Driving behavior classification using long short term memory networks

Mumcuoğlu, Mehmet Emin and Alcan, Gökhan and Ünel, Mustafa and Çiçek, Onur and Mutluergil, Mehmet and Yılmaz, Metin and Köprübaşı, Kerem (2019) Driving behavior classification using long short term memory networks. In: International Conference of Electrical and Electronic Technologies for Automotive, Torino, Italy

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Abstract

Researchers in the automotive industry aim to enhance the performance, safety and energy management of intelligent vehicles with driver assistance systems. The performance of such systems can be improved with a better understanding of driving behaviors. In this paper, a driving behavior recognition algorithm is developed with a Long Short Term Memory (LSTM) Network using driver models of IPG's TruckMaker. Six driver models are designed based on longitudinal and lateral acceleration limits. The proposed algorithm is trained with driving signals of these drivers controlling a realistic truck model with five different trailer loads on an artificial training road. This training road is designed to cover possible road curves that can be seen in freeways and rural highways. Finally, the algorithm is tested with driving signals that are collected with the same method on a realistic road. Results show that the LSTM structure has a substantial capability to recognize dynamic relations between driving signals even in small time periods.
Item Type: Papers in Conference Proceedings
Uncontrolled Keywords: Acceleration behavior; Classification; Driver behaviors; Intelligent vehicles; LSTM networks
Subjects: T Technology > TJ Mechanical engineering and machinery > TJ163.12 Mechatronics
T Technology > TL Motor vehicles. Aeronautics. Astronautics
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Mechatronics
Faculty of Engineering and Natural Sciences
Depositing User: Mustafa Ünel
Date Deposited: 31 Jul 2019 11:21
Last Modified: 26 Jul 2023 15:55
URI: https://research.sabanciuniv.edu/id/eprint/37739

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