Trajectory Planning and Tracking for Autonomous Vehicle Based on State Lattice and Model Predictive Control

Zhang, Chaoyong; Chu, Delin; Liu, Shidong; Deng, Zejian; Wu, Chaozhong; Su, Xiaocong

doi:10.1109/mits.2019.2903536

Cited by 94 publications

(42 citation statements)

References 31 publications

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“…The path planning and tracking for AVs can be solved separately as a hierarchical framework with two independent parts or integrated as an entire part. In a hierarchical framework, a reference path is planned firstly, then a controller is designed to track the planned path [26] [27]. The integrated solution method aims to improve the time delay of tracking by directly outputting the reference control variables rather than the expected reference route [28].…”

Section: An Adaptive Potential Field Based Path Planningmentioning

confidence: 99%

Adaptive Potential Field-Based Path Planning for Complex Autonomous Driving Scenarios

et al. 2020

IEEE Access

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Section: An Adaptive Potential Field Based Path Planningmentioning

confidence: 99%

Adaptive Potential Field-Based Path Planning for Complex Autonomous Driving Scenarios

et al. 2020

IEEE Access

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“…The vehicle active obstacle avoidance system is one of the core issues in the research of autonomous vehicle (AV) control [ 1 , 2 ]. A safe and reasonable obstacle avoidance trajectory planning in real time based on accurate obstacle information perception through multiple sensors can promote trajectory tracking technology, which can effectively improve the intelligent level of the autonomous system and reduce the frequency of traffic accidents [ 3 , 4 , 5 ]. As one of the key technologies of an active obstacle avoidance system for vehicles, the local trajectory replanning refers to designing a safe trajectory that enables AVs to promptly and accurately bypass obstacles based on global path planning [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Human-Like Obstacle Avoidance Trajectory Planning and Tracking Model for Autonomous Vehicles That Considers the Driver’s Operation Characteristics

Sun

Guo

et al. 2020

Sensors

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Developing a human-like autonomous driving system has gained increasing amounts of attention from both technology companies and academic institutions, as it can improve the interpretability and acceptance of the autonomous system. Planning a safe and human-like obstacle avoidance trajectory is one of the critical issues for the development of autonomous vehicles (AVs). However, when designing automatic obstacle avoidance systems, few studies have focused on the obstacle avoidance characteristics of human drivers. This paper aims to develop an obstacle avoidance trajectory planning and trajectory tracking model for AVs that is consistent with the characteristics of human drivers’ obstacle avoidance trajectory. Therefore, a modified artificial potential field (APF) model was established by adding a road boundary repulsive potential field and ameliorating the obstacle repulsive potential field based on the traditional APF model. The model predictive control (MPC) algorithm was combined with the APF model to make the planning model satisfy the kinematic constraints of the vehicle. In addition, a human driver’s obstacle avoidance experiment was implemented based on a six-degree-of-freedom driving simulator equipped with multiple sensors to obtain the drivers’ operation characteristics and provide a basis for parameter confirmation of the planning model. Then, a linear time-varying MPC algorithm was employed to construct the trajectory tracking model. Finally, a co-simulation model based on CarSim/Simulink was established for off-line simulation testing, and the results indicated that the proposed trajectory planning controller and the trajectory tracking controller were more human-like under the premise of ensuring the safety and comfort of the obstacle avoidance operation, providing a foundation for the development of AVs.

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“…As the advantages of dealing with the multi-variable optimization problem with diverse constraints, model predictive control (MPC) becomes the promising method. 17 Berntorp et al 18 utilize adaptive nonlinear MPC for tracking control of vehicle. Elbanhawi et al 19 propose a model predictive active yaw control implementation for pure pursuit path tracking.…”

Section: Introductionmentioning

confidence: 99%

Overtaking control strategy based on model predictive control with varying horizon for unmanned ground vehicle

Zhao

2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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In this paper, a synthesized novel strategy of varying predictive horizon-based model predictive control is proposed for the overtaking control of unmanned ground vehicle. The whole control strategy includes path planning and path tracking. First, the preferred path in presence of diverse constraints of states, inputs, and collision avoidance can be calculated using Gauss pseudospectral method where expected position, velocity, and attitude are provided. Correspondingly, the continuous optimal control problem is converted to discrete nonlinear programming. Second, model predictive control is developed for tracking the optimized path. Considering the effect of the predictive horizon and the Gauss points’ distribution on tracking performance, the varying predictive horizon is introduced to improve the tracking accuracy in non-smooth path. By the varying predictive horizon-based model predictive control method, less computation burden and better control performance are achieved. For the difference between the mathematical expressions and the real unmanned ground vehicle dynamics, genetic algorithm is utilized to identify the parameters of tire model. Simulations in MATLAB and CarSim are both implemented to illustrate the effectiveness of the proposed method.

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Trajectory Planning and Tracking for Autonomous Vehicle Based on State Lattice and Model Predictive Control

Cited by 94 publications

References 31 publications

Adaptive Potential Field-Based Path Planning for Complex Autonomous Driving Scenarios

Adaptive Potential Field-Based Path Planning for Complex Autonomous Driving Scenarios

Human-Like Obstacle Avoidance Trajectory Planning and Tracking Model for Autonomous Vehicles That Considers the Driver’s Operation Characteristics

Overtaking control strategy based on model predictive control with varying horizon for unmanned ground vehicle

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