Abstract: An automobile vehicle continuous validation system includes a backend collecting data from a vehicle fleet and wirelessly communicating with the vehicle fleet. The backend is in wireless communication with at least one client. A vehicle module is provided on-board individual ones of multiple automobile vehicles of the vehicle fleet and performing an on-board vehicle validation analysis. A fleet-based validation module provided either at the backend or cloud based manages data defining a configuration of and a capability of the multiple automobile vehicles of the vehicle fleet. A validation manager generates validation tasks based on a user's definition or a desired production of the validation tasks of the validation analysis and a fleet vehicle availability. A client-side module remote from the multiple automobile vehicles of the vehicle fleet has interface items applied by the at least one client seeking to perform the validation analysis.

Abstract: A virtual LiDAR sensor system fora motor vehicle includes a plurality of camera modules and algorithms that generate a depth image, a RGB image, and a segmentation information image. The system is implemented with an algorithm that associates the backscattered signals with information from a color-reflectivity table, incident angle determination and depth information.

Abstract: A method for facilitating uses of a code for vehicle experiences includes receiving code data from the code in a sign. The sign is disposed outside a host vehicle. The method further includes establishing a connection with a service using the code data obtained from the code and in response to establishing the connection with the service, receiving service data. The service data includes information about the service. The method further includes, in response to receiving the service data, controlling the host vehicle according to the service data received after establishing the connection with the service.

Abstract: A perception processing system includes a memory and a main controller. The main controller includes modules and implements a data processing pipeline including algorithm stages, which are executed in parallel relative to sets of data and are executed sequentially relative to each of the sets of data. The algorithm stages share resources of the modules and the memory to process the sets of data and generate perception information. One of the modules executes global and local controllers. The global controller sets a processing rate for the local controllers. The local controllers monitor current processing rates of the algorithm stages. When one of the current processing rates is less than the set processing rate, the corresponding one of the local controllers sends a first signal to the global controller and in response the global controller sends a broadcast signal to the local controllers to adjust the current processing rates.

Abstract: The present application relates to a method and apparatus for generating a graphical user interface indicative of a vehicle underbody view including a LIDAR operative to generate a depth map of an off-road surface, a camera for capturing an image of the off-road surface, a chassis sensor operative to detect an orientation of a host vehicle, a processor operative to generate an augmented image in response to the depth map, the image and the orientation, wherein the augmented image depicts an underbody view of the host vehicle and a graphic representative of a host vehicle suspension system, and a display operative to display the augmented image to a host vehicle operator. A static and dynamic model of the vehicle underbody is compared vs the 3-D terrain model to identify contact points between the underbody and terrain are highlighted.

Abstract: A system includes first and second sensors and a controller. The first sensor is of a first type and is configured to sense objects around a vehicle and to capture first data about the objects in a frame. The second sensor is of a second type and is configured to sense the objects around the vehicle and to capture second data about the objects in the frame. The controller is configured to down-sample the first and second data to generate down-sampled first and second data having a lower resolution than the first and second data. The controller is configured to identify a first set of the objects by processing the down-sampled first and second data having the lower resolution. The controller is configured to identify a second set of the objects by selectively processing the first and second data from the frame.

Abstract: In an exemplary embodiment, a LiDAR is provided that is configured for installation in a mobile platform. The LiDAR includes a scanner and a light-intensity receiver. The scanner includes a light source configured to direct illumination in an illuminating direction. The light-intensity receiver includes one or more light-intensity sensors; and one or more lens assemblies configured with respect to the one or more light-intensity sensors, such that that at least one sensor plane from the one or more light-intensity sensors is tilted to form a non-zero angle with at least one equivalent lens plane from the one or more lens assemblies, transferring the sensor focal plane to be align with the main light illumination direction and be consistent with the direction of movement of a mobile platform.

