Road Anomaly

Road anomaly detection focuses on automatically identifying unexpected objects or conditions on roads, primarily to enhance autonomous vehicle safety and improve infrastructure maintenance. Current research emphasizes developing efficient algorithms, often employing convolutional autoencoders or self-supervised learning methods, to process various data modalities including video, audio (tire and driving noise), and radio telescope spectrograms. These advancements aim to improve the accuracy and real-time capabilities of anomaly detection systems, addressing challenges like noise reduction, latency, and the need for efficient data storage and transmission, ultimately contributing to safer transportation and more robust infrastructure monitoring.