# See, Attend and Brake: An Attention-based Saliency Map Prediction Model for End-to-End Driving

arXiv: https://arxiv.org/abs/2002.11020 · HTML: https://mlsanity.com/paper/2002.11020/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2020.

## Built on by (most cited first, 3 of 3)

- [CausalAgents: A Robustness Benchmark for Motion Forecasting using Causal Relationships](https://mlsanity.com/paper/2207.03586/) (2022)
- [Important Object Identification with Semi-Supervised Learning for Autonomous Driving](https://mlsanity.com/paper/2203.02634/) (2022)
- [Where and What: Driver Attention-based Object Detection](https://mlsanity.com/paper/2204.12150/) (2022)

## Abstract (arXiv)

Visual perception is the most critical input for driving decisions. In this study, our aim is to understand relationship between saliency and driving decisions. We present a novel attention-based saliency map prediction model for making braking decisions This approach constructs a holistic model to the driving task and can be extended for other driving decisions like steering and acceleration. The proposed model is a deep neural network model that feeds extracted features from input image to a recurrent neural network with an attention mechanism. Then predicted saliency map is used to make braking decision. We trained and evaluated using driving attention dataset BDD-A, and saliency dataset CAT2000.