all AI news
Context-Aware Quantitative Risk Assessment Machine Learning Model for Drivers Distraction
Feb. 22, 2024, 5:41 a.m. | Adebamigbe Fasanmade, Ali H. Al-Bayatti, Jarrad Neil Morden, Fabio Caraffini
cs.LG updates on arXiv.org arxiv.org
Abstract: Risk mitigation techniques are critical to avoiding accidents associated with driving behaviour. We provide a novel Multi-Class Driver Distraction Risk Assessment (MDDRA) model that considers the vehicle, driver, and environmental data during a journey. MDDRA categorises the driver on a risk matrix as safe, careless, or dangerous. It offers flexibility in adjusting the parameters and weights to consider each event on a specific severity level. We collect real-world data using the Field Operation Test (TeleFOT), …
abstract accidents arxiv assessment class context cs.cy cs.lg data driver driving environmental environmental data journey machine machine learning machine learning model matrix novel quantitative risk risk assessment type
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
AI Research Scientist
@ Vara | Berlin, Germany and Remote
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Robotics Technician - 3rd Shift
@ GXO Logistics | Perris, CA, US, 92571