Abstract
This paper presents a Soft Computing based system to identify risky driving maneuvers on conventional two-lane roads. Road design and vehicle dynamics are considered. Specifically, a fuzzy rule-based Mamdani-type inference system is applied. The vehicle dynamics features are measured by smartphone inertial sensors. The real data obtained from the GPS, accelerometer, and gyroscope are used to identify the driving maneuvers. A conventional two-lane road located in the Madrid Region, Spain is used for this research. The results obtained with the fuzzy system are promising and suggest that this intelligent system can be used to warn drivers of a risky maneuver in real time for a safer, more ecological and comfortable driving.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Santos, M., López, V.: Fuzzy decision system for safety on roads. In: Lu, J., Jain, L.C., Zhang, G. (eds) Handbook on Decision Making, pp. 171–187. Springer, Berlin (2012). https://doi.org/10.1007/978-3-642-25755-1_9
MartÃn, S., Romana, M.G., Santos, M.: Fuzzy model of vehicle delay to determine the level of service of two-lane roads. Expert Syst. Appl. 54, 48–60 (2016)
Arroyo, C., Bergasa, L.M., Romera, E.: Adaptive fuzzy classifier to detect driving events from the inertial sensors of a smartphone. In: 2016 IEEE 19th International Conference on Intelligent Transportation Systems ITSC, pp. 1896–1901. IEEE (2016)
Wu, C., Yu, D., Doherty, A., Zhang, T., Kust, L., Luo, G.: An investigation of perceived vehicle speed from a driver’s perspective. PLoS ONE 12(10), e0185347 (2017)
Zeeman, A.S., Booysen, M.J.: Combining speed and acceleration to detect reckless driving in the informal public transport industry. In: 16th International IEEE Conference on Intelligent Transportation Systems ITSC 2013, pp. 756–761. IEEE (2013)
Ly, M.V., Martin, S., Trivedi, M.M.: Driver classification and driving style recognition using inertial sensors. In: 2013 IEEE Intelligent Vehicles Symposium IV, pp. 1040–1045. IEEE (2013)
Yay, E., Madrid, N.M.: SEEDrive--an adaptive and rule based driving system. In: 2013 9th International Conference on Intelligent Environments, pp. 262–265. IEEE (2013)
Hsu, Y.W., Perng, J.W., Wu, Z.H.: Design and implementation of an intelligent road detection system with multisensor integration. In: 2016 International Conference on Machine Learning and Cybernetics ICMLC, vol. 1, pp. 219–225. IEEE (2016)
Pinilla, A.C.C., Quintero, M.C.G., Premachandra, C.: Intelligent driving diagnosis based on a fuzzy logic approach in a real environment implementation. In: 2014 IEEE Intelligent Vehicles Symposium Proceedings, pp. 102–107. IEEE (2014)
Khan, M.Q., Lee, S.: A comprehensive survey of driving monitoring and assistance systems. Sensors 19(11), 2574 (2019)
Spanish Ministry of Development: Standard 3.1- IC. Road tracing. Order FOM/273/2016, of February 19 (2016). www.fomento.gob.es/recursos_mfom/norma_31ic_trazado_orden_fom_273_2016.pdf
AASHTO (American Association of State Highway and Transportation Officials): A Policy on Geometric Design of Highways and Streets, 6th edn. Washington, D.C. (2011)
Bergasa, L.M., AlmerÃa, D., Almazán, J., Yebes, J.J., Arroyo, R.: Drivesafe: an app for alerting inattentive drivers and scoring driving behaviors. In: 2014 IEEE Intelligent Vehicles Symposium Proceedings, pp. 240–245. IEEE (2014)
Romera, E., Bergasa, L.M., Arroyo, R.: Need data for driver behaviour analysis? Presenting the public UAH-DriveSet. In: 2016 IEEE 19th International Conference on Intelligent Transportation Systems ITSC, pp. 387–392. IEEE (2016)
Mamdani, E.H.: Application of fuzzy algorithms for control of simple dynamic plant. In: Proceedings of the Institution of Electrical Engineers, vol. 121(12), pp. 1585–1588. IET, (1974)
Khodairy, M.A., Abosamra, G.: Driving behavior classification based on oversampled signals of smartphone embedded sensors using an optimized stacked-LSTM neural networks. IEEE Access 9, 4957–4972 (2021)
Zhang, J., Liu, M., Sun, Z.: A modified mixed car-following model considering that the connected and intelligent vehicle and non-connected vehicle. In: Journal of Physics: Conference Series, vol. 1910(1), p. 012019. IOP Publishing (2021)
Silva, M.I., Henriques, R.: Finding manoeuvre motifs in vehicle telematics. Accid. Anal. Prev. 138, 105467 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Barreno, F., Santos, M., Romana, M. (2022). Fuzzy Logic System for Risk and Energy Efficiency Estimation of Driving Maneuvers. In: Gude Prego, J.J., de la Puerta, J.G., GarcÃa Bringas, P., Quintián, H., Corchado, E. (eds) 14th International Conference on Computational Intelligence in Security for Information Systems and 12th International Conference on European Transnational Educational (CISIS 2021 and ICEUTE 2021). CISIS - ICEUTE 2021. Advances in Intelligent Systems and Computing, vol 1400. Springer, Cham. https://doi.org/10.1007/978-3-030-87872-6_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-87872-6_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87871-9
Online ISBN: 978-3-030-87872-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)