Speaker: Ekim Yurtsever, Dept. of ECE
Time: Thurs 01/16/2020, 4pm-5pm
Location: Dreese Lab 480
Title: Holistic Risk Perception for Automated Driving using Spatiotemporal Networks
Abstract: Recently, increased public interest and market potential precipitated the emergence of self-driving platforms with varying degrees of automation. However, robust, fully automated driving in urban scenes is not achieved yet.
In this talk, I will introduce a new concept called ‘Holistic Risk Perception’ to alleviate the shortcomings of conventional risk assessment approaches. Holistic risk perception can be summarized as quantifying uncertainties and inferring the risk level of the driving scene.
I will then present a supervised deep learning framework to realize this goal. The proposed method applies semantic segmentation to individual video frames with a pre-trained model. Then, frames overlayed with these masks are fed into a time distributed CNN-LSTM network with a final softmax classification layer. This network was trained on a semi-naturalistic driving dataset with annotated risk labels. A comprehensive comparison of state-of-the-art pre-trained feature extractors was carried out to find the best network layout and training strategy. The best result, with a 0.937 AUC score, was obtained with the proposed framework. The code and trained models are available open-source.
Bio: Ekim Yurtsever received his Ph.D. in Information Science from Nagoya University in 2019 and is currently a postdoctoral researcher at the Department of Electrical and Computer Engineering, Ohio State University. His research interests include artificial intelligence, machine learning, and computer vision. Currently, he is working on machine learning and computer vision tasks in the intelligent vehicle domain.