Deep Learning-based Anomaly Detection in Radar Data with Radar-Camera Fusion

초록

Sensors such as cameras, lidars, and radars are crucial to understanding driving situations in autonomous vehicles. These sensors are susceptible to external and internal abnormalities, potentially leading to severe traffic accidents. A radar sensor is inevitably affected by the obstruction caused by small objects, which can cause the system to malfunction. This paper presents a deep learning approach for detecting anomalies in radar data. The accuracy of anomaly detection is improved by using radar-camera fusion. Our proposed model detects the data anomaly by calculating the deviation from the standard radar cross section (RCS) range. The result demonstrates that the model is capable of identifying the normal range of radar signal and anomaly signal under several different obtained features situations. It enables the detection of potential hazards and warns of dangers to drivers and higher-level control systems, creating a more resilient environment for ensuring autonomous driving safety.

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