Autonomous Vehicle Engineering - July 2022 - 4

Cover Story
Sensing Enters a
New Era
by Lawrence Burns, Babek Hassibi, and Behrooz Rezvani
Neural Propulsion Systems' advanced information theory enables
AVs to 'see' everything sooner, clearer and farther ahead.
More than 1.3 million people die on the world's
roadways each year and another 50 million are
injured. While roadway safety has improved over
time, all countries face formidable challenges in
reducing the trend. To accelerate progress, Vision
Zero and the U.S. National Roadway Safety Strategy
have made the objective not just improved safety, but
zero roadway deaths.
This article answers three questions:
* How well must vehicles 'see' to eliminate
preventable roadway deaths?
* What is required to see this well?
* How can these requirements be met?
We conclude that to eliminate preventable
roadway deaths under the worst conditions, vehicles
must sense (sample) and process information (the
environment) a billion times faster than humans. We
also conclude it is possible for an advanced sensor
system to 'see' this well and that this system can be
commercialized within the next two years.
How well must vehicles see?
Accidents can be prevented when vehicles can avoid
hazards by safely stopping or swerving. A vehicle
suddenly crossing into oncoming traffic or a boulder
suddenly falling in front of a car usually can't be
avoided. But, whenever vehicles obeying traffic laws
can safely stop or swerve if required, such preventable
accidents can be eliminated.
Our premise is that seeing sooner, clearer and
farther under all roadway conditions will accelerate
progress toward zero roadway deaths. Seeing sooner
reduces sensing time so perception can start earlier;
seeing clearer improves perception reliability so
braking/swerving can start earlier and seeing farther
allows sensing to start earlier.
To eliminate preventable roadway deaths, vehicles
must sense things that humans can't. Advances in radar,
lidar, processors and analytics are allowing vehicles to
sense up ahead and around much better than humans
can see. This helps eliminate preventable accidents in
worst driving conditions and allows smoother braking
in less-than-worst conditions. Worst conditions include:
* rain, snow, ice, fog and darkness
* maximum allowable speeds for a given road
design
* curves, slopes, buildings and canyons that can
obstruct vision
* lower bounds on brake conditions relative to
loads and tire conditions relative to temperature,
pressure and surface friction
We used principles from information theory and
physics to determine the data rate (bits/sec) required
to reconstruct worst-case scenes with sufficient precision
(fidelity) and frequency (frames/sec) such that
preventable accidents do not occur.1
To illustrate, consider the scene faced by a
heavy-duty truck operating on a highway in snow
4 July 2022
AUTONOMOUS VEHICLE ENGINEERING

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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