mlsanityData to 14 Oct 2026

Multi-modal Data based Semi-Supervised Learning for Vehicle Positioning

arXiv 2410.20680 ↗First posted 15 Oct 2024eess.SP cs.LGcited by 2

Not on the map. Why?

Abstract (arXiv)

In this paper, a multi-modal data based semi-supervised learning (SSL) framework that jointly use channel state information (CSI) data and RGB images for vehicle positioning is designed. In particular, an outdoor positioning system where the vehicle locations are determined by a base station (BS) is considered. The BS equipped with several cameras can collect a large amount of unlabeled CSI data and a small number of labeled CSI data of vehicles, and the images taken by cameras. Although the collected images contain partial information of vehicles (i.e. azimuth angles of vehicles), the relationship between the unlabeled CSI data and its azimuth angle, and the distances between the BS and the vehicles captured by images are both unknown. Therefore, the images cannot be directly used as the labels of unlabeled CSI data to train a positioning model. To exploit unlabeled CSI data and images, a SSL framework that consists of a pretraining stage and a downstream training stage is proposed. In the pretraining stage, the azimuth angles obtained from the images are considered as the labels of unlabeled CSI data to pretrain the positioning model. In the downstream training stage, a small sized labeled dataset in which the accurate vehicle positions are considered as labels is used to retrain the model. Simulation results show that the proposed method can reduce the positioning error by up to 30% compared to a baseline where the model is not pretrained.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 29 of its 52 references that are in the map. First posted on arXiv 15 Oct 2024.

Builds on

29 of 52 references.

References · 29

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17 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Equivariant Multi-agent Reinforcement Learning for Multimodal Vehicle-to-Infrastructure Systemscited by 1
  2. 2026Vision-Wireless Fusion for Multi-User Localization: A Cross-Modal Transformer Approachcited by 0