Electronic Thesis and Dissertation Repository

Thesis Format

Integrated Article

Degree

Doctor of Philosophy

Program

Computer Science

Supervisor

Steven S. Beauchemin

2nd Supervisor

Michael A. Bauer

Co-Supervisor

Abstract

The number of vehicles on the roads increases every day. According to the National Highway Traffic Safety Administration (NHTSA), the overwhelming majority of serious crashes (over 94 percent) are caused by human error. The broad aim of this research is to develop a driver behavior model using real on-road data in the design of Advanced Driving Assistance Systems (ADASs). For several decades, these systems have been a focus of many researchers and vehicle manufacturers in order to increase vehicle and road safety and assist drivers in different driving situations. Some studies have concentrated on drivers as the main actor in most driving circumstances. The way a driver monitors the traffic environment partially indicates the level of driver awareness. As an objective, we carry out a quantitative and qualitative analysis of driver behavior to identify the relationship between a driver’s intention and his/her actions. The RoadLAB project developed an instrumented vehicle equipped with On-Board Diagnostic systems (OBD-II), a stereo imaging system, and a non-contact eye tracker system to record some synchronized driving data of the driver cephalo-ocular behavior, the vehicle itself, and traffic environment. We analyze several behavioral features of the drivers to realize the potential relevant relationship between driver behavior and the anticipation of the next driver maneuver as well as to reach a better understanding of driver behavior while in the act of driving. Moreover, we detect and classify road lanes in the urban and suburban areas as they provide contextual information. Our experimental results show that our proposed models reached the F1 score of 84% and the accuracy of 94% for driver maneuver prediction and lane type classification respectively.

Summary for Lay Audience

The large number of vehicle collisions leads to both tremendous human and economic costs. Road traffic injury is the leading cause of death among young people and children aged 5-29 years and makes road fatalities the eighth leading cause of death across all age groups. Evidence has shown that a significant number of vehicle accidents are due to driver error. The broad aim of this research is to develop a driver behavior model using real on-road data in the design of Advanced Driving Assistance Systems (ADASs). In many driving situations, drivers may receive an alert from their passengers to avoid an accident with another vehicle or a pedestrian. This role can be played by an intelligent ADAS by warning the driver or even intervening if ADAS finds it necessary. An intelligent ADAS can understand and benefit from valuable information including the state of the driver’s behavior, the vehicle, and the environment to analyze driver behavior in different driving situations as well as to predict driver maneuvers. We analyze several behavioral features of the drivers to realize the potential relevant relationship between driver behavior and the anticipation of the next driver maneuver as well as to reach a better understanding of driver behavior while in the act of driving.

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