Abstract: A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive first environmental data recorded by an environmental sensor on board a vehicle, receive nonenvironmental data recorded on board the vehicle independently of the first environmental data, add a plurality of annotations derived from the nonenvironmental data to the environmental data, and train a machine-learning program to process second environmental data by using the first environmental data as training data and the annotations as ground truth for the first environmental data.

Abstract: A transportation system is disclosed. The transportation system includes a vehicle, a plurality of seating assemblies positioned within a passenger compartment of the vehicle and defining an arrangement, a plurality of actuators that effect movement of various components of the plurality of seating assemblies, a plurality of sensors, and a controller. Specific examples of pre-set or pre-programmed arrangements are disclosed, as well as the ability to customize the arrangement. Additionally, exemplary methods are disclosed that illustrate transitions between a variety of the pre-set or pre-programmed arrangements.

Abstract: Sensor fusion is performed for efficient deep learning processing. A camera image is received from an image sensor and supplemental sensor data is received from one or more supplemental sensors, the camera image and the supplemental sensor data including imaging of a cabin of a vehicle. Regions of interest in the camera image are determined based on one or more of the camera image or the supplemental sensor data, the regions of interest including areas of the camera image flagged for further image analysis. A machine-learning model is utilized to perform object detection on the regions of interest of the camera image to identify one or more objects in the camera image. The objects are placed into seating zones of the vehicle.

Abstract: An acceleration of a head of an occupant of a vehicle relative to the vehicle is determined based on data describing motion of the vehicle and respective poses of the head from a plurality of consecutive frames of image data. A vehicle suspension is adjusted based on the determined acceleration.

Abstract: A two-dimensional image of a vehicle occupant in a vehicle is collected. The collected two-dimensional image is input to a machine learning program trained to output one or more reference points of the vehicle occupant, each reference point being a landmark of the vehicle occupant. One or more reference points of the vehicle occupant in the two-dimensional image is output from the machine learning program. A location of the vehicle occupant in an interior of the vehicle is determined based on the one or more reference points. A vehicle component is actuated based on the determined location. For each of the one or more reference points, a similarity measure is determined between the reference point and a three-dimensional reference point, the similarity measure based on a distance between the reference point and the three-dimensional reference point.

Abstract: Performing object localization inside a cabin of a vehicle is provided. A camera image is received of a cabin of a vehicle by an image sensor at a first location with respect to a plurality of seating zones of a vehicle. Object detection on the camera image is performed to identify one or more objects in the camera image. A machine-learning model trained on images taken at the first location is utilized to place the one or more objects into the seating zones of the vehicle according to a plurality of bounding boxes corresponding to the plurality of seating zones for the first location.

Abstract: A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive first environmental data recorded by an environmental sensor on board a vehicle, receive nonenvironmental data recorded on board the vehicle independently of the first environmental data, add a plurality of annotations derived from the nonenvironmental data to the environmental data, and train a machine-learning program to process second environmental data by using the first environmental data as training data and the annotations as ground truth for the first environmental data.

Abstract: A system receives vehicle sensor data indicating upcoming route characteristics as a vehicle travels a route. The system identifies one or more passibility-limiting features from the vehicle sensor data and, based on the sensor data, interpolates at least one characteristics of at least one feature that would affect vehicle travel. The system determines if the feature has been previously identified by comparison to previously identified features associated with locations within a proximity of a present location of the vehicle. Further, the system determines if the at least one characteristic has more than a predefined deviance from one or more previously interpolated characteristics associated with the feature and alert the user via the vehicle if at least one of the previously interpolated characteristics or the at least one characteristic indicates that the vehicle may have difficulty passing the feature based on predefined parameters of the vehicle.

Abstract: Method and system for providing a mobility service is described. The mobility service can include delivering goods in rough terrain, rural areas, and other similar environments, by selecting and configuring vehicles for terrain considerations based on known and dynamically changing information. The disclosed system can include a vehicle configured with a deployable autonomous drone, and can determine optimized vehicle routes to remote locations using the deployable drone and vehicle navigation sensors, for real time mapping that can be combined with existing map/terrain data. The terrain data may also be sent to a vehicle in a vehicle fleet, and/or to a cloud-based server, and be used to compute the best available vehicles designed for the mobility service. The system may deploy one or more mobility solutions, collect telematics, road information, navigational data, and other information during the delivery. This feedback may then be used for future mobility services.

Abstract: A vehicle system receives a request for a first vehicle micro-service to be executed on behalf of a first feature and to be executed having a configuration included in the request. The system determines whether the first micro-service with the configuration is already executing on behalf of a second feature. The system determines if vehicle resources will support an instance of the first micro-service with the configuration when the first micro-service with the configuration is not already executing and, responsive to determining that vehicle resources will support the instance, launches the requested first micro-service with the configuration. The system additionally registers the instance along with the configuration and any features on behalf of which the instance is functioning.

