Grant: Alliance Grants
University of Waterloo
$204,566.00
Development of an All-condition Dependable Vehicle Health Monitoring System
EF 2023-24
Résumé
The automotive industry is in a transformational period of vehicle technology advancement. Significant progress in low-cost sensors, high processing power, and ubiquitous real-time information access are making vehicles safer and more intelligent, in turn providing better control and paving the way to fully automated driving. This unprecedented evolution in transferring human-centered decisions to machine-centered decisions increases reliance on algorithms to control vehicles, leading to significant concerns related to reliability, accuracy, health and safety. Today's Advanced Driver Assistance Systems (ADAS) increase safety by reducing human error, but still require driver attention and interaction to transfer the system control and make decisions in cases of ambiguity. Rigorous diagnosis algorithms are essential for safe and robust performance of vehicle active safety systems and, more importantly, for fully automated driving systems. In current systems, the focus is on sensor and actuator fault detection and not on state estimation or control systems. The proposed research project will develop vehicle health monitoring systems that are critical to automated driving and ADAS which include accuracy and performance monitoring of safety critical estimation and control units, and powertrain systems. There are very limited works in health monitoring of estimation and control modules although they are critical in vehicles equipped with ADAS and automated driving in which driver's attention is not fully required. Any full or partial failure in electric powertrain could seriously endanger the safety of the vehicle for which a robust real-time monitoring is needed. This research will develop the necessary knowledge and technologies for practical and dependable automated driving technology in all conditions, making automated driving and overall road travel safer. This interdisciplinary research will provide a dynamic training environment, exposing students and engineers to a broad range of disciplines and technologies, as well as the opportunity to develop research skills with practical applications in the evolving Canadian transportation industry.
Programme
Alliance Grants
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
566320-2021
Name nom
Khajepour, Amir A
Department département
Mechanical Engineering and Mechatronics
Organization id
33
Institution établissement
University of Waterloo
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2023
Competition year année de concours
2021
Award amount
204566
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
1206
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Engineering
Area of application fr
Génie
Research subject code
1150
Research subject group en
Systems engineering
Research subject group fr
Systems engineering
Research subject en
Génie des systèmes
Research subject fr
Génie des systèmes
Application title
Development of an All-condition Dependable Vehicle Health Monitoring System
Keyword
ADAS Systems; Automated driving; Electric Powertrains; Estimation and control failure; Health monitoring systems; Supervised and unsupervised learning; Vehicle control; Vehicle Safety; Vehicle states estimation; Vehicle testing
Application summary
The automotive industry is in a transformational period of vehicle technology advancement. Significant progress in low-cost sensors, high processing power, and ubiquitous real-time information access are making vehicles safer and more intelligent, in turn providing better control and paving the way to fully automated driving. This unprecedented evolution in transferring human-centered decisions to machine-centered decisions increases reliance on algorithms to control vehicles, leading to significant concerns related to reliability, accuracy, health and safety. Today's Advanced Driver Assistance Systems (ADAS) increase safety by reducing human error, but still require driver attention and interaction to transfer the system control and make decisions in cases of ambiguity. Rigorous diagnosis algorithms are essential for safe and robust performance of vehicle active safety systems and, more importantly, for fully automated driving systems. In current systems, the focus is on sensor and actuator fault detection and not on state estimation or control systems. The proposed research project will develop vehicle health monitoring systems that are critical to automated driving and ADAS which include accuracy and performance monitoring of safety critical estimation and control units, and powertrain systems. There are very limited works in health monitoring of estimation and control modules although they are critical in vehicles equipped with ADAS and automated driving in which driver's attention is not fully required. Any full or partial failure in electric powertrain could seriously endanger the safety of the vehicle for which a robust real-time monitoring is needed. This research will develop the necessary knowledge and technologies for practical and dependable automated driving technology in all conditions, making automated driving and overall road travel safer. This interdisciplinary research will provide a dynamic training environment, exposing students and engineers to a broad range of disciplines and technologies, as well as the opportunity to develop research skills with practical applications in the evolving Canadian transportation industry.