# Periphery-Fovea Multi-Resolution Driving Model guided by Human Attention

arXiv: https://arxiv.org/abs/1903.09950 · HTML: https://mlsanity.com/paper/1903.09950/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2019.

## Built on by (most cited first, 19 of 19)

- [DRIVE: Deep Reinforced Accident Anticipation with Visual Explanation](https://mlsanity.com/paper/2107.10189/) (2021)
- [MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/1912.07773/) (2019)
- [Human Attention in Fine-grained Classification](https://mlsanity.com/paper/2111.01628/) (2021)
- [Recent Advances in Leveraging Human Guidance for Sequential Decision-Making Tasks](https://mlsanity.com/paper/2107.05825/) (2021)
- [RiskBench: A Scenario-based Benchmark for Risk Identification](https://mlsanity.com/paper/2312.01659/) (2023)
- [Beyond Average: Individualized Visual Scanpath Prediction](https://mlsanity.com/paper/2404.12235/) (2024)
- [DROID: Driver-centric Risk Object Identification](https://mlsanity.com/paper/2106.13201/) (2021)
- [Development and testing of an image transformer for explainable autonomous driving systems](https://mlsanity.com/paper/2110.05559/) (2021)
- [FALIP: Visual Prompt as Foveal Attention Boosts CLIP Zero-Shot Performance](https://mlsanity.com/paper/2407.05578/) (2024)
- [M2DA: Multi-Modal Fusion Transformer Incorporating Driver Attention for Autonomous Driving](https://mlsanity.com/paper/2403.12552/) (2024)
- [Efficiently Guiding Imitation Learning Agents with Human Gaze](https://mlsanity.com/paper/2002.12500/) (2020)
- [Gaze-Guided Graph Neural Network for Action Anticipation Conditioned on Intention](https://mlsanity.com/paper/2404.07347/) (2024)
- [Imitation Learning with Human Eye Gaze via Multi-Objective Prediction](https://mlsanity.com/paper/2102.13008/) (2021)
- [Potential Field as Scene Affordance for Behavior Change-Based Visual Risk Object Identification](https://mlsanity.com/paper/2409.15846/) (2024)
- [CUEING: a lightweight model to Capture hUman attEntion In driviNG](https://mlsanity.com/paper/2305.15710/) (2023)
- [Guiding Attention in End-to-End Driving Models](https://mlsanity.com/paper/2405.00242/) (2024)
- [DriverGaze360: OmniDirectional Driver Attention with Object-Level Guidance](https://mlsanity.com/paper/2512.14266/) (2025)
- [Prompt-Guided Attention Head Selection for Focus-Oriented Image Retrieval](https://mlsanity.com/paper/2504.01348/) (2025)
- [Neurosymbolic Imitation Learning with Human Guidance: A Privileged Information Approach](https://mlsanity.com/paper/2605.07166/) (2026)

## Abstract (arXiv)

Inspired by human vision, we propose a new periphery-fovea multi-resolution driving model that predicts vehicle speed from dash camera videos. The peripheral vision module of the model processes the full video frames in low resolution. Its foveal vision module selects sub-regions and uses high-resolution input from those regions to improve its driving performance. We train the fovea selection module with supervision from driver gaze. We show that adding high-resolution input from predicted human driver gaze locations significantly improves the driving accuracy of the model. Our periphery-fovea multi-resolution model outperforms a uni-resolution periphery-only model that has the same amount of floating-point operations. More importantly, we demonstrate that our driving model achieves a significantly higher performance gain in pedestrian-involved critical situations than in other non-critical situations.