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) 1-11 In-depth literature review on project topics
2) 1-15 Technical study on AC1120S rate table
3) 1-51 Magnetometer data acquisition and comprehensive analysis
4) 1-52 Preliminary modeling of the AHRS algorithm
5) 1-53 Test / validation of the initial AHRS algorithms
6) 1-54 Study of soft and hard iron effects
7) 2-51 Modeling of the magnetic disturbance detection / compensation algorithm
8) 2-52 Test / validation of the complete AHRS algorithm
9) 2-71 Study of advanced sensor error estimation models
10) 2-72 Comprehensive study on sensor errors
11) 2-73 Mathematical modeling of online calibration algorithms
12) 2-74 Initial implementation of the online calibration models
The general objective of the Master’s research project is to develop a robust and precise attitude and heading reference system (AHRS) for low-cost car navigation based principally on magnetometer measurements. During the early stage of the project, the master degree student will first realize an in-depth literature review on magnetometer-based AHRS as well as on low-cost magnetometers in general and their associated sources of error (i.e. soft and hard iron effects). Following this theoretical study, the student will develop an AHRS model using simulation tools (i.e. Matlab/Simulink) and conduct a comprehensive analysis on the sensors measurements in order to practically understand the behavior of these error sources in various environments. Once these errors have been analysed, the student will be able to model it and assess its impact on the AHRS solution with the objective to develop a method for detection and correction of the magnetic field disturbance. As a first step, the Master’s student will develop this detection/correction method in simulation with simulated but realistic disturbances and then, it will be tested and validated in a real but controlled environment with known disturbance sources (i.e. strong magnets). Finally, the student will test and validate its implementation in real car navigation scenarios.
