Non-Compression Auto-Encoder for Detecting Road Surface Abnormality via Vehicle Driving Noise

Abstract

Road accident can be triggered by wet road because it decreases skid resistance. To prevent the road accident, detecting road surface abnomality can be helpful. In this paper, we propose the deep learning based cost-effective real-time anomaly detection architecture, naming with non-compression auto-encoder (NCAE). The proposed architecture can reflect forward and backward causality of time series information via convolution operation. Moreover, the above architecture shows higher anomaly detection performance of published anomaly detection model via experiments. We conclude that NCAE is a cutting-edge model for road surface anomaly detection.

Noise Reduction and Driving Event Extraction Method for Performance Improvement on Driving Noise-based Surface Anomaly Detection

“…We have developed a model NCAE for driving noisebased anomaly detection to ease the above problem [3]. The NCAE shows an improvement in the anomaly detection performance compared to the prior models while increasing the computational efficiency.…”