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 trainee student will be in charge of the following tasks:
1) 3-11 Real-time development of navigation systems
2) 3-12 Real test setup and planning
3) 3-13 Test / validation of the navigation systems in various scenarios
The main objective of this training is to assist graduate students in the implementation, testing and validation of integrated navigation systems based on the use of nonlinear models (i.e. unscented Kalman filter, particle filter and neural network). During this internship, the student must first set up realistic automotive scenarios, including but not limited to highways, urban canyons, dense foliage and tunnels. Then, the student will conduct an intensive series of tests under these different scenarios in order to compare the performances of the developed systems with other systems, including the original iMetrik’s Orchid platform, the first model developed earlier in the project (i.e. based on EKF) as well as some commercial products, including a high-end system that will be used as a reference. Finally, the student will analyze the results of these tests in order to characterize the actual performances of the system, to investigate its main weaknesses and to explore possible improvements.
