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) 1-41 Inertial data acquisition and comprehensive analysis
4) 1-42 Preliminary modeling of the calibration procedure
5) 1-43 Test / validation of the initial calibration procedure
6) 1-44 Study of temperature effect on inertial measurements
7) 2-41 Modeling of the temperature dependant model
8) 2-42 Test / validation of the complete calibration procedure
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 complete temperature dependant model for in-lab as well online calibration of very low-cost inertial sensors (i.e. accelerometers and gyroscopes). During the early stage of the project, the masters degree student will first realize an in-depth literature review on low-cost sensor calibration and temperature dependant models. Following this theoretical study, the student will conduct a comprehensive analysis on the measurements from selected inertial sensors in order to identify their principal error sources. Once these errors have been identified, the student will be able to model the calibration method. In order to achieve this goal, the student will use a high precision rate table (i.e. the ACUTRONIC model AC1120S) with its associated temperature controlled chamber.
As a first step, the Master’s student will first study the in-lab calibration of the deterministic errors inherent in the inertial sensors. Firstly, he will ignore the temperature effect and only study the calibration procedure for a fixed controlled temperature. While this first version of the algorithm will be tested and validated by an undergraduate student, the Master’s student will conduct an intensive study of the effect of temperature variations on the inertial measurements. This study will allow him to develop a robust temperature dependent calibration model. Finally, the student will conduct an intensive series of tests in order to validate this advanced calibration model and to evaluate its accuracy and reproducibility.
The second step of this research project is to focus on the modeling and implementation of an online calibration algorithm for stochastic error estimation and correction. During this phase, the Master’s student will mainly study linear estimation techniques but will also investigate non-linear approaches. Given the complexity of this research topic, the Master’s student will work closely with the Ph.D. student during this phase of the project. Testing and validation of this online calibration algorithm will first be made using the simulation tools developed by the research team. Subsequently, the calibration algorithm will be integrated into the various navigation systems in order to quantify its contribution to the accuracy and robustness of the system.