No abstract

No abstract

No abstract

The availability of advanced driver assistance systems (ADAS), for safety and well-being, is becoming increasingly important to avoid traffic accidents caused by fatigue, stress, or distractions. In this sense, automatic identification of a driver among a group of various drivers (i.e. real-time driver identification) is a key factor in the development of ADAS, mainly when driver's comfort and security is also to be taken into account. The main objective 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, 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 signal and gas pedal signal. The identification of a driver within groups with 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. In this sense, 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 µ µ µ µs.

No abstract