Path Planning and Trajectory Tracking Strategy of Autonomous Vehicles

Abstract

With the development of global urbanization and the construction of regional urbanization, residents around urban cities are increasingly making demands on urban public transportation system. A new kind of modern public transportation vehicle named Multi-Articulated Guided Vehicle based on Virtual Track (MAAV-VT) with the advantages of beautiful, smart energy conservation and environmental protection is proposed in this paper, which aims at optimizing the public transportation system between and within urban areas. Therefore, concentrating on the general design and control strategy, the main contents of this paper are as follows. At first, the design concepts and key technologies of MAAV-VT are introduced. It is the fusion of urban rail transit operation mode and advanced automotive technologies, which have the characteristics of 100% low-floor, medium to high velocity, medium to big capacity, and low construction cost. Then, as the core subsystem, to guarantee the properties of self-guiding and trajectory tracking of the new vehicle, this paper is focused on the control system based on the dynamics and kinematics model of the whole multi-articulated vehicle. The multi-trace-points cooperative trajectory tracking control strategy on the basis of the circulation of feasible path generation method is proposed and the lateral controller is designed for trajectory tracking. The process of feasible path generation is conducted once the tracking error exceeded. A simulation platform is built considering the mechanical properties of each vehicle element and the characteristic of articulated mechanism. Finally, the function of control system is validated. The tracking error of each vehicle elements would be reduced to make sure the whole multi-articulated vehicle moves along the preset virtual track.

Distributed Drive Autonomous Vehicle Trajectory Tracking Control Based on Multi-Agent Deep Reinforcement Learning

“…As stated earlier, trajectory tracking in DDAVs is a responsive outcome under multiexecution system control involving both the propulsion and steering systems. Because of the transformation between the vehicle's coordinate system and the global coordinate system, the steering system not only induces lateral position changes but also affects the vehicle's longitudinal velocity [31]. Therefore, when employing deep reinforcement learning methods for joint longitudinal and lateral control of DDAVs, the complexity arises from the multitude of vehicle state indicators and control objectives.…”

A tracking control method for articulated virtual rail train with N-unit based on the dynamic model

“…Articulated vehicles are widely used in transport for their economy, energy efficiency, environmental protection, and large transport capacity and are extensively investigated as urban public vehicles. [1][2][3][4][5][6][7][8] In this paper, a new articulated virtual rail train (AVRT) is investigated to improve the urban public transportation convenience and capacity, and the architecture is shown in Figure 1.…”

Path Planning For Unmanned Surface Vehicles Based On Modified Artificial Fish Swarm Algorithm With Local Optimizer

“…Recently, there has been a growing appeal for path planning algorithms with the vigorous development of artifcial intelligence [5,6]. Developing path planning algorithms with high computing efciency, robustness, and high-quality solutions is an attractive topic in the current studies [7][8][9]. Qin et al [10] proposed a rapid USV path planning algorithm to decrease the computational complexity.…”