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: 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 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 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, including a processor and a memory, the memory including instructions to be executed by the processor to determine camera calibration parameters including camera focal distance, camera height and camera tilt based on a two-dimensional calibration pattern located parallel to and coincident with a ground plane determined by a roadway surface and determine object data including object location, object speed and object direction for one or more objects in a field of view of the video camera.

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 reference pose of an object in a coordinate system of a map of an area is determined. The reference pose is based on a three-dimensional (3D) reference model representing the object. A first pose of the object is determined as the object moves with respect to the coordinate system. The first pose is determined based on the reference pose and sensor data collected by the sensor at a first time. A second pose of the object is determined as the object continues to move with respect to the coordinate system. The second pose is determined based on the reference pose, the first pose, and sensor data collected by the sensor at a second time consecutive to the first time.

Abstract: A system, including a processor and a memory, the memory including instructions to be executed by the processor to identify first object features from sensor data acquired by a stationary sensor at a first time step, determine second object features at a second time step. The instructions can include further instructions to determine one or more object clusters of first object features by determining distances measured in pixels between the first object features and corresponding second object features and comparing the distances to one or more mean distances and determine one or more object groups of inlier first object features in the one or more object clusters by determining a plurality of similarity transformations for a plurality of random samples of first object features and determining inlier first object features based on maximizing the number of first object features included in a similarity transformation.

Abstract: A system, including a processor and a memory, the memory including instructions to be executed by the processor to determine camera calibration parameters including camera focal distance, camera height and camera tilt based on a two-dimensional calibration pattern located parallel to and coincident with a ground plane determined by a roadway surface and determine object data including object location, object speed and object direction for one or more objects in a field of view of the video camera.

Abstract: A system, including a processor and a memory, the memory including instructions to be executed by the processor to identify first object features from sensor data acquired by a stationary sensor at a first time step, determine second object features at a second time step.

Abstract: A reference pose graph can be generated from first point cloud data collected at each of a plurality of mobile nodes. A location of a stationary node can be specified based on the reference pose graph and second point cloud data collected by the stationary node.

Abstract: An infrastructure component includes a sensor, a transmitter, and a computer communicatively coupled to the sensor and to the transmitter. The computer is programmed to, in response to receiving data from the sensor indicating an object approaching a predesignated zone, determine an estimated time until the object reaches the predesignated zone; and then instruct the transmitter to broadcast a message to nearby vehicles, the message including an identifier of the predesignated zone and the estimated time.

Abstract: A reference pose of an object in a coordinate system of a map of an area is determined. The reference pose is based on a three-dimensional (3D) reference model representing the object. A first pose of the object is determined as the object moves with respect to the coordinate system. The first pose is determined based on the reference pose and sensor data collected by the sensor at a first time. A second pose of the object is determined as the object continues to move with respect to the coordinate system. The second pose is determined based on the reference pose, the first pose, and sensor data collected by the sensor at a second time consecutive to the first time.

Abstract: A system includes a processor and a memory. The memory stores instructions executable by the processor to determine a plurality of waypoints for mapping an area by analyzing a received aerial image of the area, to select an optimal number of the waypoints for mapping, and to actuate the vehicle to traverse a route determined to cover the selected waypoints.

Abstract: A reference pose graph can be generated from first point cloud data collected at each of a plurality of mobile nodes. A location of a stationary node can be specified based on the reference pose graph and second point cloud data collected by the stationary node.

Abstract: A system includes a processor and a memory. The memory stores instructions executable by the processor to determine a plurality of waypoints for mapping an area by analyzing a received aerial image of the area, to select an optimal number of the waypoints for mapping, and to actuate the vehicle to traverse a route determined to cover the selected waypoints.