Amazon Technologies, Inc.

Indraneel Mitra

Brett Francis

Roland Mesde

Alex Bessonov

Stefano Marzani

David Joseph Mifsud

A vehicle information system maintains a street shadow representing attributes of an environment in which a vehicle is located or travels through. The attributes may include objects or events in the surrounding environment. In some embodiments, cloud-based computing devices maintain a primary street shadow, and roadside resources maintain information about short-lived objects or events that may be added to the primary street shadow to augment it. Additionally, a vehicle may further augment the street shadow representation with locally detected events and/or objects. Also, events and objects detected by a vehicle are provided to the roadside resource. The roadside resource may infer an estimated lifetime of the events and objects, and cache short-lived events and objects for distribution to other vehicles, and also provide long-lived events and objects to the cloud-computing devices for use in updating the primary street shadow.

Composite street shadows for vehicles

A vehicle artifact (e.g., software, firmware, content, etc.) distribution system uses direct vehicle-to-vehicle communications to extend a range of physical infrastructure and to improve speed and cost of distributing artifacts to vehicles. An artifact to be distributed is assigned a vehicle-to-vehicle distribution policy that defines gateway vehicle selection, wherein gateway vehicles are used to distribute the artifact to other recipient vehicles. A local software agent executing on the vehicles distributes the artifact in accordance with the vehicle-to-vehicle distribution policy using locally installed antennas of the vehicles.

Distribution network using roadside resources and direct vehicle-to-vehicle communication

A vehicle information system and an in-vehicle agent implement a system for detecting discrepancies, for example due to drift, between actual real-world conditions in an environment surrounding a vehicle and corresponding conditions as represented in a model used by the vehicle. Additionally, the in-vehicle agent provides the detected discrepancies to a road-side resource and/or cloud-based computing device in order to distribute the detected discrepancies to other vehicles in the area. Also, a model maintained at the cloud-based computing devices may be updated to account for the discrepancies. The roadside resource and/or vehicle may infer estimated lifetimes of the detected discrepancies and intelligently distribute information describing the discrepancies based on their respective estimated lifetimes.

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Edgewater, NJ, United States of America

Indraneel Mitra