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Fig. 4.5
Concept of a Support Vector Machine. Source: Wikimedia Commons
4.4
Challenges and Practical Aspects
DDD systems are complex pieces of engineering, which should perform reliably
under a broad range of practical scenarios. In this section we summarize some of the
challenges and practical issues involved in the design and development of successful
DDD systems.
4.4.1
Data Collection
Due to the difficulty in collecting proper (electro-physiological, behavioral etc.)
driver drowsiness data in a real world environment, researchers have resorted to
safe and controlled simulated environments to carry out their experiments. The main
advantages of using simulators include: experimental control, efficiency, low cost,
safety, and ease of data collection [ 10 , 19 ].
Driving simulators are being increasingly used for training drivers all over the
world. Research has shown that driving simulators are proven to be excellent
practical and effective educational tools to impart safe driving training techniques
for all drivers. There are various types of driving simulators in use today, e.g., train
simulator, bus simulator, car simulator, truck simulator etc. The most complex, such
as the National Advanced Driving Simulator (Fig. 4.6 ), have a full-sized vehicle
body, with six-axis movement and 360-degree visual displays.
On the other end of the range, there are simple desktop simulators such as the
York Driving Simulator, which are often implemented using a computer monitor for
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