# Emergency-braking Distance Prediction using Deep Learning

arXiv: https://arxiv.org/abs/2112.01708 · HTML: https://mlsanity.com/paper/2112.01708/

## Key facts

- Cited by 1 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 3 Dec 2021.

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

- [Dual-AEB: Synergizing Rule-Based and Multimodal Large Language Models for Effective Emergency Braking](https://mlsanity.com/paper/2410.08616/) (2024)

## Abstract (arXiv)

Predicting emergency-braking distance is important for the collision avoidance related features, which are the most essential and popular safety features for vehicles. In this study, we first gathered a large data set including a three-dimensional acceleration data and the corresponding emergency-braking distance. Using this data set, we propose a deep-learning model to predict emergency-braking distance, which only requires 0.25 seconds three-dimensional vehicle acceleration data before the break as input. We consider two road surfaces, our deep learning approach is robust to both road surfaces and have accuracy within 3 feet.