Autonomous driving paper index
Multi-Modal Edge-AI System for Real-Time Road Health Diagnostics using Vision-Acoustic Sensor Fusion
One-line summary
This paper presents a Multi-Modal Edge AI System for Real-Time Road Health Diagnostics Using Vision-Acoustic Sensor Fusion.
Engineering notes
Key topics: autonomous driving, sensor fusion. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Road surface defects such as potholes are a major concern for road safety, vehicle performance, and infrastructure maintenance. Traditional inspection methods are often time-consuming, expensive, and unsuitable for continuous monitoring. This paper presents a Multi-Modal Edge AI System for Real-Time Road Health Diagnostics Using Vision-Acoustic Sensor Fusion. The proposed system combines OpenCV-based computer vision with vibration sensor data to improve the accuracy and reliability of pothole detection. A Raspberry Pi performs real-time edge processing, while GPS records the location of detected road defects for visualization on Google Maps. By integrating multiple sensing techniques, the system minimizes false detections and enables efficient road condition monitoring without relying on continuous cloud connectivity. The proposed approach provides a low-cost, scalable, and practical solution for intelligent transportation systems, smart city applications, and timely road maintenance.
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