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) 3-71 Review of the actual analysis metrics
2) 3-72 Modeling of the preliminary analysis metrics
3) 3-73 Metrics implementation using simulation software
4) 3-74 Metrics validation using simulation tools
5) 3-81 Implementation of the accident analysis metrics
6) 3-82 Real car test setups and planning
7) 3-83 Real car accident test realisation
8) 3-84 Results analysis/study on possible metrics improvement
9) 3-85 Modeling and testing of improved analysis metrics
10) 4-21 Modeling of improved analysis metrics
11) 4-22 Metric validation in controlled environment
12) 4-23 Final driving behaviour test realisation
13) 4-24 Final real car accident tests realisation
14) 4-25 Analysis metrics validation and performance analysis
The general objective of the Master’s research is to develop preliminary metrics that will be used to analyse and better understand car accident scenarios based on a multi-sensor analysis. This student will first conduct a comprehensive review of the currently available metrics and evaluate how the new measurements could help improving these metrics. Since it is difficult and costly to collect measurements from a real car accident, this student will first analyse measurements from simulation software (e.g. X-Plane) and from different data bases available from other research groups. Based on these measurements, the student will select appropriate sensors and location on the vehicle for the development of the accident analysis and diagnosis system. Subsequently, the student will perform intensive data acquisition in simulation and small scale tests on various accident scenarios in order to determine which measurements best describe the dynamics of a car accident. Following this acquisition phase, the student will analyze the results in order to establish different metrics that will help identify different types of car accidents as well as their gravity.
