A fuzzy-based driver assistance system using human cognitive parameters and driving style information. (December 2020)

• Record Type: Journal Article
• Authors: Vasconez, Juan Pablo; Viscaino, Michelle; Guevara, Leonardo; Auat Cheein, Fernando
• Is Part Of: Cognitive systems research. Volume 64(2020)
• Page Start: 174
• Page End: 190
• Publication Date: 2020-12
• Subjects: Human robot interaction -- Human cognition -- Driver assistance system -- Fuzzy logic; Cognition -- Periodicals; Cognitive engineering (System design) -- Periodicals; Artificial intelligence -- Periodicals; 153.05
• DOI: 10.1016/j.cogsys.2020.08.007
• Languages: English
• ISSNs: 1389-0417

Abstract

Highlights: A driver assistance system based on driver cognitive parameters and driving style is proposed. Deep neural networks-based approaches are used to detect human cognitive parameters. Driving style assessment is done based on traffic sign detection and vehicle speed analysis. A fuzzy-based approach is used to combine the whole information about driver behavior. 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.