Autonomous Vehicle Engineering - November 2022 - 13

Sensors
More targets, shorter minimum distance,
higher resolution, and a continuous FoV
are essential to real-world radar testing.
Road testing of the complete integrated system
within a prototype or road-legal vehicle enables
OEMs to validate the final product before bringing it
to market. This system is helping to advance the development
of better trained algorithms, with the goal of
greater overall safety on the road.
The most recent data suggests self-driving cars
could reduce traffic deaths by as much as 90% (Fig.
1). ADAS in current production vehicles have reached
SAE L2 and L3, which in most traffic situations require
the driver to control the vehicle. Many OEMs and
industry experts believe pushing further toward SAE
L4 and L5 autonomy - where five represents vehicles
not requiring any human interaction - will make our
roadways safer (Fig. 2).
To achieve the next level in vehicle autonomy,
many advancements are required. There will be
massive investments in sensor technologies, such as
radar, lidar and camera, which will continue to improve
environmental scanning. As each sensor type has its
own advantages and disadvantages, they will need to
complement each other to ensure the object detection
process has the required built-in redundancy.
Huge investments in computationally powerful
software algorithms are also necessary to combine and
carry the large amount of high-resolution sensor data
including vehicle-to-everything (V2X) communication
inputs. Machine learning (ML) is the established method
for training self-improving algorithms and artificial intelligence
(AI). Those algorithms are then making decisions
to ensure safety in complex traffic situations. Training
these algorithms with the most realistic stimuli available,
in a repeatable and controlled fashion in the lab, is crucial
for their accuracy and their safe deployment.
The testing/simulation gap
Today, a large amount of testing time is spent focused
on sensors and their control modules (ECU) by simulating
environments in software or software-in-theloop
(SIL) testing. Road testing of the completely integrated
system within a prototype or road-legal vehicle
allows OEMs to validate the final product before
bringing it to market. Recreating a virtual world in
the lab, with accurate rendering of the scenes, plus real
radar sensors and signals, will bridge the gap between
simulation and road testing.
The challenge today is the emulation of full radar
scenes, especially when the scenes are complex and
have many variables. The goal is to thoroughly test
all driving scenarios in the lab, even the corner cases,
before bringing the vehicle to the test track or open
roadways. Software simulation is used in the early
development cycle. The software ultimately is an
abstract view, and it has imperfections and limitations.
Relying only on real-world road testing is also
unrealistic because it would take millions of meters for
vehicles to become safely reliable to navigate in urban
and rural roadways 100% of the time. To truly test the
AV/ADAS functionality, it is necessary to control all
relevant parameters.
AUTONOMOUS VEHICLE ENGINEERING
November 2022 13
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Autonomous Vehicle Engineering - November 2022

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