Abstract: A computer includes a processor and a memory storing instructions executable by the processor to predict a time to failure of a vehicle component according to a damage model generated from vehicle operating data, based on the predicted time to failure, retrieve a schematic of the vehicle component and input the schematic to an additive printer to manufacture a replacement vehicle component, and replace the vehicle component with the replacement vehicle component according to maintenance instructions that, based at least in part on the predicted time to failure, specify a time and location to replace the vehicle component.

Abstract: Harvesting the high performance computing capabilities of vehicle processors while the vehicles are parked and/or charging may provide use to otherwise unused resources as well as offer a way to discount parking and/or charging costs for the vehicle owner/driver. To harvest the computing capabilities, a dispatcher is utilized that receives incoming complex processing requests and parses the requests into tasks. The dispatcher dispatches the tasks to vehicle processors that are within, for example, a parking garage to which the dispatcher has access. The dispatcher may prioritize requests and/or prioritize the vehicle processors to distribute the tasks to optimize the power consumption, time, and processing capabilities used. When the vehicle processors complete the tasks and return results to the dispatcher, the dispatcher finalizes the job results and provides the results to the requestor. The dispatcher may discount the parking and/or charging costs for the vehicles that performed the computational tasks.

Abstract: Intelligent ticketing and data offload planning is provided. A data center receives a ticket request from a vehicle requesting to perform a data upload of vehicle data over a communications network. An optimizer is utilized to generate a ticket, the ticket specifying a time and a location for the vehicle to perform the data upload. The ticket is received from the vehicle. The ticket is validated to ensure that the vehicle should still perform the data upload. The vehicle is indicated to perform the data upload over the communications network responsive to the optimizer confirming the data upload to proceed. The data upload is stored to a storage of the data center.

Abstract: Devices, systems, and methods for remote control of autonomous vehicles are disclosed herein. A method may include receiving, by a device, first data indicative of an autonomous vehicle in a parking area, and determining, based on the first data, a location of the autonomous vehicle. The method may include determining, based on a the location, first image data including a representation of an object. The method may include generating second image data based on the first data and the first image data, and presenting the second image data. The method may include receiving an input associated with controlling operation of the autonomous vehicle, and controlling, based on the input, the operation of the autonomous vehicle.

Abstract: A system that includes a computer and an unmanned aerial vehicle (UAV) is described. The computer may be programmed to receive, from the UAV, image data of a vehicle parking region; to process the data by identifying an actuation of a UAV indicator that occurs within a predetermined interval of a response of a vehicle in the region; and based on the identification, to determine a location of the vehicle.

Abstract: Methods and apparatus for vehicle climate control using distributed sensors are disclosed. A disclosed example method includes receiving data from sensors distributed within a vehicle at a first controller, processing the data at the first controller to identify an event associated with the interior of the vehicle and sending an instruction based on the event from the first controller to a second controller of the vehicle to affect an operation of a climate control system of the vehicle.

Abstract: Autonomous vehicles may utilize neural networks for image classification in order to navigate infrastructures and foreign environments, using context dependent transfer learning adaptation. Techniques include receiving a transferable output layer from the infrastructure, which is a model suitable for the infrastructure and the local environment. Sensor data from the autonomous vehicle may then be passed through the neural network and classified. The classified data can map to an output of the transferable output layer, allowing the autonomous vehicle to obtain particular outputs for particular context dependent inputs, without requiring further parameters within the neural network.

Abstract: A user destination is identified based on received location information from a user. A first seat in a vehicle is assigned to the user based on the user destination. At least one of the first seat and a second seat in the vehicle is moved upon user entry until a distance between the first seat and the second seat is greater than a distance threshold.

Abstract: A system includes a processor configured to wirelessly broadcast a message obtained from a first originating vehicle BUS or controller, following a determination that the message was on a pre-approved list for broadcast and having encrypted the message utilizing a temporary random key generated for a message session. The system may include vehicle controllers, a gateway module, and vehicle BUSSES connecting the system controllers to the gateway module. The gateway module may include a memory storing a list of pre-approved message types and corresponding source types, and a processor configured to receive a message from one of the vehicle controllers over one of the vehicle BUSSES to determine if a message type and source type of the received message matches an element of the list.

Abstract: A system communicates with a plurality of vehicles onsite at a parking garage. The system determines an eventual intended destination for each respective vehicle of the plurality of vehicles and determines a path through the garage for each respective vehicle, to the destination for the respective vehicle. The system may segment the path into a plurality of shorter tasks for the respective vehicle, wherein each task moves the respective vehicle along the path until a point where the respective vehicle encounters an obstruction and instruct the respective vehicle to execute the shorter tasks for the respective vehicle.

