Digital Signal Processing Reference
In-Depth Information
Similarly, the brake pressure application is observed to vary from driver
to driver considerably. There are drivers who exhibit a single-step continuous
breaking action, an initial big kick in the pedal followed by a number of
smaller kicks, and multiple kicks with close values. This can be attributed to
the way a particular driver has adjusted himself/herself to best use the
vehicle they normally drive.
In particular, the relative frequency of the accelerator pedal pressure is
concentrated under for driver 1 with a peak at 0.35. However, its
brake pressure has sharp peaks around 0.25 and The first peak is
expectedly the initial impact on the brake pedal after making the decision to
stop or to slow down.
On the other hand, driver 3 has multiple peaks over a very long range
after the initial impact for the accelerator behavior but it has a sharp peak
around 3.9 in the brake pressure plot. Yet another observation is the brake
histograms for drivers 2 and 3 are regularly higher that of driver 6.
Despite the apparent variations among these eight drivers, unfortunately,
it was not clear from these plots that neither of the two measurements alone
would be sufficient to identify the driver completely.
5.
INTEGRATION OF MULTI-SENSOR DATA
Limitations imposed by unimodal treatment of driving features could be
overcome by using multiple modalities or data fusion as it was recently done
in a number biometric systems (Chapter 16 in this topic and [10,17].
Preliminary findings from such systems, known as
multimodal biometric
systems
indicate higher performance and more reliable due to the presence of
multiple, independent pieces of evidence. Data fusion has been effectively
used in speech processing community very successfully since 1970s.
Excitation signals, gain, zero-crossing rate, pitch information, and LPC
coefficients or their offsprings have been fused in one form or another in
speech compression, speech/speaker recognition and speaker verification
applications. In this section, we propose a multiple sensor version of the
ubiquitous linear prediction model for studying the driver individuality.
