Abstract
As vehicles get increasingly automated, they need to properly evaluate different situations and assess threats at run-time. In this scenario automated vehicles should be able to evaluate risks regarding a dynamic environment in order to take proper decisions and modulate their driving behavior accordingly. In order to avoid collisions, in this work we propose a risk estimator based on fuzzy logic which accounts for risk indicators regarding (1) the state of the driver, (2) the behavior of other vehicles and (3) the weather conditions. A scenario with two vehicles in a car-following situation was analyzed, where the main concern is to avoid rear-end collisions. The goal of the presented approach is to effectively estimate critical states and properly assess risk, based on the indicators chosen.
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
Boverie, S., Cour, M., Le Gall, J.Y.: Adapted human machine interaction concept for driver assistance systems driveasy. IFAC Proc. Vol. 44(1), 2242–2247 (2011)
Flemisch, F., Heesen, M., Hesse, T., Kelsch, J., Schieben, A., Beller, J.: Towards a dynamic balance between humans and automation: authority, ability, responsibility and control in shared and cooperative control situations. Cogn. Technol. Work 14(1), 3–18 (2012)
González, D., Pérez, J., Milanés, V., Nashashibi, F., Tort, M.S., Cuevas, A.: Arbitration and sharing control strategies in the driving process. In: Towards a Common Software/Hardware Methodology for Future Advanced Driver Assistance Systems, p. 201 (2017)
Harding, J., et al.: Vehicle-to-vehicle communications: Readiness of v2v technology for application. Technical report DOT HS 812 014. National Highway Traffic Safety Administration, Washington, DC, August 2014
Hayward, J.C.: Near miss determination through use of a scale of danger. Technical report, Pennsylvania Transportation and Traffic Safety Center (1972)
Van der Horst, A.R.A.: A time based analysis of road user behaviour in normal and critical encounters. No. HS-041 255 (1990)
Katrakazas, C., Quddus, M., Chen, W.H., Deka, L.: Real-time motion planning methods for autonomous on-road driving: state-of-the-art and future research directions. Transp. Res. Part C Emerg. Technol. 60, 416–442 (2015). http://www.sciencedirect.com/science/article/pii/S0968090X15003447
Kilpeläinen, M., Summala, H.: Effects of weather and weather forecasts on driver behaviour. Transp. Res. Part F Traffic Psychol. Behav. 10(4), 288–299 (2007)
Lattarulo, R., Pérez, J., Dendaluce, M.: A complete framework for developing and testing automated driving controllers. IFAC PapersOnLine 50(1), 258–263 (2017)
Lefèvre, S., Laugier, C., Ibañez-Guzmán, J.: Evaluating risk at road intersections by detecting conflicting intentions. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4841–4846. IEEE (2012)
Lefèvre, S., Vasquez, D., Laugier, C.: A survey on motion prediction and risk assessment for intelligent vehicles. ROBOMECH J. 1(1), 1 (2014)
Levenson, N.G.: System Safety and Computers. Addison Wesley, Boston (1995)
Llorca, D.F., et al.: Autonomous pedestrian collision avoidance using a fuzzy steering controller. IEEE Trans. Intell. Transp. Syst. 12(2), 390–401 (2011)
Pérez, J., et al.: Development and design of a platform for arbitration and sharing control applications. In: 2014 International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS XIV), pp. 322–328. IEEE (2014)
SAE: Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems. Standard, Society of Automotive Engineers, January 2014
Singh, S.: Critical reasons for crashes investigated in the national motor vehicle crash causation survey. Technical report, National Center for Statistics and Analysis (NCSA), NHTSA, February 2015
World Health Organization, WHO: Global status report on road safety 2015. Technical report, WHO, September 2015. Accessed 11 Sept 2017
Worrall, S., Orchansky, D., Masson, F., Nebot, E.: Improving vehicle safety using context based detection of risk. In: 2010 13th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 379–385. IEEE (2010)
Acknowledgments
This work was supported by the AMASS project (H2020-ECSEL) with grant agreement number 692474.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
González, L., Martí, E., Calvo, I., Ruiz, A., Pérez, J. (2018). Towards Risk Estimation in Automated Vehicles Using Fuzzy Logic. In: Gallina, B., Skavhaug, A., Schoitsch, E., Bitsch, F. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2018. Lecture Notes in Computer Science(), vol 11094. Springer, Cham. https://doi.org/10.1007/978-3-319-99229-7_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-99229-7_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99228-0
Online ISBN: 978-3-319-99229-7
eBook Packages: Computer ScienceComputer Science (R0)