Driving Anomaly Detection Using Recurrent Neural Networks
Date
2022-03-25
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Abstract
Deep learning has changed many aspects of our lives in recent years. Every day, the improvements in artificial intelligence make computers more capable of doing our daily tasks. Traffic management has never been separated from these changes. Researchers have proposed many machine learning solutions to help traffic management centers monitor vehicles’ activities in the transportation networks. Driving anomaly detection refers to finding unexpected vehicles, situations and traffic flows in the transportation systems. Many research works have been conducted recently to address driving anomaly detection problem, however each of these solutions has drawbacks. This thesis suggests two innovative solutions for detecting anomalies in intelligent transportation systems using recurrent neural networks (RNNs). A brief introduction of driving anomaly detection techniques and RNNs is presented in the first part of the thesis. Then in the second part, two suggested solutions, DeepFlow and ThirdEye, are discussed. DeepFlow is a method to detect abnormal traffic flows in smart cities. It is argued in this thesis that finding a complete dataset of vehicles’ behaviors in driving scenarios is very difficult. To address this issue the DeepFlow solution from this thesis applies machine learning techniques to reduce the requirement for a comprehensive dataset without loosing accuracy. ThirdEye, the second solution introduced, focuses on detecting anomalous behaviors of driver-less vehicles. This model works based on predicting the vehicle’s state in the future. By measuring the distance between the actual state of the vehicle and the predicted one, the system can detect more than 90% of anomalies. Three different recurrent neural networks were tested to determine the best for ThirdEye.
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Keywords
Machine Learning, Anomaly Detection, Siamese Network, Recurrent Neural Network
Citation
Sabour, S. (2022). Driving Anomaly Detection Using Recurrent Neural Networks (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.