A fuzzy-based driver assistance system using human cognitive parameters and driving style information |
| |
| Affiliation: | 1. Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education (KARE), Virudhunagar, Tamil Nadu, India;2. Department of Computer Science and Engineering, FST, ICFAI Foundation for Higher Education, Hyderabad, India;3. Maharaja Agrasen Institute of Technology, Delhi, India;4. Department of Computer Applications, Alagappa University, Karaikudi, India;1. Department of Electronics and Communication Engineering, TKM College of Engineering, India;2. Department of Electrical and Electronics Engineering, TKM College of Engineering, India;3. A P J Abdul Kalam Technological University, Thiruvananthapuram, India;1. Department of Psychosomatic, Imam Khomeini Hospital Complex (IKHC), School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran;2. Department of Neuroscience, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran;3. Neuroscience Research Center, Qom University of Medical Sciences, Qom, Iran;4. Spiritual Health Research Center, Qom University of Medical Sciences, Qom, Iran;5. Department of Molecular Medicine, Pasteur Institute of Iran, Tehran, Iran |
| |
| 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 |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
