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.
@article{arxiv.2112.01708,
title = {Emergency-braking Distance Prediction using Deep Learning},
author = {Ruisi Zhang and Ashkan Pourkand},
journal= {arXiv preprint arXiv:2112.01708},
year = {2021}
}