Brain4Cars analyzes driving behavior with AI to predict and prevent road accidents

By Chloe Olewitz October 14, 2015

As cars gets smarter, tech developments in our dashboards aim to help humans keep up, or at the very least to keep human drivers alert behind the wheel. To make this happen, researchers have developed a deep learning neural network called Brain4Cars that combines the many approaches of top-of-the-line smart car technology, and augments them with artificial intelligence.

Cars obviously aren’t the only things getting smarter, and as our gadget collections pile up we’re prone to becoming ever more distracted. Even with existing road safety regulations and legislation to keep drivers safe, 33,000 people die in road accidents every year. What’s more, over 90 percent of road accidents in the U.S. can be attributed to driver error, according to the National Highway Traffic Safety Authority.

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Many car manufacturers have already taken to installing safety systems in new models that alert drivers to dangerous behaviors and external environmental risks. Brain4Cars basically takes all these technologies and rolls them into one, combining safety measures like internal and external cameras with GPS, vehicle dynamics reporting, and an extensive database of driver behavior recordings.

What sets Brain4Cars apart is what it does with this comprehensive, real-time data collection. By feeding information from the data sources into a proprietary artificial intelligence, the system is able to reliably predict individual driver behavior in real-time, and anticipate errors up to 3.5 seconds in advance with an 80% accuracy rate.

Researchers from Stanford and Cornell modeled the Brain4Cars system on Recurrent Neural Networks with Long Short-Term Memory. The artificial intelligence analyzes in-car visual cues like the driver’s facial expressions and sightlines, while external camera feeds provide the system with a contextual understanding of the safety and appropriateness of driver choices in relation to the situation unfolding on the road.

In theory, a deep learning architecture like this could come to learn the habits of each individual driver it assists. Over time, Brain4Cars would be able to predict driver accuracy with greater precision and recall as data from the specific driver becomes part of the network’s knowledge base.

This background contextual information provided to the neural network was amassed over the course of 1,180 miles driven collectively by ten different drivers in California. Road areas included freeway and city driving to expand the AI’s contextual knowledge for commonly encountered road situations and external car environments.

Even driverless cars get their fair share of heat when it comes to on-road errors, but prediction about the future of autonomous cars suggest that accidents and collisions will be mostly due to human error — not robotic drivers. But instead of replacing human drivers entirely, Brain4Cars combines the best of driver safety tech to bring a bit of robotic precision support to humans behind the wheel.

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