Deep Learning for Real-Time Anomaly Detection in Autonomous Vehicles - A Computational Intelligence Perspective: Explores the use of deep learning for real-time anomaly detection in AVs, from a computational intelligence viewpoint

Abstract

This paper presents a comprehensive study on the application of deep learning for real-time anomaly detection in autonomous vehicles (AVs) from a computational intelligence perspective. With the rapid advancement of AV technology, ensuring the safety and reliability of these vehicles has become paramount. Traditional rule-based anomaly detection systems often struggle to handle the complexity and variability of real-world driving scenarios. Deep learning, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), has shown promising results in detecting anomalies in various domains. This paper investigates the effectiveness of deep learning models for detecting anomalies in AVs and discusses the challenges and future directions in this field. Experimental results demonstrate the superiority of deep learning-based approaches in detecting anomalies in real-time AV environments.