Project description :
Summary (MAX of 2100 characters with spaces)
This project aims to establish new design methods for robust and efficient automotive navigation and optimal management of a fleet of vehicles in harsh environments. In addition, the project also aims to develop innovative metrics for real-time analysis of dangerous driving behaviour as well as real-time analysis of car accidents in order to significantly improve global safety of Canadian drivers. In general, this research proposes to combine measurements from a high sensitivity GPS receiver with data coming from a self-contained inertial navigation system and other complementary autonomous sensors such as odometers and magnetometers. Moreover, in order to provide an affordable solution, the targeted system will be based exclusively on the use of very low cost sensors. It is expected that this project will help reduce the environmental footprint of motor vehicles in addition to having a significant positive impact on overall vehicle safety. For example, improving vehicle localisation accuracy and robustness in harsh environments can significantly reduce the time to find a stolen or misplaced vehicle, which can have an important impact on Canadian companies’ finances. Furthermore, having a robust and precise solution for monitoring vehicle behaviour can lead to the implementation of a new taxation system based on car usage or on driving behaviour, which according to recent studies, can help reduce vehicle greenhouse gas emissions by up to 10%. In addition, accurate reconstruction of car accidents in real-time allow prediction of specific parameters of an accident scene thus improving reaction time and vehicle safety. The proof-of-concept demonstrator will be evaluated in-laboratory and on-road using simulation equipment and a car test platform under real operating conditions in order to characterize protocols and system performance. The project will contribute to international initiatives for the definition of new standards and contribute to Canadian efforts to reduce greenhouse gas emissions, and create new employment opportunities for the team of highly qualified personnel.
Responsibilities of the candidate:
According to the schedule, this master’s student will be in charge of the following tasks:
1) 2-21 Review of the actual analysis metrics
2) 2-22 Modeling of the preliminary analysis metrics
3) 2-23 Metrics implementation using simulation software
4) 2-24 Metrics validation using simulation tools
5) 3-41 Implementation of the driving behaviour analysis metrics
6) 3-42 Real car test setups and planning
7) 3-43 Driving behaviour test realisation
8) 3-44 Results analysis/study on possible metrics improvement
9) 3-45 Modeling and testing of improved analysis metrics
The general objective of the Master’s research is to develop preliminary metrics that will be used to identify and quantify dangerous driving behaviours based on a multi-sensor analysis. This student will first conduct a comprehensive review of the current analysis metrics and evaluate how the new available measurements could help improving these metrics. Folowing this study, the student will conduct massive data recording sessions with a large number of sensors placed at several places on the vehicle in order to properly characterize the normal and dangerous driving behavior in terms of raw inertial measurements. Based on these measurements, the student will establish different metrics that will quantify the level of danger of a user’s driving behaviours. Finally, the student will conduct a study to identify strategic locations where inertial sensors should be placed on the vehicle in order to fully capture the necessary informations for the developed analysis metrics.