Abstract: Devices, systems, and methods for remote control of autonomous vehicles are disclosed herein. A method may include receiving, by a device, first data indicative of an autonomous vehicle in a parking area, and determining, based on the first data, a location of the autonomous vehicle. The method may include determining, based on a the location, first image data including a representation of an object. The method may include generating second image data based on the first data and the first image data, and presenting the second image data. The method may include receiving an input associated with controlling operation of the autonomous vehicle, and controlling, based on the input, the operation of the autonomous vehicle.

Abstract: A system includes a computer remote from a vehicle. The computer is programmed to store a label of an odor associated with a user, instruct the vehicle to pick up the user, receive data from an environmental sensor in the vehicle, and output a message upon determining that the data indicate that a level of the odor exceeds a threshold.

Abstract: In one implementation, a method activates an unmanned aircraft inside a vehicle to capture images of the vehicle interior. The method accesses a flight path for the unmanned aircraft and receives data associated with the vehicle's current movement. The method adjusts the flight path of the unmanned aircraft to compensate for the vehicle's current movement.

Abstract: A system communicates with a plurality of vehicles onsite at a parking garage. The system determines an eventual intended destination for each respective vehicle of the plurality of vehicles and determines a path through the garage for each respective vehicle, to the destination for the respective vehicle. The system may segment the path into a plurality of shorter tasks for the respective vehicle, wherein each task moves the respective vehicle along the path until a point where the respective vehicle encounters an obstruction and instruct the respective vehicle to execute the shorter tasks for the respective vehicle.

Abstract: Methods and apparatus for vehicle HVAC control are disclosed. A disclosed example apparatus includes a portable device interface of a vehicle, the portable device interface to be communicatively coupled with a portable device, a heating, ventilation and air conditioning (HVAC) control system of the vehicle, where the HVAC control system is to be communicatively coupled to the portable device interface. The example apparatus also includes a processor to control the HVAC control system based on signals received at the portable device interface from the portable device.

Abstract: Autonomous vehicles may utilize neural networks for image classification in order to navigate infrastructures and foreign environments, using context dependent transfer learning adaptation. Techniques include receiving a transferable output layer from the infrastructure, which is a model suitable for the infrastructure and the local environment. Sensor data from the autonomous vehicle may then be passed through the neural network and classified. The classified data can map to an output of the transferable output layer, allowing the autonomous vehicle to obtain particular outputs for particular context dependent inputs, without requiring further parameters within the neural network.

Abstract: Harvesting the high performance computing capabilities of vehicle processors while the vehicles are parked and/or charging may provide use to otherwise unused resources as well as offer a way to discount parking and/or charging costs for the vehicle owner/driver. To harvest the computing capabilities, a dispatcher is utilized that receives incoming complex processing requests and parses the requests into tasks. The dispatcher dispatches the tasks to vehicle processors that are within, for example, a parking garage to which the dispatcher has access. The dispatcher may prioritize requests and/or prioritize the vehicle processors to distribute the tasks to optimize the power consumption, time, and processing capabilities used. When the vehicle processors complete the tasks and return results to the dispatcher, the dispatcher finalizes the job results and provides the results to the requestor. The dispatcher may discount the parking and/or charging costs for the vehicles that performed the computational tasks.

Abstract: A system includes a processor configured to receive image data gathered by a vehicle camera, relating to a fixed environmental feature. The processor is also configured to determine a vehicle position relative to the fixed environmental feature and determine a vehicle lane-level location on a digital map, based on the vehicle position relative to the fixed environmental feature.

Abstract: A computer includes a processor and a memory storing instructions executable by the processor to predict a time to failure of a vehicle component according to a damage model generated from vehicle operating data, based on the predicted time to failure, retrieve a schematic of the vehicle component and input the schematic to an additive printer to manufacture a replacement vehicle component, and replace the vehicle component with the replacement vehicle component according to maintenance instructions that, based at least in part on the predicted time to failure, specify a time and location to replace the vehicle component.

Abstract: An autonomous vehicle includes a display interface. The vehicle includes a controller configured to execute commands to transport a passenger from a pickup location to a destination and display a brand name or logo associated with a brand identifier during at least a portion of the transport. The execution is responsive to a request for transportation service including the brand identifier, the pickup location, and the destination.