Autonomous Vehicle Engineering - November 2022 - 15
Sensors
enables pushing the boundaries of the
algorithms and correctly identifying
complex objects on the road.
The sensor's entire FoV must be
covered to achieve high test coverage
and run comprehensive test scenarios.
A wide FoV is needed, ideally with RF
front ends that are static in space, to
enable reproducible and accurate AoA
validation. Similarly, it is important to
test the radar sensor's ability to notice
object's heights and traffic on multi-layered
roads. A practical example - the
autonomous vehicle must not apply the
brakes when another vehicle is crossing
on an overpass, but correctly detect the
overpass and drive underneath it instead.
Realistic traffic scenes require the
emulation of objects very close to the
radar unit. For example, at a stoplight
where cars are no more than two meters
apart, bikes might move into the lane or
pedestrians might suddenly cross the
road. Passing this test is critical for the
safety features of an ADAS/AD.
Object separation, the ability to
distinguish between obstacles on the
road, is another test area for a smoother
and faster transition to L4 and L5
vehicles. For example, a radar detection
algorithm will need to differentiate
between a guard rail and a pedestrian
while the car is driving on a highway.
Greater confidence in
ADAS functionality
More targets, shorter minimum
distance, higher resolution, and a
continuous field of view are essential to
real-world testing. In the lab, this will
enable an increase in test coverage to
not only save time, but safely run and
repeat test scenarios.
A traditional radar target simulator
(RTS) will return one reflection
independent of distance while a radar
scene emulator increases the number
of reflections as the vehicle gets closer,
also known as dynamic resolution. This
means the number of objects varies with
the distance of the object.
AD and ADAS software decisions
must be based on the complete picture,
not only on what the test equipment
allows. New radar emulation technology
is one more way to shift testing of complex
driving scenarios from the road to the lab.
To learn more, visit https://www.keysight.
com/find/DiscoverRSE. ■
Silviu Tuca is the radarbased
autonomous
vehicle product line
manager for Keysight
Technologies. He is an
expert in RF electronics,
biophysics, and calibration
tech.
AUTONOMOUS VEHICLE ENGINEERING
November 2022 15
https://www.keysight.com/find/DiscoverRSE
https://www.keysight.com/find/DiscoverRSE
http://info.hotims.com/82341-705
Autonomous Vehicle Engineering - November 2022
Table of Contents for the Digital Edition of Autonomous Vehicle Engineering - November 2022
Autonomous Vehicle Engineering - November 2022 - CVR1
Autonomous Vehicle Engineering - November 2022 - CVR2
Autonomous Vehicle Engineering - November 2022 - 1
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