Chettle, John Edward

A first computing device is configured to receive an indication of subscription request to at least one data of a plurality of data fields of a media bridging protocol from a second computing device. The first computing device receives the indication of subscription request as specifying one or more types of messages that correspond to at least one of the plurality of data fields and that are to be sent via the bridging protocol to the second computing device. The first computing device is further configured to generate messages for the plurality of data fields and determine a subset of the generated messages that are associated with the subscribed data fields to be provided to the second computing device. The first computing device is further configured to provide the subset of messages to the second computing device via the bridging protocol.

The disclosed systems and techniques are directed to tracking and predicting live availability of parking resources by a fleet of vehicles. The disclosed techniques include receiving communications vehicles of a fleet with identification of object(s) located at an edge of a driving environment and edge visibility data for the edge from sensing systems of the vehicles, updating a map of live parking space occupancy for the driving environment with the received identification and the edge visibility data, determining, based on the updated map of live parking space occupancy and a historical parking space availability, a likelihood value associated with a parking space in the driving environment remaining unoccupied within a target time, and directing a vehicle of the fleet to the parking space based at least on the likelihood value.

Example embodiments relate to camera arrangements for vehicular object detection and avoidance. An example system includes a vehicle and at least one camera of a first camera type attached to the vehicle. The system also includes a plurality of cameras of a second camera type attached to the vehicle. Further, the system includes a plurality of cameras of a third camera type attached to the vehicle. Moreover, the system includes a computing device communicatively coupled to the at least one camera of the first camera type, plurality of cameras of the second camera type, and plurality of cameras of the third camera type. The computing device is configured to identify objects located within a first range of distances from the vehicle, objects located within a second range of distances from the vehicle, and objects located within a third range of distances from the vehicle.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining respective sensor data captured by one or more sensors of an autonomous vehicle at each of a sequence of time steps, the sequence of time steps comprising one or more context time steps followed by one or more prediction time steps; generating respective ground truth birds-eye-view (BEV) representations of the respective sensor data for each of the prediction time steps; for each prediction time step, processing the respective sensor data at one or more preceding time steps in the sequence using a future prediction neural network to generate a predicted BEV representation for the prediction time step; and training the future prediction neural network based on, for each prediction time step, an error between the ground truth BEV representation for the prediction time step and the predicted BEV representation for the prediction time step.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a prediction task using sensor data. The method includes obtaining scene data characterizing a scene in an environment at a current time point, wherein the scene comprises an autonomous vehicle and a plurality of agents, wherein the scene data comprises sensor data captured by one or more sensors of the autonomous vehicle and scene context data; generating, from the sensor data using a multimodal language model (MLM) neural network, one or more text outputs that each describe one or more aspects of the scene; generating, from at least the one or more text outputs describing the one or more aspects of the scene and the scene context data, a prediction input to a prediction neural network; and processing the prediction input using the prediction neural network to generate a prediction output for the prediction task.

Provided are methods, systems, devices, and tangible non-transitory computer readable media for offline mapping and navigation. The disclosed technology determines a current region associated with a current location of a navigation device. Predicted travel destinations from the current region are determined based on aggregated travel data. The aggregated travel data can include previous travel destinations from within the current region. Based on the current region and the predicted travel destinations, additional regions associated with the current region are determined. Further, connectivity information is determined based on network data. The connectivity information is associated with network connectivity within the additional regions. The additional regions is prioritized based on the connectivity information. Furthermore, portions of offline map data associated with the respective additional regions are sent to the navigation device in an order partly based on the priority of the additional regions.

The disclosed systems and techniques are directed to identifying and responding to presence of target objects in occluded areas of driving environments. The techniques include training, using perception data associated with a first driving scene, a first machine learning model (MLM) to determine a location, within the first driving scene, of a target object masked with a masking transformation. The techniques further include training, using an output of the first MLM for a training driving scene, a second MLM to generate a map of probabilities of one or more target objects to be in an occluded region of the training driving scene, the training driving scene comprising at least one of the first driving scene or a second driving scene, and causing the second MLM to be deployed on an autonomous vehicle.

In one example, a method is described. The method includes receiving a wireless signal from a remote control device; determining that the wireless signal comprises a command sent from the remote control device to a media presentation device, where the media presentation device is configured to provide streaming media to a media output device for presentation in a monitored environment, and where the computing device, the remote control device, the media presentation device, and the media output device are located within the monitored environment; based on the determination that the wireless signal comprises the command sent from the remote control device to the media presentation device, controlling the media output device to capture an image of the streaming media and transmit the image to the computing device; and based on the image, determining source information identifying a streaming service provider as a source of the streaming media.

A virtual meeting user interface, UI (146), is presented during a virtual meeting between a plurality of participants. The UI comprises a plurality of regions each corresponding to a media stream provided by one of a plurality of client devices (140A - 140n). The plurality of regions comprises a region corresponding to one or more media streams provided to a client device (140A). The one or more media streams, each comprising respective metadata, are received at the client device. Respective metadata of a media stream indicates a spatial location in the media stream of a participant. One or more content presentation layout characteristics of the client device are identified. A visual representation of the media stream is caused to be modified in the region based at least on the location and the layout characteristics. The virtual meeting UI comprising the region with the modified visual representation is presented on the client device.

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