# Visual Attention for Behavioral Cloning in Autonomous Driving

arXiv: https://arxiv.org/abs/1812.01802 · HTML: https://mlsanity.com/paper/1812.01802/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2018.

## Built on by (most cited first, 1 of 1)

- [Revealing Human Attention Patterns from Gameplay Analysis for Reinforcement Learning](https://mlsanity.com/paper/2504.11118/) (2025)

## Abstract (arXiv)

The goal of our work is to use visual attention to enhance autonomous driving performance. We present two methods of predicting visual attention maps. The first method is a supervised learning approach in which we collect eye-gaze data for the task of driving and use this to train a model for predicting the attention map. The second method is a novel unsupervised approach where we train a model to learn to predict attention as it learns to drive a car. Finally, we present a comparative study of our results and show that the supervised approach for predicting attention when incorporated performs better than other approaches.