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.
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) 1-13 Training on Orchid platform and associated tools
3) 1-14 Technical study on vehicles’ embedded sensors and data networks
4) 1-34 Initial system architecture selection
5) 1-35 Study of sensor interconnection and data fusion
6) 1-61 Realization of the integrated navigation simulation tools
7) 1-62 Mathematical modeling of the linearized navigation model
8) 1-63 Simulation of the preliminary navigation system
9) 1-64 Tests and validation of the simulated navigation system
10) 2-31 Test scenarios setup and planning
11) 2-32 Tests and validation of the linearized navigation models
12) 2-61 Study of automotive vehicle constraints
13) 2-62 Modeling and integration of vehicle constraints
14) 2-63 Implementation and validation of vehicle constraints
The general objective of the Master’s research project is to develop a robust multi-sensor integrated navigation system for automotive application based on the use of very low-cost sensors. This student’s work will involve using classical integration methods based on the use of linearized error models and extended Kalman filter rather than more advanced estimation models. Instead, this student will focus on the multi-sensor data fusion aspect of the project as well as the use of non-holonomic constraints and zero velocity updates (ZUPT). The student will begin its work by analyzing a classic GPS/INS integration model that has been developed by the LACIME laboratory in previous work. From this model, he will include measurements from the different autonomous sensors used in this project (e.g. odometer and magnetometers). The implementation of this algorithm will first be carried out using Matlab / Simulink with simulated data in a fully controlled environment. Subsequently, the masters degree student will implement the algorithm within the Orchid VTADS prototype for intensive testing in real environments. Once the algorithms have been validated, the student will study the integration of non-holonomic constraints and ZUPT on the actual system in order to evaluate their impact on the overall accuracy and robustness of the navigation solution. Finally, an intensive series of tests will be performed in realistic automotive scenarios in order to assess and compare the performances of each system that are: 1) the original Orchid platform, 2) the classical GPS / INS algorithm, 3) the multi-sensor integrated navigation system including magnetometers and odometer measurements and 5) the use of non-holonomic constraints and ZUPT.
