Driver attention prediction is currently becoming the focus in safe driving
research community, such as the DR(eye)VE project and newly emerged Berkeley
DeepDrive Attention (BDD-A) database in critical situations. In safe driving,
an essential task is to predict the incoming accidents as early as possible.
BDD-A was aware of this problem and collected the driver attention in
laboratory because of the rarity of such scenes. Nevertheless, BDD-A focuses
the critical situations which do not encounter actual accidents, and just faces
the driver attention prediction task, without a close step for accident
prediction. In contrast to this, we explore the view of drivers' eyes for
capturing multiple kinds of accidents, and construct a more diverse and larger
video benchmark than ever before with the driver attention and the driving
accident annotation simultaneously (named as DADA-2000), which has 2000 video
clips owning about 658,476 frames on 54 kinds of accidents. 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). For the driver attention representation, we collect the maps of
fixations, saccade scan path and focusing time. The accidents are annotated by
their categories, the accident window in clips and spatial locations of the
crash-objects. Based on the analysis, we obtain a quantitative and positive
answer for the question in this paper.

本文提出了一个新的基于驾驶员目光追踪和车祸注释的视频基准测试集（DADA-2000），涵盖了 54 种不同类型的车祸，可以更全面地预测即将发生的事故。