Home > Leadership Development Seminars > 1. Leadership seminar > Driver Modeling for Autonomous Driving (A new application for behavior signal processing) and University Startups

Driver Modeling for Autonomous Driving (A new application for behavior signal processing) and University Startups

This talk reviews data-centric approaches for statistical modeling of driver behavior. Modeling driver behavior is challenging due to its stochastic nature and the high degree of inter- and intra-driver variability. One way to deal with the highly variable nature of driving behavior is to employ a data-centric approach that models driver behavior using large amounts of driving data collected from numerous drivers in a variety of traffic conditions. To obtain large amounts of realistic driving data, several projects have collected real-world driving data. Statistical machine-learning techniques, such as hidden Markov models (HMMs) and deep learning, have been successfully applied to model driver behavior using large amounts of driving data. We have also collected on-road data recording hundreds of drivers over more than 15 years. We have applied statistical signal processing and machine-learning techniques to this data to model various aspects of driver behavior, e.g., driver pedal-operation, car-following, and lane-change behaviors for predicting driver behavior and detecting risky driver behavior and driver frustration. By reviewing related studies and providing concrete examples of our own research, this talk is intended to illustrate the usefulness of such data-centric approaches for statistical driver-behavior modeling. In addition, technical activities of university startups from RWDC (Real-World Data Circulation leaders) program will be introduced.

Bio
Prof. Kazuya Takeda is working in the field of signal processing technology research for acoustic, speech and vehicular applications. In particular, understanding human behavior through data centric approaches utilizing signal corpus in real world has been his main interest. Prof. Takeda is a professor at the Nagoya University, Japan. He received his B.E.E., M.E.E. and Ph.D in 1983, 1985 and 1995, respectively from Nagoya University. After graduating from the university, he worked for ATR and KDD R&D Lab. He visited MIT as a visiting scientist before joined in Nagoya University in 1995. Currently, he is a BoG (Board of Governors) member of IEEE ITS Society and vice president of Acoustical Society Japan. He is also working as a director of a university startup company Tier IV.


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