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A novel multimodal fusion network based on a joint-coding model for lane line segmentation

Zou, Zhenhong; Zhang, Xinyu; Liu, Huaping; Li, Zhiwei; Hussain, Amir; Li, Jun


Zhenhong Zou

Xinyu Zhang

Huaping Liu

Zhiwei Li

Jun Li


There has recently been growing interest in utilizing multimodal sensors to achieve robust lane line segmentation. In this paper, we introduce a novel multimodal fusion architecture from an information theory perspective, and demonstrate its practical utility using Light Detection and Ranging (LiDAR) camera fusion networks. In particular, we develop, for the first time, a multimodal fusion network as a joint coding model, where each single node, layer, and pipeline is represented as a channel. The forward propagation is thus equal to the information transmission in the channels. Then, we can qualitatively and quantitatively analyze the effect of different fusion approaches. We argue the optimal fusion architecture is related to the essential capacity and its allocation based on the source and channel. To test this multimodal fusion hypothesis, we progressively determine a series of multimodal models based on the proposed fusion methods and evaluate them on the KITTI and the A2D2 datasets. Our optimal fusion network achieves 85%+ lane line accuracy and 98.7%+ overall. The performance gap among the models will inform continuing future research into development of optimal fusion algorithms for the deep multimodal learning community.

Journal Article Type Article
Acceptance Date Oct 31, 2021
Online Publication Date Nov 13, 2021
Publication Date 2022-04
Deposit Date Jan 5, 2022
Journal Information Fusion
Print ISSN 1566-2535
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 80
Pages 167-178
Keywords Multimodal fusion, Information theory, Lane line segmentation, Semantic segmentation, Neural Network
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