Abstract: A method, apparatus and computer program product are provided for surveillance of road environments via deep learning. In this regard, one or more features for vehicle sensor data associated with one or more vehicles traveling along a road segment proximate to a vehicle are determined. The vehicle includes one or more sensors that captures the vehicle sensor data. Furthermore, vehicle behavior data associated with the one or more vehicles is predicted using a machine learning model that receives the one or more features. The machine learning model is trained for detection of vehicle behavior based on historical vehicle sensor data and one or more rules associated with the road segment. The vehicle behavior data is also encoded in a database to facilitate modeling of vehicle behavior associated with the road segment.

Abstract: A method, apparatus and computer program product are provided controlling transmission of sensor data. First sensor is processed to identify an area of interest and generate a trigger signal to direct the transmission of additional sensor data pertaining to the area of interest. Transmission of sensor data may be identified and/or prioritized based on various factors such as proximity, angle, orientation, interest level, category, rating, sensor type, sensor data quality and/or the like. Certain transmission characteristics may be controlled, directed and/or varied such as quantity, quality, rate of transmission, transmission frequencies and intervals, and/or the like. As such, user experience may be improved, and the consumption and timing of certain computation resources may be improved.