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Driving Scene understanding is a key ingredient for intelligenttransportation systems. To achieve systems that can operate in a complexphysical and social environment, they need to understand and learn how humansdrive and interact with traffic scenes. We present the Honda Research InstituteDriving Dataset (HDD), a challenging dataset to enable research on learningdriver behavior in real-life environments. The dataset includes 104 hours ofreal human driving in the San Francisco Bay Area collected using aninstrumented vehicle equipped with different sensors. We provide a detailedanalysis of HDD with a comparison to other driving datasets. A novel annotationmethodology is introduced to enable research on driver behavior understandingfrom untrimmed data sequences. As the first step, baseline algorithms fordriver behavior detection are trained and tested to demonstrate the feasibilityof the proposed task.

Toward Driving Scene Understanding: A Dataset for Learning Driver Behavior and Causal Reasoning