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) 1-21 Literature review on car dynamics
2) 1-22 Car dynamic simulation using simulation software
3) 1-23 Real data acquisition with multiple sensors
4) 1-24 Accident simulation using small scale vehicles
5) 1-31 Study of sensor requirements
6) 1-32 Study of sensor location on the vehicle’s frame
7) 1-34 Initial system architecture selection
The main objective of this training is to assist the research team in the preliminary analysis of the dynamics of a car accident in order to be able to select appropriate sensors for the implementation of the accident analysis and diagnosis system. This phase will also help the team choose the best location where the sensors should be placed on the vehicle’s frame for optimum system performances. To achieve this goal, the student will study three options: 1) the use of a simulation software such as X-Plane for the acquisition of realistic measures of a car accident 2) the use of reduced size vehicles in order to reproduce realistic accident scenarios 3) the use of real vehicles during special events such as a demolition derby or a car race. For either of these options, the student should first develop realistic scenarios of accidents, including, but not limited to, tests for frontal and side impacts as well as turnovers. Subsequently, the student will perform intensive data acquisition on various accident scenarios in order to determine which measurements best describe the dynamics of a car accident. Following this acquisition phase, the trainee will analyze the results in order to conclude on the type of sensor that should be used for the system as well as their required characteristics and their location on the vehicle’s frame. These results are particularly important for further work to be carried out during this research project.
