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) 1-13 Training on Orchid platform and associated tools
2) 1-14 Technical study on vehicles’ embedded sensors and data networks
3) 1-33 Study of car’s embedded sensors and available signals
4) 1-34 Initial system architecture selection
5) 1-71 Review of the actual monitoring data networks and their associated acquisition methods
6) 1-72 Identification of the various monitoring data available on the networks
7) 1-73 Evaluation and seletion of the most relevant monitoring data
8) 1-74 Measurements analysis on multiple vehicles
9) 2-81 Development of data transmission protocols
10) 2-82 Study vehicle monitoring data integration schemes
11) 2-83 Monitoring data integration with navigation algorithms / analysis metrics
12) 2-84 Real-time tests and validation of integrated monitoring data integration
The general objective of the Master’s research project is to study the various data networks available in recent automotive vehicles (e.g. LIN, CAN, MOST, etc.) and the corresponding measurements in order to incorporate some relevant data within the navigation algorithms and analysis metrics. During the early stages of its project, the student will first conduct a comprehensive study of the different data bus available in recent vehicles and their different characteristics (i.e. communication protocol, accessing methods, data rate, etc.). The student will first need to use a commercial acquisition card, then he will develop its own communications protocols to access relevant selected data. Finally, the student will explore different integration schemes to integrate the selected measurements within the navigation algorithms and analysis metrics.
