To allow the research community to investigate safety-critical driving scene understanding for autonomous driving thoroughly, this workshop introduces the DADA-2000 dataset, a novel human-gaze-aligned egocentric accident video benchmark collected in driving scenes. DADA-2000 is a more diverse and larger video benchmark dataset annotated with the driver fixations for each video frame. Each gaze map is obtained by accumulating five subjects's fixation points and utilizing the Gaussian filter with (50×50) pixel kernel.

DADA-2000 collects over 658, 476 frames of human driver gaze fixations for 2000 video clips with the resolution of 1584× 660 on 54 kinds of accident scenarios. These clips are crowd-sourced and captured in various occasions (highway, urban, rural, and tunnel), weather (sunny, rainy and snowy) and light conditions (daytime and nighttime). Leveraging the features of the MM-AU dataset, each video is temporally aligned with the text descriptions of accident reasons, prevention solutions, accident categories, and object boxes manually annotated for video frames.

We propose to organize the Critical Object Grounding Task, supported by the DADA-2000 (train) and MM-AU (Partial Test) datasets.