<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The lack of attention during the driving task is considered as a major risk factor for fatal road accidents around the world. Despite the ever-growing trend for autonomous driving which promises to bring greater road-safety benefits, the fact is today’s vehicles still only feature partial and conditional automation, demanding frequent driver action. Moreover, the monotony of such a scenario may induce fatigue or distraction, reducing driver awareness and impairing the regain of the vehicle’s control. To address this challenge, we introduce a non-intrusive system to monitor the driver in terms of fatigue, distraction, and activity. The proposed system explores state-of-the-art sensors, as well as machine learning algorithms for data extraction and modeling. In the domain of fatigue supervision, we propose a feature set that considers the vehicle’s automation level. In terms of distraction assessment, the contributions concern (i) a holistic system that covers the full range of driver distraction types and (ii) a monitoring unit that predicts the driver activity causing the faulty behavior. By comparing the performance of Support Vector Machines against Decision Trees, conducted experiments indicated that our system can predict the driver’s state with an accuracy ranging from 89% to 93%.</p></abstract>

The lack of attention during the driving task is considered as a major risk factor for fatal road accidents around the world. Despite the ever-growing trend for autonomous driving which promises to bring greater road-safety benefits, the fact is today’s vehicles still only feature partial and conditional automation, demanding frequent driver action. Moreover, the monotony of such a scenario may induce fatigue or distraction, reducing driver awareness and impairing the regain of the vehicle’s control. To address this challenge, we introduce a non-intrusive system to monitor the driver in terms of fatigue, distraction, and activity. The proposed system explores state-of-the-art sensors, as well as machine learning algorithms for data extraction and modeling. In the domain of fatigue supervision, we propose a feature set that considers the vehicle’s automation level. In terms of distraction assessment, the contributions concern (i) a holistic system that covers the full range of driver distraction types and (ii) a monitoring unit that predicts the driver activity causing the faulty behavior. By comparing the performance of Support Vector Machines against Decision Trees, conducted experiments indicated that our system can predict the driver’s state with an accuracy ranging from 89% to 93%.

Detecting Driver’s Fatigue, Distraction and Activity Using a Non-Intrusive Ai-Based Monitoring System

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

The lack of attention during the driving task is considered as a major risk factor for fatal road accidents around the world. Despite the ever-growing trend...

Detecting Driver’s Fatigue, Distraction and Activity Using a Non-Intrusive Ai-Based Monitoring System

Data publikacji: 30 sie 2019

Zakres stron: 247 - 266

Otrzymano: 08 lis 2018

Przyjęty: 20 sty 2019

DOI: https://doi.org/10.2478/jaiscr-2019-0007

Słowa kluczowe
driver monitoring system, intelligent transportation systems, driver distraction monitoring, driver fatigue monitoring

© 2019 Miguel Costa et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Detecting Driver’s Fatigue, Distraction and Activity Using a Non-Intrusive Ai-Based Monitoring System

Data publikacji: 30 sie 2019

Zakres stron: 247 - 266

Otrzymano: 08 lis 2018

Przyjęty: 20 sty 2019

DOI: https://doi.org/10.2478/jaiscr-2019-0007

Słowa kluczowedriver monitoring system, intelligent transportation systems, driver distraction monitoring, driver fatigue monitoring

© 2019 Miguel Costa et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Słowa kluczowe
driver monitoring system, intelligent transportation systems, driver distraction monitoring, driver fatigue monitoring