46 / 84
Road
safety
has
benefited
significantly from Moore’s law,
increases in processing capability
and the development of CMOS
Image Sensors (CIS) and other
sensor technologies have enabled
vehicle manufacturers to introduce
Advanced Driver Awareness Systems
(ADAS). ADAS enhances the driver’s
awareness of the environment
around them reducing the chances
of collision. Some systems are also
capable of monitoring the driver and
alerting them, should they become
sleepy for instance.
Increasingly ADAS also takes
control (or provides information
to autonomous driving systems),
providing assistance to the driver
with capabilities like parking assist,
lane assist and adaptive cruise
control.
It is no surprise therefore the
ADAS market is predicted to be
worth $42 Billion a year by 2021
and is currently experiencing a
10% Compounded Annual Growth
Rate (CAGR) (Source: http://www.
marketsandmarkets.com/Market-Reports/driver-assistance-systems-
market-1201.htm).
ADAS use a wide spectrum of
sensors encompassing embedded
vision, RADAR and LIDAR, often
to extract the information required
they utilize a sensor fusion approach
combining information from several
sensors. Within the embedded vision
sphere, ADAS can be split further into
two categories, external monitoring
which addresses aspects like lane
departure, object detection, blind spot
detection and traffic sign recognition.
While internal systems monitor
aspects such as driver drowsiness
and eye detection. Both internal and
external ADAS applications bring
with them challenges to address in
implementing the image processing
algorithms.
These challenges range from the
ability to implement the algorithms
required for the application, to
complyingwith the correct automotive
standards. Many ADAS applications
also require sensor fusion to combine
the inputs from several sensors,
significantly increasing the required
processing power. Sensor fusion can
be homogeneous where the multiple
sensors of the same type are used,
or heterogeneous where different
sensor types are used to extract the
information required.
Many applications utilize an All
Programmable SoC or FPGA to
implement the system due to the
flexibility provided. Both to implement
the required algorithms but also
due to the ability to interface with
different sensors types and networks.
Along with performance ADAS
applications also come with several
Considerations for Advanced Driver Awareness
Systems why use an All Programmable SoC
Aaron Behman & Adam Taylor, XILINX
Automotive
Special Edition
46 l New-Tech Magazine Europe