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) 1-14 Technical study on vehicles’ embedded sensors and data networks
3) 1-21 Literature review on car dynamics
4) 1-22 Car dynamic simulation using simulation software
5) 1-23 Real data acquisition with multiple sensors
6) 1-24 Accident simulation using small scale vehicles
7) 1-25 Development of accident simulation tools
8) 1-61 Realization of the integrated navigation simulation tools
9) 3-51 Study on sensor error models
10) 3-52 Sensor error modeling and validation
11) 3-53 Addition of error models to simulation tools
The general objective of the Master’s research project is to develop a complete simulation platform alowing generation of realistic raw measurements for different automotive scenarios, including or not a road accident. This platform should allow reproduction of the measurements from every sensor/system used during the project including accelerometers, gyroscopes, odometer, magnetometers and GPS as well as their respective error sources. The main motivation behind the development of such a platform is to provide a source of realistic measurements to the research team without having to make real tests allowing saving time and money. This platform will be intensively used by the research team for fast modeling and validation of the developed systems. In addition, it will also be used later in a second masters project that will develop a complete virtual test bench for autonomous prototype and system validation. During the early stages of its project, the student will achieve a comprehensive study of the raw measurements of each sensor in order to clearly identify their behavior as well as their main source of error. From this analysis, the student will be able to model the realistic behaviour of each sensor related to a specific trajectory. This modeling will first be carried out using Matlab / Simulink and the results will be compared with measurements from real sensors. Subsequently, the student will study the posibility of using trajectory simulation software such as X-Plane (or other software) in order to generate realistic trajectories (i.e. position, speed, orientation) of automotive navigation. Finally, the student will adapt the developed sensors error models so that they can receive different simulated trajectories as inputs.
