Road Surface State Change Detection based on Binocular Vision for Autonomous Driving System

Road surface condition monitoring is crucial for enhancing transportation safety and efficiency, with applications in autonomous driving and urban infrastructure management. Existing methods often rely on single-camera setups or manual inspections, which are either insufficient for real-time monitoring or labor-intensive. This system focuses on two critical factors: road slope and surface damage, both significantly impacting driving safety and experience, highlighting the need for timely detection. To ensure accuracy and robustness, the system employs a binocular camera for detailed road environment insights and integrates urban sensing techniques. Its hardware deployment processes stereo vision data on embedded platforms, ensuring compatibility with urban IoT networks. This approach surpasses single-camera systems in detecting road surface variations. The research motivation stems from the pressing need to enhance road safety and driving conditions in urban areas. By analyzing binocular camera data and urban sensing technologies, the system offers real-time road condition analysis for effective decision-making. Regarding results, the system showed robust performance in detecting both road slope and surface damage. Slope detection achieved high accuracy with minimal error, and road damage detection reached an overall accuracy of 84%. The system remained stable across diverse conditions, including adverse weather and varying lighting.