Robust Multisensor-based Framework for Efficient Road Information Services

Loading...
Thumbnail Image

Authors

Elwakeel, Amr

Date

Type

thesis

Language

eng

Keyword

Signal Processing , Machine Learning , Road Information Services , Sensor Fusion , Intelligent Transportation Systems , Route Planning , Integrated Positioning

Research Projects

Organizational Units

Journal Issue

Alternative Title

Abstract

Next-generation intelligent transportation systems of future road traffic monitoring will be required to provide reports of road conditions. Monitoring road surface conditions benefits drivers and the community. Road surface anomalies contribute to increased risk of traffic accidents, reduced driver comfort and increased wear of vehicles. Municipalities usually inspect the road surface quality through manual eye inspection that is inefficient, exhausting and consumes resources. Motivated by the growing demand for efficient road information services for connected and automated vehicles, this thesis proposes a comprehensive framework for monitoring road surface conditions. An intelligent and robust road surface monitoring system integrating vehicular and smart devices’ inertial sensors and GNSS receivers within and amongst vehicles is designed and developed in this research. To ensure rich data collections, land vehicles of different types are used in the road tests. A wavelet packet analysis was applied to the sensor measurements to separate the road anomalies’ signatures from both the vehicle motion dynamics and the sensor noise. Various feature extraction techniques were used to identify several types of road anomalies. The constructed features were utilized in building datasets of these road anomalies. The datasets were leveraged to build and assess the performance of a support vector machine classifier to detect and categorize multiple road anomalies of different severity levels. In order to overcome the GNSS receivers' challenges in geo-referencing the monitored anomalies,. In this framework, the vehicles’ onboard motion sensors are integrated with GNSS receivers to provide accurate, continuous, and adaptive resolution geo-referencing to the monitored anomalies. The road anomaly information is then utilized to provide assessment for the average quality of the road segments using a fuzzy inference system. The developed road segment quality database was then used by another fuzzy classifier that can be utilized in route planning to evaluate all possible routes and suggest the one of highest road quality. The future deployment of the system developed in this research will contribute to reducing congestion and accidents along the roads, enabling road labelling based on accessibility and conditions, providing personalized alerts and route recommendations, and improving road safety for all drivers.

Description

Citation

Publisher

License

Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada
ProQuest PhD and Master's Theses International Dissemination Agreement
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.

Journal

Volume

Issue

PubMed ID

External DOI

ISSN

EISSN