Advances in Autonomous Vehicle Testing: The State of the Art and Future
Outlook on Driving Datasets, Simulators, and Proving Grounds
- Ao Guo,
- * YukeLi,
- Jun Huang,
- * BaiLi,
- * XiaoxiangNa,
- Chen Lv,
- Long Chen,
- * LingxiLi,
- Fei-Yue Wang
Long Chen
Chinese Academy of Sciences Institute of Automation
Author ProfileAbstract
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