On-Board Estimation of Vehicle Speed and the Need of Braking Using Convolutional Neural Networks

Razvan Itu, Radu Danescu

2023

Abstract

Detecting the ego-vehicle state is a challenging problem in the context of autonomous vehicles. Perception-based methods leverage information from on-board cameras and sensors to determine the surrounding traffic scene and vehicle state. Monocular based approaches are becoming more popular for driver assistance, and accurate vehicle speed prediction plays an important role for improving road safety. This research paper presents an implementation of a Convolutional Neural Network (CNN) model for vehicle velocity prediction using sequential image input, as well as an extended model that also features sensorial data as input. The CNN model is trained on a dataset featuring sets of 20 sequential images, captured from a moving car in a road traffic scene. The aim of the model is to predict the current vehicle speed based on the information encoded in the previous 20 frames. The model architecture consists of convolutional layers followed by fully connected layers, having a linear output layer for the ego-vehicle velocity prediction. We evaluate our proposed models and compare them using existing published work that features Recurrent Neural Networks (RNNs). We also examine the prediction of the brake pedal pressure required while driving.

Paper Citation

in Harvard Style

Itu R. and Danescu R. (2023). On-Board Estimation of Vehicle Speed and the Need of Braking Using Convolutional Neural Networks. In Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-670-5, SciTePress, pages 600-607. DOI: 10.5220/0012163800003543

in Bibtex Style

@conference{icinco23,
author={Razvan Itu and Radu Danescu},
title={On-Board Estimation of Vehicle Speed and the Need of Braking Using Convolutional Neural Networks},
booktitle={Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2023},
pages={600-607},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012163800003543},
isbn={978-989-758-670-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - On-Board Estimation of Vehicle Speed and the Need of Braking Using Convolutional Neural Networks
SN - 978-989-758-670-5
AU - Itu R.
AU - Danescu R.
PY - 2023
SP - 600
EP - 607
DO - 10.5220/0012163800003543
PB - SciTePress