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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.

Traffic accident detection and anticipation is an obstinate road safety problem and painstaking efforts have been devoted. With the rapid growth of video data, Vision-based Traffic Accident Detection and Anticipation (named Vision-TAD and Vision-TAA) become the last one-mile problem for safe driving and surveillance safety. However, the long-tailed, unbalanced, highly dynamic, complex, and uncertain properties of traffic accidents form the Out-of-Distribution (OOD) feature for Vision-TAD and Vision-TAA. Current AI development may focus on these OOD but important problems. What has been done for Vision-TAD and Vision-TAA? What direction we should focus on in the future for this problem? A comprehensive survey is important. We present the first survey on Vision-TAD in the deep learning era and the first-ever survey for Vision-TAA. The pros and cons of each research prototype are discussed in detail during the investigation. In addition, we also provide a critical review of 31 publicly available benchmarks and related evaluation metrics. Through this survey, we want to spawn new insights and open possible trends for Vision-TAD and Vision-TAA tasks.

Driver attention prediction has recently absorbed increasing attention in traffic scene understanding and is prone to be an essential problem in vision-centered and humanlike driving systems. This work, different from other attempts, makes an attempt to predict the driver attention in accidental scenarios containing normal, critical and accidental situations simultaneously. However, challenges tread on the heels of that because of the dynamic traffic scene, intricate and imbalanced accident categories. With the hypothesis that driver attention can provide a selective role of crash-object 1 for assisting driving accident detection or prediction, this paper designs a multi-path semantic-guided attentive fusion network (MSAFNet) that learns the spatio-temporal semantic and scene variation in prediction. For fulfilling this, a large-scale benchmark with 2000 video sequences (named as DADA-2000) is contributed with laborious annotation for driver attention (fixation, saccade, focusing time), accident objects/intervals, as well as the accident categories, and superior performance to state-of-the-arts are provided by thorough evaluations. As far as we know, this is the first comprehensive and quantitative study for the human-eye sensing exploration in accidental scenarios. DADA-2000 is available at https://github.com/JWFangit/LOTVS-DADA.

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