Researchers design AI-based early warning system for autonomous cars

With massive importance being given to passenger safety in self-driving cars, a squad of researchers from the Technical University of Munich (TUM), in this regard, has designed a new early warning AI-based system for autonomous vehicles.

As per reliable reports, a study was recently carried out in association with the BMW Group, which was published in the journal IEEE Transaction on Intelligent Transportation Systems. The results of the study depicted that, when used in the self-driving vehicles of today, the system can warn seven seconds in advance, with an efficiency and accuracy of 85%, against critical situations that the cars cannot handle alone.

Notably, the technology makes use of cameras and sensors to capture surrounding conditions and environment while simultaneously recording the data for vehicle such as road conditions, speed, visibility, and steering wheel angle.

The AI-system then, based on recurrent neural network, learns to detect patterns with the procured data. If the system detects a pattern in a new driving situation that the car control system was unable to handle formerly, the driver would be cautioned well before time of a possible emergency situation.

It has been reported that a team working with Prof. Eckehard Steinbach, an elite member of the panel of Directors of the Munich School of Robotics and Machine Learning at TUM, has taken the responsibility of commercializing the new approach.

Steinbach mentioned that the utmost advantage of their novel technology is that the AI determines hypothetically critical situations that various car models might not be proficient of detecting. In this case, the system thus provides a safety function that aptly identifies the weakened point of the cars.

Meanwhile, a co-author of the study, Christopher Kuhn, when questioned about the working of the system under possible critical situations, stated that every time an emergency situation comes up on a test drive, the team ends up with a new training example. The central storage of the data makes it really possible for every vehicle to gain knowledge from the data recorded across the entire convoy.

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