Autonomous Vehicle Engineering - November 2022 - 14

Sensors
Fig. 2: SAE levels of vehicle autonomy.
of <4m: Many test cases, such as the New Car
Assessment Program's (NCAP) Vulnerable Road User
Protection - AEB Pedestrian, require object emulation
very close to the radar unit. Most of the target
simulation solutions existing on the market today are
designed for long distances.
Lower resolution between objects: Until
Figs. 3a and 3b: Target simulation vs. scene emulation.
To close the gap between real-world testing and
simulation, real and physical sensors are needed in the
test setup. This complexity must be added to the test to
predict how AVs will behave on the road.
The vision is for technology to fully replace the
human behind the wheel to enable reliable, accurate,
and safe decisions on the road. Software simulation
cannot fully test the real sensor response and testing
on the track is not repeatable.
When emulating radar targets, several technology
gaps currently exist:
Limited number of targets and FoV:
A common approach ties each simulated target to a
delay line. Even if additional targets are added, only
one radar echo is processed at a time. Also, if an
antenna array is created, it isn't possible to simultaneously
emulate targets at the extreme ends of the radar
module's field of view. In addition, each movement of
the antennas introduces a change in the echo's angle
of arrival (AoA), which might lead to errors and loss
of accuracy in rendering targets, if not recalculated.
Inability to generate objects at distances
recently, target simulators could only generate one
object as one radar signature - this leaves gaps in scene
details. For example, on a crowded multi-lane boulevard,
test equipment must accurately tell the difference
between all the traffic participants. With only one echo
per object, the algorithm might not be able to tell the
difference between a bicycle and a lamp post.
New technology is needed
Full-scene emulation in the lab is key to developing the
robust radar sensors and algorithms needed to realize
ADAS capabilities on the path to full vehicle autonomy.
One method is to shift from an approach centered on
object detection via target simulation to traffic scene
emulation (Figs. 3a and 3b). This will enable the ability
to emulate complex scenarios, including coexisting
high-resolution objects, with a wide field of view and
a reduced minimum object distance.
Real-life vehicles are in fact extended objects,
whose dimensions span multiple radar sensor resolution
cells. As a result, the radar sensors report multiple
detections of these objects in a single scan. Object
tracking algorithms are employed to identify and
cluster these detections into objects. Realistic emulation
of such objects by means of multiple radar echoes
14 November 2022
AUTONOMOUS VEHICLE ENGINEERING
Keysight
SAE International
Autonomous Vehicle Engineering - November 2022

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