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40 l New-Tech Magazine Europe
ill self-driving cars be able to
react better than a person can
when something unforeseen happens
on the road? That’s just one of many
questions that auto manufacturers
and the electronics industry will need
to address in the coming years.
Sensors are essential technology for
making it possible for vehicles to
act independently. Automakers are
now integrating into their systems
a variety of key sensor types:
LiDAR for generating 3D maps of
the environment, sonar for short-
range sensing, cameras for short-/
mid-range sensing, and radar for
mid-/long-range sensing. For many
advanced driver assistance systems
(ADAS) functions, decisions are
made by fusing or aggregating data
from multiple sensors. For instance,
an obstacle or pedestrian detection
function will typically fuse data from
cameras as well as radar sensors.
But, of course, sensors are only a part
of the equation. Just as important
are the sophisticated algorithms that
bring intelligence to the aggregated
data and the DSPs to do all of the
processing.
At Cadence, there’s a team of
engineers in the IP Group that spends
its time defining and developing
such algorithms and DSPs for ADAS
and communications applications.
Recently, I had the opportunity to chat
with two of the team members: Pierre-
Xavier Thomas, design engineering
group director, whose team develops
software product collateral for
Cadence Tensilica DSPs, such as DSP
libraries, application use cases, and
software signal processing example
kernels; and Pushkar Patwardhan,
design engineering architect.
Aggregating Data: in the
Cloud or in the Car?
Now, while advances in algorithms
and DSP and sensor technology
have been impressive, the act of
aggregating and then extracting
useful insights from collected
data remains a work in progress.
According to Patwardhan, who leads
development in radar algorithms,
automotive electronics engineers are
trying various approaches. “One of the
main challenges for ADAS functions
is to decide how to distribute the
processing and data aggregation
between the vehicle and the cloud,” he
said. “In one school of thought, more
data aggregation and processing are
done in the vehicle, with lesser data
communications overhead. Another
approach is a more cloud-centric
mechanism, with the vehicle requiring
more communications with the cloud
to obtain information about the
environment, with lesser processing
done within the vehicle itself. It’s not
clear yet which approach is a winner.”
W
Who’s the Better Decision-Maker: Self-
Driving Car or Human?
Christine Young, Cadence