Proactive road safety analysis allows for the pre-emptive diagnosis of road safety issues without direct observation of traffic accidents by observing accident precursor events instead (i.e. "traffic conflicts"). This approach to road safety diagnosis is made possible with the collection and analysis of large quantities of high-resolution road user trajectory data acquired from video data automatically.
The shipping container is one of the most important assets of international shipping and global trade. Built to withstand extreme conditions, the quality of these large metallic boxes is often overestimated resulting in the international container fleet being perpetually undermaintained. As trade volumes increase terminal inspectors lave less time to conduct container quality inspections. This project aims to create an automated shipping container inspection system using high definition cameras and machine learning software.
The proposed project aims to develop numerical models using computational fluid dynamics (CFD) to understand and predict the performance of heat pipes in the context of cooling applications. Heat pipes are a type of enhanced heat transfer device that uses a continuous cycle of boiling and condensing a fluid to transfer heat at a very high rate. The industry partner designs and manufactures heat pipes that are used extensively in the cooling of molds for making automotive parts.
The goal of this project is to design computationally-efficient solvers that can be used for autonomous vehicle control developments. Because autonomous cars have complex mathematical models, it is usually hard to perform their necessary control computations on-line and when the vehicle is running. Therefore, it is required to come up with much faster solvers for their controllers. At the end of this project, the developed control methods will be tested on an accurate simulation platform to evaluate their performance and robustness in realistic scenarios.
In this project, charging and driving data of 1000 electric vehicles (EVs) across Canada will be monitored and analyzed to figure out the impact of EVs on the electrical power grid, and their potential capability to reduce CO2 emissions. For this purpose, the degree to which a particular electricity grid profile, the vehicle type and driving style, and charging patterns impact CO2 emissions will be studied.
In a context of global warming, it is essential to find green alternatives to public transportation. The National Smart Vehicle Demonstration Project aims to improve mobility options for Canadians by advancing the implementation of low-speed electrified autonomous shuttles (LSAs). This project aims to support job growth in the design of technologically advanced electrification, sensing, communication and cybersecurity tools that support LSAs.
Transportation that uses green energy is environmentally friendly and helps to reduce greenhouse gas emission. But there is a tension between the stakeholders, policy makers and public on their economic return, policy implementation and perception on innovation in technology in transit respectively.
The project deploys and try out a coordination model and platform that helps developers to build smart city applications that run in large scale, dynamic fog computing infrastructure. Fog computing is a computing infrastructure that involves devices across the edge network such as smart phones, smart cars, the access network such as Wi-Fi routers, modems and the cloud servers.
Carsharing is a service where members have access to a fleet of shared vehicles distributed across a city. Members can book a vehicle when needed, allowing for the convenience of vehicle ownership while reducing the need to own private vehicles. The two primary forms of carsharing are a free-floating or free floating model, where users can pick up and drop off vehicles anywhere inside a service area, and a round-trip or round trip model, where members pick up the vehicle at a specific location and later return it to that starting location.
In Montreal, pavement distresses are causing serious problem to the road network with more than half of the road considered in a bad and a very bad shape. Many pavement inspection methods are developed in order to inspect, detect, locate, and classify pavement distresses; however, these methods are not efficient in term of time, cost, and accuracy. In our project, we aim to develop a new approach in detecting, classifying, and locating pavement distresses using conventional unmanned autonomous vehicle LiDAR.