A fuzzy-based driver assistance system using human cognitive parameters and driving style information

Vasconez, Juan Pablo, Viscaino, Michelle, Guevara, Leonardo and Auat Cheein, Fernando (2020) A fuzzy-based driver assistance system using human cognitive parameters and driving style information. Cognitive Systems Research, 64 . pp. 174-190. ISSN 1389-0417

Full content URL: https://doi.org/10.1016/j.cogsys.2020.08.007

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Abstract

Reducing the number of traffic accidents due to human errors is an urgent need in several countries around the world. In this scenario, the use of human-robot interaction (HRI) strategies has recently shown to be a feasible solution to compensate human limitations while driving. In this work we propose a HRI system which uses the driver’s cognitive factors and driving style information to improve safety. To achieve this, deep neural networks based approaches are used to detect human cognitive parameters such as sleepiness, driver’s age and head posture. Additionally, driving style information is also obtained through speed analysis and external traffic information. Finally, a fuzzy-based decision-making stage is proposed to manage both human cognitive information and driving style, and then limit the maximum allowed speed of a vehicle. The results showed that we were able to detect human cognitive parameters such as sleepiness –63% to 88% accuracy–, driver’s age –80% accuracy– and head posture –90.42% to 97.86% accuracy– as well as driving style –87.8% average accuracy. Based on such results, the fuzzy-based architecture was able to limit the maximum allowed speed for different scenarios, reducing it from 50 km/h to 17 km/h. Moreover, the fuzzy-based method showed to be more sensitive with respect to inputs changes than a previous published weighted-based inference method.

Keywords:Human robot interaction, Human cognition, Driver assistance system, Fuzzy logic
Subjects:H Engineering > H671 Robotics
H Engineering > H330 Automotive Engineering
H Engineering > H130 Computer-Aided Engineering
Divisions:College of Science > Lincoln Institute for Agri-Food Technology
ID Code:53707
Deposited On:23 Mar 2023 16:48

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