التفاصيل البيبلوغرافية
| العنوان: |
Neural Networks for Driver Behavior Analysis |
| المؤلفون: |
Martinelli, Fabio, Marulli, Fiammetta, Mercaldo, Francesco, Santone, Antonella |
| المصدر: |
MDPI - Electronics - Special Issue Security and Trust in Next Generation Cyber-Physical Systems, (2021-02-01) |
| بيانات النشر: |
Zenodo |
| سنة النشر: |
2021 |
| المجموعة: |
Zenodo |
| مصطلحات موضوعية: |
Automotive, Artificial Intelligence, Neural Network, Machine Learning, Deep Learning, Safety |
| الوصف: |
The proliferation of info-entertainment systems in nowadays vehicles has provided a really cheap and easy-to-deploy platform with the ability to gather information about the vehicle under analysis. With the purpose to provide an architecture to increase safety and security in automotive context, in this paper we propose a fully connected neural network architecture considering position-based features aimed to detect in real-time: (i) the driver, (ii) the driving style and (iii) the path. The experimental analysis performed on real-world data shows that the proposed method obtains encouraging results. |
| نوع الوثيقة: |
article in journal/newspaper |
| اللغة: |
unknown |
| Relation: |
https://zenodo.org/communities/cybersane_h2020; https://doi.org/10.3390/electronics10030342; oai:zenodo.org:4555253 |
| DOI: |
10.3390/electronics10030342 |
| الاتاحة: |
https://doi.org/10.3390/electronics10030342 |
| Rights: |
info:eu-repo/semantics/openAccess ; Creative Commons Attribution 4.0 International ; https://creativecommons.org/licenses/by/4.0/legalcode |
| رقم الانضمام: |
edsbas.CCF2BB66 |
| قاعدة البيانات: |
BASE |