Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | International Joint Conference on Neural Networks, IJCNN 2014 |
| Publisher | IEEE |
| Pages | 1848-1855 |
| ISBN (Electronic) | 978-147991484-5, 978-1-4799-6627-1 |
| ISBN (Print) | 978-1-4799-1482-1 |
| DOIs | |
| Publication status | Published - Sept 2014 |
| Event | Neural Networks, 2014 International Joint Conference - Beijing, China Duration: 6 Jul 2014 → 11 Jul 2014 |
Conference
| Conference | Neural Networks, 2014 International Joint Conference |
|---|---|
| Abbreviated title | IJCNN |
| Country/Territory | China |
| City | Beijing |
| Period | 6/07/14 → 11/07/14 |
Bibliographical note
The paper was given at the International Joint Conference on Neural Networks, IJCNN 2014; Beijing; China; 6 July 2014 through 11 July 2014© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
