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 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.