Abstract: Autonomous vehicles are are the future. Driver assistive technologies has evolved substantially over recent years. Adaptive cruise control, blind spot monitoring or forward collision alerts that warn of accidents have made life safer and easier for drivers. It will not be long before, automated technology in cars replace humans. Self-driving vehicles will provide tremendous independence to people with disabilities, non drivers and senior citizens. As population increases, the number of vehicles will also increase. The question is what impact will autonomous vehicles have on roads? There are opposing views on how self-driving cars will affect urban cities. Some argue that driverless vehicles will not increase traffic congestion. This may or may not be true. Currently, however, traffic congestion is a major problem in large urban areas. Traffic congestion is hazardous to health. It not only increases air pollution but also increases travel time causing driver frustration, stress and in some cases road rage. Vehicular ad hoc networks (VANET) have been proposed as one of the major applications for solving the traffic congestion problem in intelligent transportation systems. VANETs are highly decentralized, distributive and have lower cellular bandwidth cost. Vehicles in VANET communicate with nearby vehicles or infrastructure elements such as road side units that are mounted in centralized locations like intersections and parking lots. VANETs can, therefore, obtain real-time data representing the interactions between vehicles and their surroundings. This data can then be used to solve problems in intelligent transportation systems that require complex data to navigate. In this talk, I will propose a decentralized artificial intelligence based traffic aware routing algorithm for vehicular ad hoc networks.
Speaker's Bio: Parimala Thulasiraman is a Professor with the Department of Computer Science at the University of Manitoba. She received her B.Eng. (Honours) and M.A.Sc. degrees in Computer Engineering from Concordia University in Montreal, QC, Canada and obtained her Ph.D. from the University of Delaware in Newark, DE, USA after finishing most of her formalities at McGill University in Montreal, QC, Canada. Parimala’s research interests are in the intersection of high performance parallel/distributing computing and graph analytics for real world applications in Network Science. Her laboratory, Inter-Disciplinary Evolving Algorithmic Sciences (IDEAS), applies innovative soft computing techniques such as evolutionary computation, bio-inspired computation, and machine learning to solve challenging issues in complex network systems and data scientific problems. She explores novel algorithmic optimization techniques for these problems to efficiently map, design, and develop scalable algorithms for futuristic multicore architectures. Parimala has supervised and graduated over 90 students. She has published over 140 papers in notable conferences, such as the IEEE International Parallel and Distributed Symposium, journals, such as the Journal of Parallel and Distributed Computing, a book and several book chapters. She has received best paper awards in leading high performance computing conferences. Her research is supported through the national grant from the Natural Sciences and Engineering Research Council of Canada as well as other local grants. Parimala has organized many important conferences as local chair, program chair, and tutorial chair. She is the editor and guest editor for major journals and has been serving as a reviewer and program committee member for many conferences in high performance computing and soft computing. She is also a reviewer for many leading journals. She is a member and senior member of the ACM and IEEE societies, respectively.
For more information, please see Parimala’s homepage: http://www.cs.umanitoba.ca/~thulasir/.