Abstract
In this paper, we propose a new Fuzzy-based Simulation System for Driver Risk Management in Vehicular Ad hoc Networks (VANETs). The proposed system considers Driver’s Health Condition (DHC), Vehicle’s Environment Condition (VEC), Weather Condition (WC), Road Condition (RC) and Vehicle Speed (VS) to assess the risk level. The proposed system is composed of two Fuzzy Logic Controllers (FLCs): FLC1 and FLC2. FLC1 has the following inputs: WC, RC and VS and its output, together with VEC and DHC, serve as input parameters for FLC2. The input parameters’ data can come from different sources, such as on-board and on-road sensors and cameras, sensors and cameras in the infrastructure and from the communications between the vehicles. Based on the system’s output i.e., driving risk level, a smart box informs the driver for a potential risk/danger and provides assistance. We show through simulations the effect of the considered parameters on the determination of the driving risk and demonstrate a few actions that can be performed accordingly.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bylykbashi, K., Elmazi, D., Matsuo, K., Ikeda, M., Barolli, L.: Effect of security and trustworthiness for a fuzzy cluster management system in VANETs. Cogn. Syst. Res. 55, 153–163 (2019). https://doi.org/10.1016/j.cogsys.2019.01.008
Bylykbashi, K., Elmazi, D., Matsuo, K., Ikeda, M., Barolli, L.: Implementation of a fuzzy-based simulation system and a testbed for improving driving conditions in VANETs. In: International Conference on Complex, Intelligent, and Software Intensive Systems, pp. 3–12. Springer (2019). https://doi.org/10.1007/978-3-030-22354-01
Bylykbashi, K., Qafzezi, E., Ikeda, M., Matsuo, K., Barolli, L.: A fuzzy-based system for driving risk measurement (FSDRM) in VANETs: a comparison study of simulation and experimental results. In: International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, pp. 14–25. Springer (2019)
Bylykbashi, K., Qafzezi, E., Ikeda, M., Matsuo, K., Barolli, L.: Fuzzy-based driver monitoring system (FDMS): implementation of two intelligent FDMSs and a testbed for safe driving in VANETs. Future Gener. Comput. Syst. 105, 665–674 (2020). https://doi.org/10.1016/j.future.2019.12.030
Bylykbashi, K., Qafzezi, E., Ikeda, M., Matsuo, K., Barolli, L., Takizawa, M.: A fuzzy-based simulation system for driving risk management in VANETs considering weather condition as a new parameter. In: International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, pp. 23–32. Springer (2020)
Gusikhin, O., Filev, D., Rychtyckyj, N.: Intelligent vehicle systems: applications and new trends. In: Informatics in Control Automation and Robotics, pp. 3–14. Springer (2008). https://doi.org/10.1007/978-3-540-79142-31
Hartenstein, H., Laberteaux, L.: A tutorial survey on vehicular ad hoc networks. IEEE Commun. Mag. 46(6), 164–171 (2008)
Kandel, A.: Fuzzy Expert Systems. CRC Press, Boca Raton (1991)
Klir, G.J., Folger, T.A.: Fuzzy Sets, Uncertainty, and Information. Prentice Hall Inc, Upper Saddle River (1987)
McNeill, F.M., Thro, E.: Fuzzy Logic: A Practical Approach. Academic Press, Cambridge (1994)
Munakata, T., Jani, Y.: Fuzzy systems: an overview. Commun. ACM 37(3), 69–77 (1994). https://doi.org/10.1145/175247.175254
Qafzezi, E., Bylykbashi, K., Ikeda, M., Matsuo, K., Barolli, L.: Coordination and management of cloud, fog and edge resources in SDN-VANETs using fuzzy logic: a comparison study for two fuzzy-based systems. Internet Things 11, 100169 (2020)
SAE On-Road Automated Driving (ORAD) committee: taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles. Technical report, Society of Automotive Engineers (SAE) (2018). https://doi.org/10.4271/J3016201806
Zadeh, L.A., Kacprzyk, J.: Fuzzy Logic for the Management of Uncertainty. Wiley, New York (1992)
Zimmermann, H.J.: Fuzzy Set Theory and its Applications. Springer Science & Business Media, New York (1996). https://doi.org/10.1007/978-94-015-8702-0
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Bylykbashi, K., Qafzezi, E., Ampririt, P., Matsuo, K., Barolli, L., Takizawa, M. (2021). An Integrated Fuzzy-Based Simulation System for Driving Risk Management in VANETs Considering Road Condition as a New Parameter. In: Barolli, L., Li, K., Miwa, H. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2020. Advances in Intelligent Systems and Computing, vol 1263. Springer, Cham. https://doi.org/10.1007/978-3-030-57796-4_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-57796-4_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-57795-7
Online ISBN: 978-3-030-57796-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)