Automotive Engineering - February 2024 - 22

Tuning-up AI's 'understanding'
to make safer ADAS, AVs
Massive data information
is interpreted and evaluated
by Kognic's platform to help
automated-driving and
ADAS systems make more
" human " decisions.
Kodiak Robotics commercial-truck " SensorPod " side-mirror
module includes front-facing Luminar-developed lidar and a wide
field-of-view camera; Kognic's software evaluates and manages
massive amounts of data from such sensor-fusion systems.
This has to happen very, very fast because you don't
have a lot of time - you have 10 milliseconds or something
to make a prediction. That's also a task that machine
learning is actually pretty well-suited for, is to predict
where all the trajectories are going.
The third step is the planning. You then need to figure
out, based on your goals, how do you plan to navigate
the scene as an agent, which is the preferred trajectory
for yourself? That's the part that, as of right
now, is actually fairly rule-based, or at least more traditional
in its design.
As of right now, I'm fairly sure an end-to-end trained
neural network that does perception prediction and
planning as one big chunk wouldn't be good enough.
And it appears that the leading robotaxi companies
agree to that. But it's possible that a solution will overtake
other options very quickly.
Either way, the designer of the system needs to carefully
consider what sort of data they base their product on,
because it doesn't really matter if it's end-to-end trained
or if it's the three P's: perception, prediction, planning.
22 February 2024
The 2023 Cruise robotaxi incident in San Francisco - in which a
pedestrian struck by another vehicle was subsequently dragged by
a Cruise robotaxi - seems like an example of lack of AI alignment,
because it appears the vehicle's intelligence did not understand
how to react to that situation.
Langkilde: It's fair to assume that that is not something they had
ever really seen in their training and testing data. I'm pretty sure
that was what you would call an edge case that they were not prepared
to handle.
There's nothing magical going on inside a machine-learning system.
It's a set of parameters that are fine-tuned based on a dataset
to the best of the computer's ability to replicate known things. If you
can't crisply define what your expected behavior is, you will suffer
from unwanted events.
When we started six years ago with Kognic and we talked to the
German OEMs, they basically said what is now becoming the big
problem for the robotaxi companies, that they have tried on these
kinds of technologies for many years, but building a working safety
case has always been very difficult.
Does Kognic have competitors?
Langkilde: So, it depends a little bit. It's a big world, so it's hard to
definitively claim you're unique, I guess, because there's always
someone who's intersecting a little bit here and there. The things that
we believe make us stand out in the global market is, first of all, the
combination of safety-critical applications, mobility and shaping datasets.
We are the only company that is exclusively focused on what
we call 'embodied AI' and the tools required to shape such datasets.
We focus on products that have a physical manifestation and that
require sensor fusion, so camera, lidar, radar, with the intention of
navigating the work. So if it's a niche, we are the global leader for
sure - and possibly we're maybe even the only one, actually. It
makes it easier to be the leader, I guess.
AUTOMOTIVE ENGINEERING
FROM LEFT: KODIAK; KOGNIC
Automotive Engineering - February 2024

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