Artificial Intelligence recognizes potentially critical traffic situations seven seconds in advance
From:
Technical University of Munich (TUM)
March 30, 2021 -- A team of researchers
at the Technical University of Munich (TUM) has developed a new early warning
system for vehicles that uses artificial intelligence to learn from thousands
of real traffic situations. A study of the system was carried out in cooperation
with the BMW Group. The results show that, if used in today's self-driving
vehicles, it can warn seven seconds in advance against potentially critical
situations that the cars cannot handle alone -- with over 85% accuracy.
To make self-driving cars safe in the
future, development efforts often rely on sophisticated models aimed at giving
cars the ability to analyze the behavior of all traffic participants. But what
happens if the models are not yet capable of handling some complex or
unforeseen situations?
A team working with Prof. Eckehard
Steinbach, who holds the Chair of Media Technology and is a member of the Board
of Directors of the Munich School of Robotics and Machine Intelligence (MSRM)
at TUM, is taking a new approach. Thanks to artificial intelligence (AI), their
system can learn from past situations where self-driving test vehicles were
pushed to their limits in real-world road traffic. Those are situations where a
human driver takes over -- either because the car signals the need for
intervention or because the driver decides to intervene for safety reasons.
Pattern
recognition through RNN
The technology uses sensors and cameras
to capture surrounding conditions and records status data for the vehicle such
as the steering wheel angle, road conditions, weather, visibility and speed.
The AI system, based on a recurrent neural network (RNN), learns to recognize
patterns with the data. If the system spots a pattern in a new driving
situation that the control system was unable to handle in the past, the driver
will be warned in advance of a possible critical situation.
"To make vehicles more autonomous,
many existing methods study what the cars now understand about traffic and then
try to improve the models used by them. The big advantage of our technology: we
completely ignore what the car thinks. Instead we limit ourselves to the data
based on what actually happens and look for patterns," says Steinbach.
"In this way, the AI discovers potentially critical situations that models
may not be capable of recognizing, or have yet to discover. Our system
therefore offers a safety function that knows when and where the cars have
weaknesses."
Warnings up to
seven seconds in advance
The team of researchers tested the
technology with the BMW Group and its autonomous development vehicles on public
roads and analyzed around 2500 situations where the driver had to intervene.
The study showed that the AI is already capable of predicting potentially
critical situations with better than 85 percent accuracy -- up to seven seconds
before they occur.
Collecting
data with no extra effort
For the technology to function, large
quantities of data are needed. After all, the AI can only recognize and predict
experiences at the limits of the system if the situations were seen before.
With the large number of development vehicles on the road, the data was
practically generated by itself, says Christopher Kuhn, one of the authors of
the study: "Every time a potentially critical situation comes up on a test
drive, we end up with a new training example." The central storage of the
data makes it possible for every vehicle to learn from all of the data recorded
across the entire fleet.
https://www.sciencedaily.com/releases/2021/03/210330121234.htm
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