Amazon Technologies, Inc.

Brett Francis

David Joseph Mifsud

Michael Garcia

Nicholas Jay Lefler

Edwin Ricardo Mendez Rodriguez

Indraneel Mitra

Roland Mesde

Alex Bessonov

Matthew Jonathan Narksusook

Abhijit Dayakar

Stefano Marzani

Mohammad Asif Ali Khan

Guy Ernest

Dave Wayne Lasley

Desmond O'Neil Davis

A connectivity monitor of a vehicle determines current and/or future states of antennas. A workload monitor of the vehicle receives execution criteria for different workloads to be executed. An intelligent connectivity engine at the vehicle receives the current and/or future states of the antennas and the execution criteria for the respective workloads. Based on the current and/or future states of the antennas and the execution criteria for the respective workloads, the intelligent connectivity engine assigns at least one of the respective workloads for current execution and at least another of the respective workloads for future execution. A client may use an intelligent connectivity service to configure various aspects of the vehicle connectivity. For example, the client can provide workload recommendation code for the intelligent connectivity engine to assign workloads for current or future execution.

Intelligent connectivity for vehicles

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 system comprising one or more computing devices implements a vehicle analysis service, which generates logical instructions for deployment to a vehicle or a fleet of vehicles to implement in-vehicle data analysis in the vehicle or fleet of vehicles. Additionally, the vehicle analysis service and/or locally deployed analysis module provides updates based on newly learned trends in vehicle data or newly learned correlations for the vehicle or similarly situated vehicles. The vehicle analysis service and locally deployed analysis module enables analysis to be performed using detailed high-resolution vehicle data, without requiring large volumes of vehicle data to be streamed to a remote location for analysis. Also, the vehicle analysis service provides customized analysis modules that can perform local analysis using existing computing resources of the vehicle or the vehicles of the fleet.

Vehicle analysis service for providing logic for local analysis and additional remote support

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 system comprising one or more computers implements a virtual domain control unit/virtual electronic control unit service configured to deploy vehicle code packages to one or more of a plurality of supported virtual domain control unit/electronic control unit orchestration environments, which include both a local orchestration environment and one or more remote orchestration environments. In such orchestration environments, virtual domain control units and/or virtual electronic control units are implemented that execute code included in the vehicle code packages. In some embodiments, such virtual domain control units or virtual electronic control units allow computing capacity and/or data storage capacity of a vehicle to be augmented via remotely implemented virtual domain control units and/or remotely implemented virtual electronic control units.

Virtual vehicle domain control unit (DCU) service and orchestration environments

A system comprising one or more computers implements a synthetic sensor service configured to deploy synthetic sensors to an in-vehicle computing device implementing a synthetic sensor orchestration environment for a vehicle. The synthetic sensor orchestration environment determines a placement decision for a new synthetic sensor to be added to the vehicle based on one or more annotations included in a synthetic sensor package for the new synthetic sensor. The synthetic sensor service and respective synthetic sensor orchestration environments implemented in various types of vehicles provide a consistent way to remotely add additional synthetic sensors and/or other functionality to different types of vehicles after the vehicles have already been put in use by respective owners or operators of the vehicles.

In-vehicle synthetic sensor orchestration and remote synthetic sensor service

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.

Vehicle environment discrepancy identification and discrepancy distribution to other vehicles

An agent is installed on a computing device of a vehicle (e.g., an ECU); the agent receives messages from an application installed on the same computing device. The computing device is connected to a gateway computing device via a physical communication channel (e.g., CAN bus or Ethernet cable). When the gateway agent receives a message from the application, it converts the message into a different protocol based on a mapping of the protocols to physical communication channels. The message is sent to a gateway agent at the gateway device, where it is converted back to the original protocol so the message can be processed/transmitted to the internet. By using an agent, the in-vehicle application does not need to know about the vehicle's network architecture or changes to it in order to access the internet.

Converting communication protocols for messages to enable internet access for vehicle applications

A system comprising one or more computers implements a synthetic sensor service configured to deploy synthetic sensors to in-vehicle computing devices implementing an in-vehicle distributed computing environment. A synthetic sensor may be placed monolithically at a single computing device (e.g. ECU) in the vehicle, or may be modularly placed on multiple computing devices (e.g. multiple ECUs) of the vehicle that each have resources or inputs that the synthetic sensor requires. The modular components of the synthetic sensor may execute in a runtime environment of the in-vehicle distributed computing environment, such that the modular components function as a unified synthetic sensor even though they are placed on different computing devices of the vehicle (e.g. different ECUs).

In-vehicle distributed computing environment

A method of deploying machine learning-based models may include identifying a profile of a target execution environment to implement a machine learning-based model in communication with a cloud infrastructure. The method may further include identifying, using the profile, a software module implementing the model. The method may further include causing the software module to be uploaded from a code repository associated with the cloud infrastructure to the target execution environment.

Growing community of inventors

Redwood City, CA, United States of America

Brett Francis