Efficient Recognition of Broken Road Surface for Autonomous Driving using Broad Learning System

In the era of artificial intelligence, assessing road damage in autonomous driving applications is of paramount importance. Accurate and timely identification of road surface damage is critical for ensuring safety and maintaining vehicle performance. This paper proposes the utilization of Broad Lear...

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Bibliographic Details
Published in2024 5th International Conference on Computer, Big Data and Artificial Intelligence (ICCBD+AI) pp. 431 - 436
Main Authors Zhu, Junhao, Ji, Tong, Liu, Hui
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2024
Subjects
Accuracy
Autonomous Driving
Autonomous vehicles
Broad Learning System (BLS)
Feature extraction
Learning systems
Real-time systems
Roads
Safety
Surface reconstruction
Surface treatment
Surface waves
Timely identification
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DOI10.1109/ICCBD-AI65562.2024.00078

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Summary:In the era of artificial intelligence, assessing road damage in autonomous driving applications is of paramount importance. Accurate and timely identification of road surface damage is critical for ensuring safety and maintaining vehicle performance. This paper proposes the utilization of Broad Learning System (BLS) for rapid identification and classification of road surface damage, with a strong emphasis on real-time processing capabilities. Our approach leverages the efficiency and adaptability of BLS to provide swift and reliable road condition assessments, demonstrating significant potential for enhancing the safety and reliability of autonomous driving systems.
DOI:10.1109/ICCBD-AI65562.2024.00078