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.

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.

New early warning system for self-driving cars: AI recognizes potentially critical traffic situations seven seconds in advance
A team of researchers has developed a new early warning system for vehicles that uses artificial intelligence to learn from thousands of real traffic situations. The results show that, if used in today’s self-driving vehicles, it can warn seven seconds in advance against potentially critical situati…

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