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: Systems and methods for synchronization are provided. In some aspects, a method for synchronizing an image sensor is provided. The method includes receiving image data captured using an image sensor that is moving along a pathway, and assembling an image sensor trajectory using the image data. The method also includes receiving position data acquired along the pathway using a position sensor, wherein timestamps for the image data and position data are asynchronous, and assembling a position sensor trajectory using the position data. The method further includes generating a spatial transformation that aligns the image sensor trajectory and position sensor trajectory, and synchronizing the image sensor based on the spatial transformation.

Abstract: Systems and methods for synchronization are provided. In some aspects, a method for synchronizing an image sensor is provided. The method includes receiving image data captured using an image sensor that is moving along a pathway, and assembling an image sensor trajectory using the image data. The method also includes receiving position data acquired along the pathway using a position sensor, wherein timestamps for the image data and position data are asynchronous, and assembling a position sensor trajectory using the position data. The method further includes generating a spatial transformation that aligns the image sensor trajectory and position sensor trajectory, and synchronizing the image sensor based on the spatial transformation.

Abstract: An apparatus and/or method includes localization of a mobile device. The localization technique is calibrated by prompting the mobile device for a collection of static position images for an image-based localization service, sending static position images to the image-based localization service, receiving, for the static position images, a plurality of localization values from the image-based localization service, calculating a correction for the plurality of localization values, and providing a location for the mobile device based on the correction for the plurality of localization values.

Abstract: An apparatus and/or method includes localization of a mobile device. The localization technique is calibrated by prompting the mobile device for a collection of static position images for an image-based localization service, sending static position images to the image-based localization service, receiving, for the static position images, a plurality of localization values from the image-based localization service, calculating a correction for the plurality of localization values, and providing a location for the mobile device based on the correction for the plurality of localization values.

Abstract: Systems and methods are provided to incentivize collection of high definition (HD) map content. The system includes at least one sensor, a communications interface, and a device processor. The sensor is configured to acquire sensor data relating to a feature at a location on a roadway. The communications interface is configured to communicate with at least one other device. The device processor is configured to generate an observation data package from the sensor data, perform, using the location, a spatial query on a blockchain configured to store a plurality of data entries to identify whether any data entries of the plurality of data entries exist for the observation data package. When no data entries exist, the device processor is configured to generate a new data entry for the blockchain for the observation data package, and when a data entry exists, validate the observation data package and augment the existing data entry.