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Advanced Driver Assistance Systems (ADAS) have made driving safer over thelast decade. They prepare vehicles for unsafe road conditions and alert driversif they perform a dangerous maneuver. However, many accidents are unavoidablebecause by the time drivers are alerted, it is already too late. Anticipatingmaneuvers beforehand can alert drivers before they perform the maneuver andalso give ADAS more time to avoid or prepare for the danger.  In this work we propose a vehicular sensor-rich platform and learningalgorithms for maneuver anticipation. For this purpose we equip a car withcameras, Global Positioning System (GPS), and a computing device to capture thedriving context from both inside and outside of the car. In order to anticipatemaneuvers, we propose a sensory-fusion deep learning architecture which jointlylearns to anticipate and fuse multiple sensory streams. Our architectureconsists of Recurrent Neural Networks (RNNs) that use Long Short-Term Memory(LSTM) units to capture long temporal dependencies. We propose a novel trainingprocedure which allows the network to predict the future given only a partialtemporal context. We introduce a diverse data set with 1180 miles of naturalfreeway and city driving, and show that we can anticipate maneuvers 3.5 secondsbefore they occur in real-time with a precision and recall of 90.5\% and 87.4\%respectively.

Brain4Cars: Car That Knows Before You Do via Sensory-Fusion Deep Learning Architecture