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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Vydáno v:	IEEE transactions on industrial electronics (1982) Ročník 67; číslo 2; s. 1376 - 1386
Hlavní autoři:	Huang, Yanjun, Ding, Haitao, Zhang, Yubiao, Wang, Hong, Cao, Dongpu, Xu, Nan, Hu, Chuan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.02.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
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Abstract	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.
AbstractList	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.
Author	Huang, Yanjun Ding, Haitao Cao, Dongpu Zhang, Yubiao Wang, Hong Hu, Chuan Xu, Nan
Author_xml	– sequence: 1 givenname: Yanjun orcidid: 0000-0003-3133-8031 surname: Huang fullname: Huang, Yanjun email: huangyanjun404@gmail.com organization: State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, China – sequence: 2 givenname: Haitao orcidid: 0000-0003-2729-2907 surname: Ding fullname: Ding, Haitao email: dinght@jlu.edu.cn organization: State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, China – sequence: 3 givenname: Yubiao orcidid: 0000-0002-3843-3384 surname: Zhang fullname: Zhang, Yubiao email: gary.zhang@uwaterloo.ca organization: Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON, Canada – sequence: 4 givenname: Hong orcidid: 0000-0002-0279-3767 surname: Wang fullname: Wang, Hong email: wanghongbit@gmail.com organization: Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON, Canada – sequence: 5 givenname: Dongpu surname: Cao fullname: Cao, Dongpu email: dongpu.cao@uwaterloo.ca organization: Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON, Canada – sequence: 6 givenname: Nan surname: Xu fullname: Xu, Nan email: xu.nan0612@gmail.com organization: State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, China – sequence: 7 givenname: Chuan orcidid: 0000-0001-5379-1561 surname: Hu fullname: Hu, Chuan email: chuan.hu.2013@gmail.com organization: Department of Mechanical Engineering, University of Texas at Austin, Austin, TX, USA
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Snippet	This paper presents a novel motion planning and tracking framework for automated vehicles based on artificial potential field (APF) elaborated resistance...
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SubjectTerms	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
Title	A Motion Planning and Tracking Framework for Autonomous Vehicles Based on Artificial Potential Field Elaborated Resistance Network Approach
URI	https://ieeexplore.ieee.org/document/8643096 https://www.proquest.com/docview/2300338951
Volume	67
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