Highly Robust Vehicle Lateral Localization Using Multilevel Robust Network

Vision-based vehicle lateral localization has been extensively studied in the literature. However, it faces great challenges when dealing with occlusion situations where the road is frequently occluded by moving/static objects. To address the occlusion problem, we propose a highly robust lateral loc...

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Veröffentlicht in:IEEE transaction on neural networks and learning systems Jg. 34; H. 7; S. 3527 - 3537
Hauptverfasser: Zheng, Zhiyong, Li, Xu, Zhu, Jianxiao, Yuan, Jianhua, Wu, Linqi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.07.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Artificial neural networks
Cameras
Feature extraction
Localization
Location awareness
Long short-term memory
Multilevel robust network (MLRN)
Neural networks
Occlusion
Reliability
road occlusion
Roads
Robustness
Semantics
Static objects
vehicle lateral localization
ISSN:2162-237X, 2162-2388, 2162-2388
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Zusammenfassung:Vision-based vehicle lateral localization has been extensively studied in the literature. However, it faces great challenges when dealing with occlusion situations where the road is frequently occluded by moving/static objects. To address the occlusion problem, we propose a highly robust lateral localization framework called multilevel robust network (MLRN) in this article. MLRN utilizes three deep neural networks (DNNs) to reduce the impact of occluding objects on localization performance from the object, feature, and decision levels, respectively, which shows strong robustness to varying degrees of road occlusion. At the object level, an attention-guided network (AGNet) is designed to achieve accurate road detection by paying more attention to the interested road area. Then, at the feature level, a lateral-connection fully convolutional denoising autoencoder (LC-FCDAE) is proposed to learn robust location features from the road area. Finally, at the decision level, a long short-term memory (LSTM) network is used to enhance the prediction accuracy of lateral position by establishing the temporal correlations of positioning decisions. Experimental results validate the effectiveness of the proposed framework in improving the reliability and accuracy of vehicle lateral localization.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2021.3116433