To reduce the weidht of cars in order to reduce greenhouse gas emissions, the automotive industry has recently been turning to the extensive use of composite materials for structural applications. Magna Exteriors Inc (MEI), a world leader tier 1 automotive supplier and a division of Magna International Inc, is seeking ways to develop a new high-volume manufacturing process for hollow parts using sheet moulding compound (SMC).
Precision agriculture has many benefits especially for the developing world. Autonomous tractors and automatic planting systems have high accuracy, resulting in a substantially improved return on investment for growers, making food planting more economical. Moreover, the tractors can collect information on soil conditions, which can lead to improved maintenance of the crops, prevent blights, and achieve higher efficiency and higher plant food quality.
Thermal management of power electronic devices in an electric vehicle inverter is a critical factor influencing cost, size, efficiency and reliability of the system. Liquid cooling is a viable option; however, for peak power operation, the existing liquid cooling system must be optimized. A new impinging-jet-based liquid cooling system with enhanced heat transfer was designed and developed at the University of Windsor in collaboration with MAGNA International. Initial evaluations have shown that the new system has significant advantages over existing liquid cooling systems.
Automotive Open System Architecture (AUTOSAR) is a system-level standard that is used worldwide by the automotive companies and their suppliers to develop the standardized software development framework for automobiles . The basic software of AUTOSAR should be configured to develop an Electronic Control Unit (ECU). The information to configure the basic software is given in an Extensible Markup Language (XML) file. Currently, these XML files are interpreted by the developer and manually entered for each configuration.
The project will provide an opportunity to address key challenges related to user expectation of Automated and Connected Electric Shuttles. That is, the project will serve to advance the understanding of user perception and experience of smart shuttles in Canada. This technology is increasingly often being tested in pilots across Canada and it is critically to proactively understand the reaction of residents to this new technology.
This research aims to develop a novel transit signal priority (TSP) strategy under autonomous/connected vehicle environment to ease traffic congestion for transit vehicles at intersections. In this study, the accurate arrival time of transit vehicles at intersections will be estimated and the green time will be extended accordingly to help transit vehicles pass intersections. Moreover, the traffic flow of the crossing streets will be monitored constantly in order to decrease the adverse effect of TSP on traffic flow of crossing streets.
Airports in Canada have the difficult tasks of needing to remain open during adverse weather conditions in the winter. The research that we are undertaking is to develop and deploy a fully autonomous snow removal equipment for Canadian airports. Operating a snowplow is a dangerous and exhausting task for human operators, often they are unable to see clearly when they are operating in extreme conditions. Airports are also finding it increasingly more difficult to recruit and retain seasonal workers. A potential solution for these issues is a fully autonomous snow removal system.
Polymeric matrices containing nano-size additives have demonstrated remarkable mechanical, electrical, and thermal properties when compared to their micro-composite counterparts. Inserting graphene in a polymer matrix consisting of a glass fiber-reinforced resin is assumed to significantly increase the material electrical conductivity, which is needed in order to fulfill electrical conductivity requirements traditionally met by carbon black incorporation.
The public transportation system is crucial in alleviating urban congestion. The widespread of smart card automated fare collection (AFC) system produces massive data recording passengers day-to-day transport dynamic, which provides unprecedented opportunities to researchers and practitioners to understand and improve transit services. This project aims to make full use of the transit operational data (mainly smart card data) to enhance transit services. The main body of the research project is spatiotemporal behavior patterns mining.
With the recent advances in artificial intelligence, applying deep reinforcement learning to improve urban traffic efficiency and reduce traffic congestion has been gaining increasing interest in both academia and industry. This research program aims at developing computational platforms to evaluate models and algorithms for the next generation traffic control and management strategies, such as autonomous vehicles, vehicle-to-vehicle communication, and vehicle-to-infrastructure communication.