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) 2-11 Review of the actual GNSS assistance methods
2) 2-12 Initial implementation of the GPS assistance methods
3) 2-13 Tests and validation of the initial implementation of the assistance methods
4) 2-14 Integrating multi-constellation capabilities to the actual system
5) 3-21 Implementation of the GNSS receiver assitance methods
6) 3-22 Study of A-GNSS integration schemes
7) 3-23 A-GNSS integration with navigation algorithms / analysis metrics
8) 3-24 Real-time tests and validation of the A-GNSS integration
The general objective of the Master’s research project is to realize the implementation of different assistance methods to improve the overall robustness of a very low-cost GNSS receiver in urban environment. To achieve this objective, the student will first realize an in-depth literature review on assisted GPS (A-GPS) and other non-intrusive methods (i.e. that does not require modification to the receiver’s internal loops) that can allow faster signal acquisition/re-acquisition in urban environment and therefore help reduce the time to first fix (TTFF) of the receiver. Following this theoretical study, the student will start getting familiar with the current GPS module contained within the Orchid platform (i.e. GNS7560 from ST-Ericsson). The student will perform an initial implementation of the algorithms using this module and then he will explore the possibility of replacing it by a multi-constellation module (i.e. GPS and Glonass).
