# FBLNet: FeedBack Loop Network for Driver Attention Prediction

arXiv: https://arxiv.org/abs/2212.02096 · HTML: https://mlsanity.com/paper/2212.02096/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 44 references that are in the map.
- First posted on arXiv 5 Dec 2022.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection](https://mlsanity.com/paper/2005.09007/) (2020)
- [Multi-scale Interactive Network for Salient Object Detection](https://mlsanity.com/paper/2007.09062/) (2020)
- [DS-TransUNet:Dual Swin Transformer U-Net for Medical Image Segmentation](https://mlsanity.com/paper/2106.06716/) (2021)
- [Predicting the Driver's Focus of Attention: the DR(eye)VE Project](https://mlsanity.com/paper/1705.03854/) (2017)
- [DADA: Driver Attention Prediction in Driving Accident Scenarios](https://mlsanity.com/paper/1912.12148/) (2019)
- [Pyramid Grafting Network for One-Stage High Resolution Saliency Detection](https://mlsanity.com/paper/2204.05041/) (2022)
- [Predicting Driver Attention in Critical Situations](https://mlsanity.com/paper/1711.06406/) (2017)
- [DRIVE: Deep Reinforced Accident Anticipation with Visual Explanation](https://mlsanity.com/paper/2107.10189/) (2021)
- [A Dynamic Spatial-temporal Attention Network for Early Anticipation of Traffic Accidents](https://mlsanity.com/paper/2106.10197/) (2021)
- [MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/1912.07773/) (2019)
- [Tidying Deep Saliency Prediction Architectures](https://mlsanity.com/paper/2003.04942/) (2020)
- [Utilising Visual Attention Cues for Vehicle Detection and Tracking](https://mlsanity.com/paper/2008.00106/) (2020)

## Built on by (most cited first, 12 of 12)

- [Where, What, Why: Towards Explainable Driver Attention Prediction](https://mlsanity.com/paper/2506.23088/) (2025)
- [M2DA: Multi-Modal Fusion Transformer Incorporating Driver Attention for Autonomous Driving](https://mlsanity.com/paper/2403.12552/) (2024)
- [SalM2: An Extremely Lightweight Saliency Mamba Model for Real-Time Cognitive Awareness of Driver Attention](https://mlsanity.com/paper/2502.16214/) (2025)
- [When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network](https://mlsanity.com/paper/2506.17457/) (2025)
- [Causal-Entity Reflected Egocentric Traffic Accident Video Synthesis](https://mlsanity.com/paper/2506.23263/) (2025)
- [DriverGaze360: OmniDirectional Driver Attention with Object-Level Guidance](https://mlsanity.com/paper/2512.14266/) (2025)
- [From Scene to Object: Text-Guided Dual-Gaze Prediction](https://mlsanity.com/paper/2604.20191/) (2026)
- [AHMF: Adaptive Hybrid-Memory-Fusion Model for Driver Attention Prediction](https://mlsanity.com/paper/2407.17442/) (2024)
- [On-Road Object Importance Estimation: A New Dataset and A Model with Multi-Fold Top-Down Guidance](https://mlsanity.com/paper/2411.17152/) (2024)
- [Towards Robust Unsupervised Attention Prediction in Autonomous Driving](https://mlsanity.com/paper/2501.15045/) (2025)
- [Toward Safety-First Human-Like Decision Making for Autonomous Vehicles in Time-Varying Traffic Flow](https://mlsanity.com/paper/2506.14502/) (2025)
- [DiffAttn: Diffusion-Based Drivers' Visual Attention Prediction with LLM-Enhanced Semantic Reasoning](https://mlsanity.com/paper/2603.28251/) (2026)

## Abstract (arXiv)

The problem of predicting driver attention from the driving perspective is gaining increasing research focus due to its remarkable significance for autonomous driving and assisted driving systems. The driving experience is extremely important for safe driving,a skilled driver is able to effortlessly predict oncoming danger (before it becomes salient) based on the driving experience and quickly pay attention to the corresponding zones. However, the nonobjective driving experience is difficult to model, so a mechanism simulating the driver experience accumulation procedure is absent in existing methods, and the current methods usually follow the technique line of saliency prediction methods to predict driver attention. In this paper, we propose a FeedBack Loop Network (FBLNet), which attempts to model the driving experience accumulation procedure. By over-and-over iterations, FBLNet generates the incremental knowledge that carries rich historically-accumulative and long-term temporal information. The incremental knowledge in our model is like the driving experience of humans. Under the guidance of the incremental knowledge, our model fuses the CNN feature and Transformer feature that are extracted from the input image to predict driver attention. Our model exhibits a solid advantage over existing methods, achieving an outstanding performance improvement on two driver attention benchmark datasets.