A Motion Planning and Tracking Framework for Autonomous Vehicles Based on Artificial Potential Field Elaborated Resistance Network Approach

This paper presents a novel motion planning and tracking framework for automated vehicles based on artificial potential field (APF) elaborated resistance approach. Motion planning is one of the key parts of autonomous driving, which plans a sequence of movement states to help vehicles drive safely,...

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Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) Vol. 67; no. 2; pp. 1376 - 1386
Main Authors: Huang, Yanjun, Ding, Haitao, Zhang, Yubiao, Wang, Hong, Cao, Dongpu, Xu, Nan, Hu, Chuan
Format: Journal Article
Language:English
Published: New York IEEE 01.02.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Automotive parts
Autonomous vehicle
Autonomous vehicles
Collision avoidance
Heuristic algorithms
Human motion
Local current
model predictive controller
Motion planning
Motional resistance
obstacle avoidance
Planning
Potential fields
Predictive control
Resistance
resistance network
Roads
Tracking
Traffic speed
Trajectory
Trajectory analysis
Vehicles
ISSN:0278-0046, 1557-9948
Online Access:Get full text
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Summary:This paper presents a novel motion planning and tracking framework for automated vehicles based on artificial potential field (APF) elaborated resistance approach. Motion planning is one of the key parts of autonomous driving, which plans a sequence of movement states to help vehicles drive safely, comfortably, economically, human-like, etc. In this paper, the APF method is used to assign different potential functions to different obstacles and road boundaries; while the drivable area is meshed and assigned resistance values in each edge based on the potential functions. A local current comparison method is employed to find a collision-free path. As opposed to a path, the vehicle motion or trajectory should be planned spatiotemporally. Therefore, the entire planning process is divided into two spaces, namely the virtual and actual. In the virtual space, the vehicle trajectory is predicted and executed step by step over a short horizon with the current vehicle speed. Then, the predicted trajectory is evaluated to decide if the speed should be kept or changed. Finally, it will be sent to the actual space, where an experimentally validated Carsim model controlled by a model predictive controller is used to track the planned trajectory. Several case studies are presented to demonstrate the effectiveness of the proposed framework.
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ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2019.2898599