Abstract
Professional truck drivers are an essential part of
transportation in keeping the global economy alive and
commercial products moving. In order to increase
productivity and improve safety, an increasing amount of
automation is implemented in modern trucks. Transition to
automated heavy good vehicles is intended to make trucks
accident-free and, on the other hand, more comfortable to
drive. This motivates the automotive industry to bring
more embedded ICT into their vehicles in the future. An
avenue towards autonomous vehicles requires robust
environmental perception and driver monitoring
technologies to be introduced. This is the main
motivation behind the DESERVE project. This is the study
of sensor technology trials in order to minimize blind
spots around the truck and, on the other hand, keep the
river's vigilance at a sufficiently high level. The
outcomes are two innovative truck demonstrations: one R &
D study for bringing equipment to production in the
future and one implementation to the driver training
vehicle. The earlier experiments include both driver
monitoring technology which works at a 60% - 80% accuracy
level and environment perception (stereo and thermal
cameras) whose performance rates are 70% - 100%. The
results are not sufficient for autonomous vehicles, but
are a step forward, since they are in-line even if moved
from the lab to real automotive implementations.
Original language | English |
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Number of pages | 12 |
Journal | World Journal of Engineering and Technology |
Volume | 3 |
Issue number | 3 |
Publication status | Published - 2015 |
MoE publication type | A1 Journal article-refereed |
Keywords
- autonomous driving
- camera
- driver monitoring
- environment perception
- automated vehicle
- sensor
- laser scanner
- truck
- radar
- data fusion