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Self Driving Cars Analyze Other Drivers

Self Driving Cars Analyze Other Drivers
A team of researchers have developed a new early warning system for vehicles that uses artificial intelligence to learn from thousands of real traffic situations.
Technology Briefing


To make future self-driving cars safe, 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 of researchers at the Technical University of Munich is taking a new approach. It 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 with over 85% accuracy about potentially critical situations that the cars cannot handle alone. 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 (or RNN), learns to recognize patterns within 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, this method studies what the cars now understand about traffic and then try to improve the models used by them.

The big advantage of this technology is that it completely ignores what the car thinks. Instead, it limits it to the data based on what actually happens and look for patterns. In this way, the AI discovers potentially critical situations that models may not be capable of recognizing or have yet to discover.

This system therefore offers a safety function that knows when and where the cars have weaknesses. The team of researchers tested the technology with the BMW Group using 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. 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. Every time a potentially critical situation came up during a test drive, it created a new training example for the RNN. Best of all, central storage of the data makes it possible for every vehicle to simultaneously learn from all of the data recorded across the entire fleet.


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