Abstract: A system receives a request to engage one or more vehicle micro-services on behalf of a vehicle feature. The system accesses a manifest associated with the vehicle feature, the manifest including the one or more micro-services and configurations to be associated with instances of the one or more micro-services launched on behalf of the vehicle feature. The system requests launch of each of the micro-services and the associated configurations from a vehicle process responsible for micro-service launch, to build a pipeline for the vehicle feature and translates results generated by at least one micro-service of the pipeline into a format predefined as suitable for use by the vehicle feature.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to acquire a first image from a first camera by illuminating a first object with a first light and determine an object status as one of a real object or a counterfeit object by comparing a first measure of pixel values corresponding to the first object to a threshold.

Abstract: The disclosure generally pertains to systems and methods for providing off-road track drivability guidance. In an example scenario, a processor receives a sensor signal that is generated when a vehicle is driven on an off-road track. The processor evaluates the sensor signal to obtain information about a driving surface of the off-road track such as, a material composition, a gradient, and/or an amount of provided traction. The processor conveys the information to another processor (which may be a part of a server computer) that determines a drivability status of the off-road track based on the information about the driving surface and additional parameters, such as present and/or future weather conditions. The server computer may provide the drivability status in response to a query from a processor that is located in another vehicle (or in a personal communication device carried by a driver of the vehicle).

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to determine a first measure of pixel values in the first image, acquire a second image, estimate ambient light illuminating the first object based on the second image and modify the first measure of pixel values based on a second measure of pixel values corresponding to estimated ambient light based on the second image. The instructions include further instructions to perform a comparison of the modified first measure of pixel values to a third measure of pixel values determined from a third image of a second object, wherein the third image is previously acquired by illuminating the second object with a second light beam and, when the comparison determines that the first measure is equal to the third measure of pixel values within a tolerance, determine whether the first object and the second object are a same object.

Abstract: The disclosure generally pertains to systems and methods for providing off-road track drivability guidance. In an example scenario, a processor receives a sensor signal that is generated when a vehicle is driven on an off-road track. The processor evaluates the sensor signal to obtain information about a driving surface of the off-road track such as, a material composition, a gradient, and/or an amount of provided traction. The processor conveys the information to another processor (which may be a part of a server computer) that determines a drivability status of the off-road track based on the information about the driving surface and additional parameters, such as present and/or future weather conditions. The server computer may provide the drivability status in response to a query from a processor that is located in another vehicle (or in a personal communication device carried by a driver of the vehicle).

Abstract: A help system is provided for a vehicle occupant who carries a mobile communication device. The vehicle has a transceiver providing two-way wireless telecommunication and which detects a loss-of-signal condition. A powertrain system provides vehicle movement and is configured for reckoning a path traversed by the vehicle. The help system includes a guidance controller and an interface adapted to connect to the mobile communication device. The guidance controller is configured to A) identify a crossing point when the transceiver detects an initiation of the loss-of-signal condition, B) store a breadcrumb route for returning to the crossing point based on the reckoned path, C) detect a stranded condition of the vehicle during the loss-of-signal condition, D) generate guidance instructions including the breadcrumb route from the vehicle back to the crossing point when the stranded condition is detected, and E) transfer the guidance instructions to the mobile communication device.

Abstract: Performing object localization inside a cabin of a vehicle is provided. A camera image is received of a cabin of a vehicle by an image sensor at a first location with respect to a plurality of seating zones of a vehicle. Object detection on the camera image is performed to identify one or more objects in the camera image. A machine-learning model trained on images taken at the first location is utilized to place the one or more objects into the seating zones of the vehicle according to a plurality of bounding boxes corresponding to the plurality of seating zones for the first location.

Abstract: An acceleration of a head of an occupant of a vehicle relative to the vehicle is determined based on data describing motion of the vehicle and respective poses of the head from a plurality of consecutive frames of image data. A vehicle suspension is adjusted based on the determined acceleration.

Abstract: Sensor fusion is performed for efficient deep learning processing. A camera image is received from an image sensor and supplemental sensor data is received from one or more supplemental sensors, the camera image and the supplemental sensor data including imaging of a cabin of a vehicle. Regions of interest in the camera image are determined based on one or more of the camera image or the supplemental sensor data, the regions of interest including areas of the camera image flagged for further image analysis. A machine-learning model is utilized to perform object detection on the regions of interest of the camera image to identify one or more objects in the camera image. The objects are placed into seating zones of the vehicle.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to acquire a first image from a first camera by illuminating a first object with a first light and determine an object status as one of a real object or a counterfeit object by comparing a first measure of pixel values corresponding to the first object to a threshold.