# Visual Attention for Behavioral Cloning in Autonomous Driving

## TL;DR

This paper explores the use of visual attention mechanisms, both supervised and unsupervised, to improve autonomous driving by predicting attention maps, demonstrating that supervised methods yield better performance.

## Contribution

It introduces a novel unsupervised approach for predicting attention maps in autonomous driving and compares it with supervised methods, highlighting the effectiveness of supervised attention prediction.

## Key findings

- Supervised attention prediction outperforms unsupervised methods.
- Incorporating attention maps improves driving performance.
- The unsupervised approach learns attention without gaze data.

## Abstract

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.

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Source: https://tomesphere.com/paper/1812.01802