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Smart Traffic Control for the Era of Autonomous Driving

Over the last decade, the research on autonomous vehicles (AVs) has made revolutionary progress, which brings us hope of safer, more convenient, and more efficient means of transportation. Most significantly, the advance of artificial intelligence (AI), especially machine learning, allows a self-driving car to learn and adapt to complex road situations with millions of accumulated driving hours, which are way higher than any experienced human driver can reach. However, autonomous vehicles on roads also introduce new challenges to traffic management, especially when we allow them to travel mixed with human driving vehicles.

New theories for better understanding of the new era of transportation and new technologies for smart roadside infrastructures and intelligent traffic control are crucial for development and deployment of autonomous vehicles. This presentation will discuss some of these challenges, especially the social aspects of autonomous driving, including interaction between autonomous vehicles and roadside infrastructures, mechanisms of traffic management, the price of anarchy in road networks and automated negotiation between vehicles.

Dongmo Zhang is an Associate Professor in Computer Science and Associate Dean Graduate Studies in School of Computer, Data and Mathematical Sciences at Western Sydney University. He is a leading researcher in Artificial Intelligence, working in a wide range of areas, including multi-agent systems, strategic reasoning, automated negotiation, belief revision, reasoning about action, auctions, trading agent design etc. He has published around 150 papers in international journals and conferences, including the top AI Journals, such as AIJ, AAMAS & JAIR, and the top AI conferences, such as IJCAI, AAAI & AAMAS.  He has been an area chair, senior PC or PC for many top AI conferences, IJCAI, AAAI, ECAI, PRICAI, AJCAI, AAMAS, KR&R etc. He and his research team have also received several international awards, such champions of Trading Agent Competitions and best paper awards.