A Car That Can Anticipate Your Next Driving Maneuver

Through a project called Brain4Cars, Stanford and Cornell researchers released a new architecture consisting of Recurrent Neural Networks (RNNs) that use Long Short-Term Memory (LSTM) units to predict driving maneuvers several seconds in advance. This enables assistive cars to alert drivers before they make a dangerous maneuver. Maneuver anticipation complements existing Advance Driver Assistance Systems (ADAS) by giving drivers more time to react to road situations and thereby can prevent many accidents.

Can that can anticipate next maneuver

Using a Tesla K40, the researchers trained their deep learning architecture in a sequence-to-sequence prediction manner, and it explicitly learns to predict the future given only a partial temporal context. We further introduce a novel loss layer for anticipation which prevents over-fitting and encourages early anticipation. They use their architecture to anticipate driving maneuvers several seconds before they happen on a natural driving data set of 1180 miles. The context for maneuver anticipation comes from multiple sensors installed on the vehicle. The approach shows significant improvement over the state-of-the-art in maneuver anticipation by increasing the precision from 77.4% to 90.5% and recall from 71.2% to 87.4%.

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About Brad Nemire

Brad Nemire leads the Developer Communications team at NVIDIA focused on evangelizing amazing GPU-accelerated applications. Prior to NVIDIA, he worked at Arm on the Developer Relations team. Brad graduated from San Diego State University.