Scene adaptation in adverse conditions: a multi-sensor fusion framework for roadside traffic perception-Reference-Cited by-同舟云学术

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Scene adaptation in adverse conditions: a multi-sensor fusion framework for roadside traffic perception

Published:2024-08-19 Issue: Volume: Page:1-21
ISSN:1547-2450
Container-title:Journal of Intelligent Transportation Systems
language:en
Short-container-title:Journal of Intelligent Transportation Systems

Author:

Li Kong¹,Dai Zhe²^ORCID,Zuo Chen²,Wang Xuan³,Cui Hua⁴,Song Huansheng¹,Cui Mengying²

Affiliation:

1. School of Information Engineering, Chang’an University, Xi’an, China

2. School of Transportation Engineering, Chang’an University, Xi’an, China

3. School of Computer and Control Engineering, Yantai University, Yantai, China

4. School of Future Transportation, Chang’an University, Xi’an, China

Funder

Postdoctoral Science Foundation of China

Natural Science Foundation of Shaanxi Province

Department of Transportation science and technology project of Zhejiang Province

National Natural Science Foundation of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/15472450.2024.2390844

Reference53 articles.

1. Aharon N. Orfaig R. & Bobrovsky B.-Z. (2022). BoT-SORT: Robust associations multi-pedestrian tracking. ArXiv abs/2206.14651.

2. Vehicle and Guard Rail Detection Using Radar and Vision Data Fusion

3. Bansal K. Rungta K. & Bharadia D. (2022). Radsegnet: A reliable approach to radar camera fusion. arXiv preprint arXiv:2208.03849

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

5. HRFuser: A Multi-Resolution Sensor Fusion Architecture for 2D Object Detection

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