# DADA: Driver Attention Prediction in Driving Accident Scenarios

arXiv: https://arxiv.org/abs/1912.12148 · HTML: https://mlsanity.com/paper/1912.12148/

## Key facts

- Cited by 70 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2019.

## Built on by (most cited first, 30 of 70)

- [Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review](https://mlsanity.com/paper/2402.10086/) (2024)
- [Abductive Ego-View Accident Video Understanding for Safe Driving Perception](https://mlsanity.com/paper/2403.00436/) (2024)
- [Vision-Based Traffic Accident Detection and Anticipation: A Survey](https://mlsanity.com/paper/2308.15985/) (2023)
- [Behavioral Intention Prediction in Driving Scenes: A Survey](https://mlsanity.com/paper/2211.00385/) (2022)
- [Towards Knowledge-driven Autonomous Driving](https://mlsanity.com/paper/2312.04316/) (2023)
- [Exploring Event-driven Dynamic Context for Accident Scene Segmentation](https://mlsanity.com/paper/2112.05006/) (2021)
- [Nexar Dashcam Collision Prediction Dataset and Challenge](https://mlsanity.com/paper/2503.03848/) (2025)
- [Unsupervised Self-Driving Attention Prediction via Uncertainty Mining and Knowledge Embedding](https://mlsanity.com/paper/2303.09706/) (2023)
- [Cognitive Accident Prediction in Driving Scenes: A Multimodality Benchmark](https://mlsanity.com/paper/2212.09381/) (2022)
- [Real-time Accident Anticipation for Autonomous Driving Through Monocular Depth-Enhanced 3D Modeling](https://mlsanity.com/paper/2409.01256/) (2024)
- [FBLNet: FeedBack Loop Network for Driver Attention Prediction](https://mlsanity.com/paper/2212.02096/) (2022)
- [Where, What, Why: Towards Explainable Driver Attention Prediction](https://mlsanity.com/paper/2506.23088/) (2025)
- [UniUGP: Unifying Understanding, Generation, and Planing For End-to-end Autonomous Driving](https://mlsanity.com/paper/2512.09864/) (2025)
- [DROID: Driver-centric Risk Object Identification](https://mlsanity.com/paper/2106.13201/) (2021)
- [TinyHD: Efficient Video Saliency Prediction with Heterogeneous Decoders using Hierarchical Maps Distillation](https://mlsanity.com/paper/2301.04619/) (2023)
- [Text-Driven Traffic Anomaly Detection with Temporal High-Frequency Modeling in Driving Videos](https://mlsanity.com/paper/2401.03522/) (2024)
- [INS-MMBench: A Comprehensive Benchmark for Evaluating LVLMs' Performance in Insurance](https://mlsanity.com/paper/2406.09105/) (2024)
- [SafePLUG: Empowering Multimodal LLMs with Pixel-Level Insight and Temporal Grounding for Traffic Accident Understanding](https://mlsanity.com/paper/2508.06763/) (2025)
- [SCOUT+: Towards Practical Task-Driven Drivers' Gaze Prediction](https://mlsanity.com/paper/2404.08756/) (2024)
- [Vulnerable Road User Detection and Safety Enhancement: A Comprehensive Survey](https://mlsanity.com/paper/2405.19202/) (2024)
- [Ctrl-Crash: Controllable Diffusion for Realistic Car Crashes](https://mlsanity.com/paper/2506.00227/) (2025)
- [Domain-Enhanced Dual-Branch Model for Efficient and Interpretable Accident Anticipation](https://mlsanity.com/paper/2507.12755/) (2025)
- [Is my Driver Observation Model Overconfident? Input-guided Calibration Networks for Reliable and Interpretable Confidence Estimates](https://mlsanity.com/paper/2204.04674/) (2022)
- [Temporal DINO: A Self-supervised Video Strategy to Enhance Action Prediction](https://mlsanity.com/paper/2308.04589/) (2023)
- [Exploring the Potential of Multi-Modal AI for Driving Hazard Prediction](https://mlsanity.com/paper/2310.04671/) (2023)
- [Understanding and Modeling the Effects of Task and Context on Drivers' Gaze Allocation](https://mlsanity.com/paper/2310.09275/) (2023)
- [SalM2: An Extremely Lightweight Saliency Mamba Model for Real-Time Cognitive Awareness of Driver Attention](https://mlsanity.com/paper/2502.16214/) (2025)
- [BADAS: Context Aware Collision Prediction Using Real-World Dashcam Data](https://mlsanity.com/paper/2510.14876/) (2025)
- [Accident Anticipation via Temporal Occurrence Prediction](https://mlsanity.com/paper/2510.22260/) (2025)
- [Data Limitations for Modeling Top-Down Effects on Drivers' Attention](https://mlsanity.com/paper/2404.08749/) (2024)

## Abstract (arXiv)

Driver attention prediction is becoming an essential research problem in human-like driving systems. This work makes an attempt to predict the driver attention in driving accident scenarios (DADA). However, challenges tread on the heels of that because of the dynamic traffic scene, intricate and imbalanced accident categories. In this work, we design a semantic context induced attentive fusion network (SCAFNet). We first segment the RGB video frames into the images with different semantic regions (i.e., semantic images), where each region denotes one kind of semantic categories of the scene (e.g., road, trees, etc.), and learn the spatio-temporal features of RGB frames and semantic images in two parallel paths simultaneously. Then, the learned features are fused by an attentive fusion network to find the semantic-induced scene variation in driver attention prediction. The contributions are three folds. 1) With the semantic images, we introduce their semantic context features and verified the manifest promotion effect for helping the driver attention prediction, where the semantic context features are modeled by a graph convolution network (GCN) on semantic images; 2) We fuse the semantic context features of semantic images and the features of RGB frames in an attentive strategy, and the fused details are transferred over frames by a convolutional LSTM module to obtain the attention map of each video frame with the consideration of historical scene variation in driving situations; 3) The superiority of the proposed method is evaluated on our previously collected dataset (named as DADA-2000) and two other challenging datasets with state-of-the-art methods. DADA-2000 is available at https://github.com/JWFangit/LOTVS-DADA.