AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving-Reference-Cited by-同舟云学术

AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving

Published:2023-07-10 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 29th Annual International Conference on Mobile Computing and Networking
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Author:

Zheng Tianyue¹^ORCID,Li Ang²^ORCID,Chen Zhe³^ORCID,Wang Hongbo¹^ORCID,Luo Jun¹^ORCID

Affiliation:

1. Nanyang Technological University, Singapore, Singapore

2. University of Maryland, College Park, USA

3. Fudan University, Shanghai, China

Funder

National Research Foundation, Singapore and Infocomm Media Development Authority under its Future Communications Research & Development Programme

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3570361.3592517

Reference78 articles.

1. Dan Barnes , Matthew Gadd , Paul Murcutt , Paul Newman , and Ingmar Posner . 2019. The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset. arXiv preprint arXiv : 1909 .01300 (2019). https://arxiv.org/pdf/1909.01300 Dan Barnes, Matthew Gadd, Paul Murcutt, Paul Newman, and Ingmar Posner. 2019. The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset. arXiv preprint arXiv: 1909.01300 (2019). https://arxiv.org/pdf/1909.01300

2. Multidimensional binary search trees used for associative searching

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

4. nuScenes: A Multimodal Dataset for Autonomous Driving

5. Multi-view 3D Object Detection Network for Autonomous Driving

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