Invited Talk: Motion Prediction for Vulnerable Road Users (Dariu Gavrila)

Video: https://youtu.be/qWKfhrhDtlU

Abstract: Sensors are meanwhile very good at measuring 3D in the context of environment perception for self- driving vehicles. Scene labeling and object detection have also made big strides, mainly due to advances in deep learning. Time has now come to focus on the next frontier: modeling and anticipating the motion of road users. The potential benefits are large, such as earlier and more effective system reactions in dangerous traffic situations. To reap these benefits, however, it is necessary to use sophisticated predictive motion models based on intent-relevant (context) cues. In this talk, I give an overview of predictive motion models and intent-relevant cues with respect to the vulnerable road users (i.e. pedestrians, cyclists). In particular, I discuss the pros and cons of having these models handcrafted by an expert compared to learning them from data. I present results from a recent case study on cyclist path prediction involving a Dynamic Bayesian Network and a Recurrent Neural Network.

Bio: Dariu M. Gavrila received the PhD degree in computer science from the University of Maryland at College Park, USA, in 1996. During 1997 - 2016, he was with Daimler R&D in Ulm, Germany, where he became a Distinguished Scientist. During 2003 - 2018, he was also professor at the University of Amsterdam, chairing the area of Intelligent Perception Systems (part time). Since 2016 he is head of the Intelligent Vehicles group at TU Delft, full time (www.intelligent-vehicles.org). Over the past 20 years, Prof. Gavrila has focused on visual systems for detecting humans and their activity, with application to intelligent vehicles, smart surveillance and social robotics. He led the multi-year pedestrian detection research effort at Daimler R&D, which was commercialized in the Mercedes-Benz S-, E-, and C-Class models (2013-2014). He now performs research on self-driving cars in complex urban environment and focusses on the anticipation of pedestrian and cyclist behavior. Prof. Gavrila is frequently cited in the scientific literature (Google Scholar: 13.000+ times) and he received the I/O 2007 Award from the Netherlands Organisation for Scientific Research (NWO) and the IEEE Intelligent Transportation Systems Application Award 2014 (as part of a Daimler team). He served as Area and Program Co-Chair at several conferences (IV, ICCV, ECCV, AVSS). His research was covered in various print and broadcast media, such as Wired Magazine, Der Spiegel, BBC Radio, 3Sat Nano and NOS.