Driving Anomaly Detection Using Recurrent Neural Networks

Sabour, Sepehr

Driving Anomaly Detection Using Recurrent Neural Networks

dc.contributor.advisor	Ghaderi, Majid
dc.contributor.author	Sabour, Sepehr
dc.contributor.committeemember	Stefanakis, Emmanuel
dc.contributor.committeemember	Hudson, Jonathan William
dc.date	2022-06
dc.date.accessioned	2022-03-30T16:01:54Z
dc.date.available	2022-03-30T16:01:54Z
dc.date.issued	2022-03-25
dc.description.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.	en_US
dc.identifier.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.	en_US
dc.identifier.doi	http://dx.doi.org/10.11575/PRISM/39666
dc.identifier.uri	http://hdl.handle.net/1880/114517
dc.language.iso	eng	en_US
dc.publisher.faculty	Science	en_US
dc.publisher.institution	University of Calgary	en
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.	en_US
dc.subject	Machine Learning	en_US
dc.subject	Anomaly Detection	en_US
dc.subject	Siamese Network	en_US
dc.subject	Recurrent Neural Network	en_US
dc.subject.classification	Computer Science	en_US
dc.title	Driving Anomaly Detection Using Recurrent Neural Networks	en_US
dc.type	master thesis	en_US
thesis.degree.discipline	Computer Science	en_US
thesis.degree.grantor	University of Calgary	en_US
thesis.degree.name	Master of Science (MSc)	en_US
ucalgary.item.requestcopy	false	en_US