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, the first Ph.D. student will be in charge of the following tasks:
1) 2-21 Review of the actual analysis metrics
2) 2-22 Modeling of the preliminary analysis metrics
3) 2-23 Metrics implementation using simulation software
4) 2-24 Metrics validation using simulation tools
5) 3-23 A-GNSS integration with navigation algorithms / analysis metrics
6) 3-32 Monitoring data integration with navigation algorithms / analysis metrics
7) 3-41 Implementation of the driving behaviour analysis metrics
8) 3-42 Real car test setups and planning
9) 3-43 Driving behaviour test realisation
10) 3-44 Results analysis/study on possible metrics improvement
11) 3-45 Modeling and testing of improved analysis metrics
12) 3-71 Review of the actual analysis metrics
13) 3-72 Modeling of the preliminary analysis metrics
14) 3-73 Metrics implementation using simulation software
15) 3-74 Metrics validation using simulation tools
16) 3-81 Implementation of the accident analysis metrics
17) 3-82 Real car test setups and planning
18) 3-83 Real car accident test realisation
19) 3-84 Results analysis/study on possible metrics improvement
20) 3-85 Modeling and testing of improved analysis metrics
21) 4-21 Modeling of improved analysis metrics
22) 4-22 Metric validation in controlled environment
23) 4-23 Final driving behaviour test realisation
24) 4-24 Final real car accident tests realisation
25) 4-25 Analysis metrics validation and performance analysis
26) 4-51 Test setup and planning for vehicle fleet testing
27) 4-52 System implementation on different vehicles
28) 4-53 Intensive testing of the fleet management system
29) 4-54 Results analysis and management system improvement
30) 4-55 Implementation of the improved system on vehicle fleet
31) 4-56 Validation of the fleet management system
The objective of this student’s project is to develop innovative metrics for real-time monitoring of driving behaviour and real-time analysis and diagnosis of car accidents. These metrics will be based on measurements from various sensors strategically placed on the vehicle’s frame and results will be carried out using advanced signal processing techniques. He will closely collaborate with the other members of the research team and he will use a part of the research done by the Master’s and trainee students in order to fulfill his project’s goals.
The main goals of the first Ph.D. student’s work are the following:
During the second year, the PhD student will focus on the study of the dynamics of automotive vehicles during a road accident. He will first consider major types of accidents including, but not limited to, frontal impact, side impact, turnovers and skidding. This work will first involve an in-depth literature review on the state of the art techniques currently used in the accident analysis domain. Following this study, the Ph.D. student will evaluate possible methods for the simulation of road accidents in order to obtain realistic measurements on the dynamics of a car during various accident scenarios. Once the dynamics of accidents studied, the Ph.D. student will conduct a comprehensive analysis of the raw measurements in order to determine appropriate sensors as well as their location on the vehicle’s frame. During the first year, the Ph.D. student will also model preliminary metrics solely based on simulated measurements. These metrics will first be implemented, analyzed and validated in Matlab / Simulink.
The third year will be mainly dedicated to the real-time implementation and integration of the modeled metrics into the Orchid VTADS prototype. During this phase, the metrics will be intensively tested in order to quantify their performances, accuracy and robustness in various simulated as well as realistic environments. Indeed, real tests will be conducted during this year including dangerous driving behaviour tests executed by a professional driver on a closed circuit as well as car crash testing realized by a specialized company. Following these tests, the Ph.D. student will analyze the results of the preliminary metrics in order to identify its major weaknesses and suggest possible improvements.
In response to the third year, the last year will be mainly dedicated to the identification of major flaws in the current analysis metrics and its improvement. During this phase, the Ph.D. candidate will return to research in order to explore new approaches for improving these metrics using data recorded during the real environement tests. The student will then model new innovative metrics that are more adapted to realistic environments. He will first validate these metrics in simulation using Matlab / Simulink and then test it on the real data using the previously recorded measurements. Finaly, a second phase of real tests will be conducted in order to validate the real-time analysis and diagnosis metrics.