mindtalks artificial intelligence: Perceptive AI Helps Curb Traffic Accidents – RTInsights – picked by mindtalks

Researchers are exploring how perceptive AI might be used to evaluate traffic pattern scenarios and make city driving safer.

The dream of eliminating road accidents moves one step closer thanks to a research team at Universitat Oberta de Catalunya (UOC). They’re exploring how perceptive AI might notice conditions for accidents and traffic hazards, offering humans the chance to intervene before it’s too late.

How does perceptive AI work?

The team, led by Cristina Bustos and Javier Borge, developed algorithms
used to train neural networks to recognize the conditions surrounding major
traffic issues. These patterns are present before an accident occurs, but human
perception frequently misses the mark because of the sheer amount of data.

For example, a human might recognize wet road conditions, but other
much more subtle factors may go unnoticed. The team points to what they call
“the urban scene,” things like parked cars or even the placement of
advertisements that can distract drivers, causing an accident. The number of
distinct elements found in a scene correlates with the number of accidents.

See also: The Killer App for Autonomous Cars: Airport Parking

Who created the study?

Complex Systems @ IN3 (CoSIN3), a research group located at the
Internet Interdisciplinary Institute, combined forces with the Scene
Understanding and Artificial Intelligence Lab (SUNAI) from the Faculty of
Computer Science, Multimedia and Telecommunications. Spain’s National Traffic
Authority and city councils from both Barcelona and Madrid also joined the
initiative.

Affiliates from MIT and Àlex Arenas, a researcher from the Department
of Computer Engineering and Mathematics at Universitat Rovira i Virgili (URV),
also lent expertise to the project. Together, the team took traffic data and
used deep learning applied to computer image
processing
. The result was a set of algorithms able to process the
complexity of a scene in urban traffic.

The convolutional neural network first discerns simple patterns across
a top layer of an image. As the algorithm goes deeper, the patterns become more
complex until the computer can recognize even a human face.

Deep learning is still a black box problem. Machines come to
conclusions, but they don’t tell us why. The system requires the expertise of
an urban planner or engineer who can validate the result and implement changes.

The algorithms need human experts

Researchers hope this type of research can help in future city planning
as humans continue to move and relocate. Right now, the team notes the biggest
hurdle is data availability to train the algorithm. However, the team is still
looking into multiple hazardous patterns to build a system that makes car
accidents and traffic hazards a thing of the past.

 

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