The availability of advanced driver assistance systems (ADAS), for safety and well-being, is becoming increasingly important for avoiding traffic accidents caused by fatigue, stress, or distractions. For this reason, automatic identification of a driver from among a group of various drivers (i.e. real-time driver identification) is a key factor in the development of ADAS, mainly when the driver’s comfort and security is also to be taken into account. The main focus of this work is the development of embedded electronic systems for in-vehicle deployment of driver identification models. We developed a hybrid model based on artificial neural networks (ANN), and cepstral feature extraction techniques, able to recognize the driving style of different drivers. Results obtained show that the system is able to perform real-time driver identification using non-intrusive driving behavior signals such as brake pedal signals and gas pedal signals. The identification of a driver from within groups with a reduced number of drivers yields promising identification rates (e.g. 3-driver group yield 84.6 %). However, real-time development of ADAS requires very fast electronic systems. To this end, an FPGA-based hardware coprocessor for acceleration of the neural classifier has been developed. The coprocessor core is able to compute the whole ANN in less than 4 μs.
|Title of host publication||International Joint Conference on Neural Networks, IJCNN 2014|
|ISBN (Electronic)||978-147991484-5, 978-1-4799-6627-1|
|Publication status||Published - Sep 2014|
|Event||Neural Networks, 2014 International Joint Conference - Beijing, China|
Duration: 6 Jul 2014 → 11 Jul 2014
|Conference||Neural Networks, 2014 International Joint Conference|
|Period||6/07/14 → 11/07/14|
Bibliographical noteThe paper was given at the International Joint Conference on Neural Networks, IJCNN 2014; Beijing; China; 6 July 2014 through 11 July 2014
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Del Campo, I., Finker, R., Martinez, M. V., Echanobe, J., & Doctor, F. (2014). A Real-Time Driver Identification System based on Artificial Neural Networks and Cepstral Analysis. In International Joint Conference on Neural Networks, IJCNN 2014 (pp. 1848-1855).  IEEE. https://doi.org/10.1109/IJCNN.2014.6889772