robustness in different driving conditions [ 51 ]. Kianfar et al proposed a combined lateral and longitudinal direction control method in a designated trajectory.…”

Section: Related Workmentioning

confidence: 99%

Research on a Cooperative Adaptive Cruise Control (CACC) Algorithm Based on Frenet Frame with Lateral and Longitudinal Directions

Ping-li

Jiang

2023

Sensors

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Research on the cooperative adaptive cruise control (CACC) algorithm is primarily concerned with the longitudinal control of straight scenes. In contrast, the lateral control involved in certain traffic scenes such as lane changing or turning has rarely been studied. In this paper, we propose an adaptive cooperative cruise control (CACC) algorithm that is based on the Frenet frame. The algorithm decouples vehicle motion from complex motion in two dimensions to simple motion in one dimension, which can simplify the controller design and improve solution efficiency. First, the vehicle dynamics model is established based on the Frenet frame. Through a projection transformation of the vehicles in the platoon, the movement state of the vehicles is decomposed into the longitudinal direction along the reference trajectory and the lateral direction away from the reference trajectory. The second is the design of the longitudinal control law and the lateral control law. In the longitudinal control, vehicles are guaranteed to track the front vehicle and leader by satisfying the exponential convergence condition, and the tracking weight is balanced by a sigmoid function. Laterally, the nonlinear group dynamics equation is converted to a standard chain equation, and the Lyapunov method is used in the design of the control algorithm to ensure that the vehicles in the platoon follow the reference trajectory. The proposed control algorithm is finally verified through simulation, and validation results prove the effectiveness of the proposed algorithm.

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Section: Related Workmentioning

confidence: 99%

Research on a Cooperative Adaptive Cruise Control (CACC) Algorithm Based on Frenet Frame with Lateral and Longitudinal Directions

Ping-li

Jiang

2023

Sensors

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“…In addition to the above-mentioned path-tracking algorithms with more applications in high-speed conditions, new control algorithms continue to emerge as the research progresses. For example, Huang et al of Hunan University studied an adaptive enhanced path tracking system (AEPTS) [60], and Sun proposed a new algorithm using a hierarchical control structure and local linearization of the nonlinear path tracking model [105]. Others study model-based adaptive Q-matrix linear quadratic Gaussian (LQG) control [106], some use pure tracking algorithms with rolling time-domain strategies, etc.…”

Section: Other Algorithmsmentioning

confidence: 99%

A Review of Autonomous Vehicle Path Tracking Algorithm Research

CHEN

Zheng

2022

Preprint

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Driverless technology aims to improve driving safety, accuracy and comfort. Path tracking is a basic component of the motion control module of autonomous vehicles, and its control algorithm directly affects the path tracking effect. Based on the preliminary results of the application of path tracking control algorithm, this paper analyzes the principles, advantages and disadvantages, applications and current research progress of the path tracking algorithm under different working conditions from the perspective of different working conditions at low speed and high speed, and provides an outlook on the future development, aiming to provide reference for future in-depth research.

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“…The driving speed was kept constant at 5 km/h. Simple paths with monotonous curvatures were used in previous studies on the path-following algorithm [27], [28], to demonstrate the performance of their algorithms. In this study, we obtained the desired path by moving the vehicle at various steering angles and speeds in the simulator.…”

Section: A the Simulation Setupmentioning

confidence: 99%

Model Predictive Control Method for Autonomous Vehicles Using Time-Varying and Non-Uniformly Spaced Horizon

et al. 2021

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This paper proposes an algorithm for path-following and collision avoidance of an autonomous vehicle based on model predictive control (MPC) using time-varying and non-uniformly spaced horizon. The MPC based on non-uniformly spaced horizon approach uses the time intervals that are small for the near future, and time intervals that are large for the distant future, to extend the length of the whole prediction horizon with a fixed number of prediction steps. This MPC has the advantage of being able to detect obstacles in advance because it can see the distant future. However, the presence of longer time interval samples may lead to poor path-following performance, especially for paths with high curvature. The proposed algorithm performs proper adjustment of the prediction interval according to a given situation. For sections with large curvature, it uses the short prediction intervals to increase the path-following performance; further, to consider obstacles over a wider range, it uses the long prediction intervals. This technique allows simultaneous improvement of the path-following performance and the range of obstacle avoidance with fixed computational complexity. The effectiveness of the proposed method is verified through an open-source simulator, CARLA and real-time experiments.INDEX TERMS Path following, model predictive control, autonomous vehicle, collision avoidance.

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Investigation of Intelligent Vehicle Path Tracking Based on Longitudinal and Lateral Coordinated Control

Cited by 25 publications

References 21 publications

Research on a Cooperative Adaptive Cruise Control (CACC) Algorithm Based on Frenet Frame with Lateral and Longitudinal Directions

Research on a Cooperative Adaptive Cruise Control (CACC) Algorithm Based on Frenet Frame with Lateral and Longitudinal Directions

A Review of Autonomous Vehicle Path Tracking Algorithm Research

Model Predictive Control Method for Autonomous Vehicles Using Time-Varying and Non-Uniformly Spaced Horizon

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