Keynote Lecture
The Use of Contextual Reasoning for Road Users’ Behaviour Prediction in the Framework of Automated Driving Technologies
Miguel A. Sotelo
Universidad de Alcalá
Spain
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Brief Bio
Miguel Ángel Sotelo received the degree in Electrical Engineering in 1996 from the Technical University of Madrid, the Ph.D. degree in Electrical Engineering in 2001 from the University of Alcalá, and the Master in Business Administration from the European Business School in 2008. He is currently a Full Professor at the Department of Computer Engineering of the University of Alcalá, where he was the Vice-President for International Relations (2014-2019) and General Manager (2019-2022). In 1997, he was a Research Visitor at the RSISE of the Australian National University in Canberra. His research interests include Self-driving and Interacting cars, as well as Predictive and Cooperative Systems. He is author of more than 300 publications in international journals, conferences, and book chapters, being in the top 1% of researchers in the field of Logistics and Transport, according to the ranking elaborated by the University of Stanford. Miguel Ángel Sotelo has served as Project Evaluator, Rapporteur, and Reviewer for the European Commission in the field of ICT for Intelligent Vehicles and Cooperative Systems in FP6 and FP7. He was Director General of Guadalab Science & Technology Park (2011-2012) and co-founder and CEO of Vision Safety Technologies (2009-2015). Miguel Ángel Sotelo served as President of the IEEE ITS Society (2018-2019), Editor-in-Chief of the IEEE Intelligent Transportation Systems Magazine (2014-2016), and General Chair of the 2012 IEEE Intelligent Vehicles Symposium (IV’2012). He has delivered invited keynotes in conferences and seminars in ~30 different countries in the five continents and has participated as member of PhD Juries in different universities across Germany, Austria, France, Sweden, Portugal, Spain, The Netherlands, and Australia. He has been recipient of the Best Research Award in the domain of Automotive and Vehicle Applications in Spain in 2002 and 2009, the 3M Foundation Awards in the category of eSafety in 2004 and 2009, the ITSS Outstanding Editorial Service Award in 2010, the IEEE ITS Outstanding Application Award in 2013, the Prize to the Best Team with Full Automation in GCDC 2016, and the IEEE ITS Outstanding Research Award in 2022. He is a Fellow of the IEEE, and a Fellow of the AAIA (Asia-Pacific Artificial Intelligence Association).
Abstract
Automated Vehicles (AVs) have experienced a booming development in the latest years, having achieved a large degree of maturity. Their scene recognition capabilities have improved in an impressive manner, especially thanks to the development of Deep Learning techniques and the availability of immense amounts of data contained in well-organized public datasets. But still, AVs exhibit limited ability to deal with certain types of situations that become natural to human drivers, such as entering a congested round-about, predicting the presence of occluded pedestrians at cross-walks, dealing with cyclists, or giving way to a vehicle that is aggressively merging onto the highway from a ramp lane. Not to mention their limitations to interpret implicit communication -when interacting with other road users- and to anticipate dynamic scenarios, especially those dealing with near-crash situations. All these tasks require the development of advanced capabilities that rely on contextual reasoning in order to: a) anticipate the most likely behaviours of all traffic agents around the ego-car; and b) adapt the AV’s own actions to the anticipated road users’ behaviours in a socially-accepted manner. These features will open the gate to expanding the operational design domain of AVs and to contribute to their societal acceptance. In this talk, latest results achieved in the framework of contextual reasoning for behaviour understanding and prediction will be presented and discussed, with a special focus on the prediction of occluded pedestrians and the anticipation of near-crash scenarios.