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Workshop: Machine Learning for Autonomous Driving

Risk Perception in Driving Scenes

Nakul Agarwal · Yi-Ting Chen


The holy grail of intelligent vehicles is to enable a zero collision mobility experience. This endeavor requires an interdisciplinary effort to understand driver behavior and to assess risks surrounding the vehicle. A driver's perception of risk is a complex cognitive process that is largely manifested by the voluntary response of the driver to external stimuli as well as the apparent attentiveness of participants toward the ego-vehicle. In this work, we examine the problem of risk perception and introduce a new dataset to facilitate research in this domain. Our dataset consists of 4706 short video clips that include annotations of driver intent, road network topology, situation (e.g., crossing pedestrian), driver response, and pedestrian attentiveness using face annotations. We also provide a simple weakly supervised framework which performs favorably against state of the art methods.

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