Benchmarking Robustness of AI-Enabled Multi-sensor Fusion Systems: Challenges and Opportunities-Reference-Cited by-同舟云学术

Benchmarking Robustness of AI-Enabled Multi-sensor Fusion Systems: Challenges and Opportunities

Published:2023-11-30 Issue: Volume: Page:
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Container-title:Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering
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Author:

Gao Xinyu¹,Wang Zhijie²,Feng Yang¹,Ma Lei³,Chen Zhenyu¹,Xu Baowen¹

Affiliation:

1. Nanjing University, Nanjing, China

2. University of Alberta, Edmonton, Canada

3. University of Tokyo, Tokyo, Japan / University of Alberta, Edmonton, Canada

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3611643.3616278

Reference51 articles.

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2. Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics

3. Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather

4. Keenan Burnett , David J Yoon , Yuchen Wu , Andrew Zou Li , Haowei Zhang, Shichen Lu, Jingxing Qian, Wei-Kang Tseng, Andrew Lambert, and Keith YK Leung. 2022 . Boreas : A Multi-Season Autonomous Driving Dataset . arXiv preprint arXiv:2203.10168. Keenan Burnett, David J Yoon, Yuchen Wu, Andrew Zou Li, Haowei Zhang, Shichen Lu, Jingxing Qian, Wei-Kang Tseng, Andrew Lambert, and Keith YK Leung. 2022. Boreas: A Multi-Season Autonomous Driving Dataset. arXiv preprint arXiv:2203.10168.

5. nuScenes: A Multimodal Dataset for Autonomous Driving

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