Understanding spots along a route that are likely to cause hard-braking is just one part of the equation. Training our models on both sets of data makes it possible to spot actual deceleration moments from fake ones, making detection across all trips more accurate. This represents a relatively small subset of data, but it’s highly accurate because Maps is now tethered to a stable spot - your car display. To combat this, we also use information from routes driven with Google Maps when it's projected on a car’s display, like Android Auto. This is what makes it hard for our systems to decipher you tossing your phone into the cupholder or accidentally dropping it on the floor from an actual hard-braking moment. Mobile phone sensors can determine deceleration along a route, but this data is highly prone to false alarms because your phone can move independently of your car. The first set of information comes from phones using Google Maps. To do this, we train our machine learning models on two sets of data. But how exactly do we find when and where these moments are likely to occur? We believe these updates have the potential to eliminate over 100 million hard-braking events in routes driven with Google Maps each year.
GOOGLE MAPS TRAFFIC TIMES DRIVERS
We use AI and navigation information to identify hard-braking events - moments that cause drivers to decelerate sharply and are known indicators of car crash likelihood - and then suggest alternate routes when available. Let’s start with our routing update that helps you avoid situations that cause you to slam on the brakes, such as confusing lane changes or freeway exits. Teaching Maps to identify and forecast when people are hitting the brakes
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