Autonomous Vehicle Engineering - July 2022 - 5

Cover Story
Fig. 1: Class-8 truck operating at night, in snow.
at night (Fig. 1). Reconstructing this scene with sufficient
precision requires segmenting the short-range
space around the truck and the long-range space up
ahead into small " cube-like " building blocks called
voxels. Greater precision can be attained by interrogating
larger numbers of smaller voxels.
To safely stop/swerve under the worst conditions,
trucking company operators and autonomous truck
developers believe they need to see about 250 meters
(820 ft.) around trucks and 1000 meters (3,281 ft.) up
ahead with a long-range field of view (FoV) of 30 degrees.
Using these requirements, about 3 billion voxels need
to be probed by sensors to reconstruct the Fig. 1 scene.
Voxels at 1,000 meters are about 2 x 2 x 2 ft. (.6x .6x
.6 m) and require orders of magnitude more sampling
than voxels at 100 meters (328 ft.). For zero preventable
roadway deaths, high precision is required, which means
very detailed scenes must be updated rapidly.
Scene data is obtained by converting continuous
analog signals representing physical measurements
into time separated 0 and 1 digital streams representing
information. When radar and lidar sensors interrogate
raw data in voxels, they need to reliably detect targets
and avoid false alarms. Because environments are noisy,
sensors must probe voxels numerous times to attain
over 90% reliability and less than one-in-a-million false
alarms. To eliminate preventable accidents, this performance
must be attained for the weakest signal-to-noise
ratio (worst-conditions) at 1000 meters.
Based on representative values of key variables and
sensitivity analyses, our calculations indicate the required
data rate to enable zero preventable roadway deaths under
the worst conditions approaches a staggering 7 x 1015
bits/sec (7 Pb/sec)!2
To put this in perspective, the input
sensory data rate from our eyes to our brain is about 10 x
106
bits/sec3
, about one-billionth of the required information
processing rate to prevent accidents. Thus, humans
cannot sense and process information fast enough to
eliminate preventable roadway deaths.
Like humans, cameras alone also cannot sense well
enough for zero preventable roadway deaths. Cameras
are similar to our eyes in that they work well when
there is sufficient ambient and it is not necessary to see
through objects and around corners. But, to get to zero
roadway deaths, we need to quickly detect objects that
are hidden from view in worst-case lighting conditions.
AUTONOMOUS VEHICLE ENGINEERING
July 2022 5
NPS

Autonomous Vehicle Engineering - July 2022

Table of Contents for the Digital Edition of Autonomous Vehicle Engineering - July 2022

Autonomous Vehicle Engineering - July 2022 - Cov4
Autonomous Vehicle Engineering - July 2022 - Cov1
Autonomous Vehicle Engineering - July 2022 - Cov2
Autonomous Vehicle Engineering - July 2022 - 1
Autonomous Vehicle Engineering - July 2022 - 2
Autonomous Vehicle Engineering - July 2022 - 3
Autonomous Vehicle Engineering - July 2022 - 4
Autonomous Vehicle Engineering - July 2022 - 5
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Autonomous Vehicle Engineering - July 2022 - Cov3
Autonomous Vehicle Engineering - July 2022 - Cov4
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