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Advances in Autonomous Vehicle Testing: The State of the Art and Future Outlook on Driving Datasets, Simulators, and Proving Grounds
  • +6
  • Ao Guo,
  • * YukeLi,
  • Jun Huang,
  • * BaiLi,
  • * XiaoxiangNa,
  • Chen Lv,
  • Long Chen,
  • * LingxiLi,
  • Fei-Yue Wang
Ao Guo
Macau University of Science and Technology
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* YukeLi
Northwestern Polytechnical University
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Jun Huang
Macau University of Science and Technology
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* BaiLi
Hunan University
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* XiaoxiangNa
University of Cambridge
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Chen Lv
Nanyang Technological University
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Long Chen
Chinese Academy of Sciences Institute of Automation
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* LingxiLi
Purdue University
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Fei-Yue Wang
Macau University of Science and Technology

Corresponding Author:feiyue@ieee.org

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Abstract

As autonomous driving technology rapidly advances, effective testing tools and methods become crucial. This paper comprehensively assesses the capabilities and limitations of publicly available autonomous driving datasets, simulators, and proving grounds, exploring their roles in testing autonomous vehicles. The aim of the paper is to analyze how these tools can assist in evaluating the capabilities of autonomous driving systems and their tasks in the actual verification process of autonomous driving technology. Furthermore, this paper discusses the challenges faced by autonomous driving datasets, simulators, and proving grounds, as well as future directions for development. It provides guidance for researchers and practitioners in the field of autonomous driving, helping them choose appropriate tools and methods based on specific testing needs.
17 Jun 2024Submitted to Journal of Field Robotics
12 Jul 2024Submission Checks Completed
12 Jul 2024Assigned to Editor
12 Jul 2024Review(s) Completed, Editorial Evaluation Pending
12 Sep 2024Reviewer(s) Assigned