Abstract: A system for an attention-based perception includes a camera device configured to provide an image of an operating environment of a vehicle. The system further includes a computerized device monitoring the image, analyzing sensor data to identify a feature in the image as corresponding to an object in the operating environment and assign a score for the feature based upon an identification, a location, or a behavior of the object. The computerized device is further operable to define candidate regions of interest upon the image, correlate the score for the feature to the candidate regions of interest to accrue a total region score, select some of the candidate regions for analysis based upon the total region scores, and analyze the portion of the candidate regions to generate a path of travel output. The system further includes a device controlling the vehicle based upon the output.

Abstract: A perception system is adapted to receive visual data from a camera and includes a controller having a processor and tangible, non-transitory memory on which instructions are recorded. A subsampling module, an object detection module and an attention module are each selectively executable by the controller. The controller is configured to sample an input image from the visual data to generate a rescaled whole image frame, via the subsampling module. The controller is configured to extract feature data from the rescaled whole image frame, via the object detection module. A region of interest in the rescaled whole image frame is identified, based on an output of the attention module. The controller is configured to generate a first image based on the rescaled whole image frame and a second image based on the region of interest, the second image having a higher resolution than the first image.

Abstract: A latency masking system for use in an autonomous vehicle (AV) system includes a sensors module providing sensor data from a plurality of sensors. The sensor data includes image frames provided by a vehicle camera and vehicle motion data. A wireless transceiver transmits the sensor data to a remote server associated with a network infrastructure and receives remote state information derived from the sensor data. An on-board function module receives the sensor data from the sensors module and generates local state information. A state fusion and prediction module receives the remote station information and the local state information and updates the local state information with the remote state information. The state fusion and prediction module uses checkpoints in a state history data structure to update the local state information with the remote state information.

Abstract: A vehicle system includes a lidar system that obtains an initial point cloud and obtains a dual density point cloud by implementing a first neural network and based on the initial point cloud. The dual density point cloud results from reducing point density of the initial point cloud outside a region of interest (ROI). Processing the dual density point cloud results in a detection result that indicates any objects in a field of view (FOV) of the lidar system. A controller obtains the detection result from the lidar system and controls an operation of the vehicle based on the detection result.

Abstract: A vehicle communication and control system includes a servicing host capable of exchanging data with a vehicle. The servicing host provides a vehicle service and includes a service identifier (ID) that indicates the vehicle service. The vehicle is configured to actively detect the service ID and to determine the vehicle service in response to detecting the service ID. The vehicle and the servicing host establish a wireless connection to exchange data and automatically initiate the vehicle service in response to detecting the service ID.

Abstract: An electric powertrain includes a sensor-controller interface, an inverter-controller electrically connected to the battery pack, and an electric machine connected to the inverter-controller and having a rotor with an angular position. The rotor powers a driven load at a torque and/or speed level controlled by the inverter-controller in response to position signals indicative of the angular position. A rotary position sensor is operatively connected to the rotor to generate and output the position signals. The sensor derives the position signals from unmodulated sine and cosine signals, and communicates the position signals and a binary sensor state of health (SOH) to the inverter-controller over the interface. The inverter-controller also decodes the position signals and the binary sensor SOH to generate decoded control data, and controls the torque and/or speed level using the decoded control data.

Abstract: An attention-driven streaming system includes an adaptive-streaming module and a transceiver. The adaptive-streaming module includes filters, a compression module, an attention-driven strategy module and a fusion module. The filters filter sensor data received from sensors of a vehicle. The compression module compresses the filtered sensor data to generate compressed data. The attention-driven strategy module generates feedforward information based on a state of the vehicle and a state of an environment of the vehicle to adjust a region of interest. The fusion module generates an adaptive streaming strategy to adaptively adjust operations of each of the filters. The transceiver streams the compressed data to at least one of an edge computing device or a cloud-based network device and in response receive feedback information and pipeline monitoring information.

Abstract: A latency masking system for use in an autonomous vehicle (AV) system. The latency masking system comprises a sensors module providing sensor data from a plurality of sensors. The sensor data includes image frames provided by a vehicle camera and vehicle motion data. A wireless transceiver transmits the sensor data to a remote server associated with a network infrastructure and receives remote state information derived from the sensor data. An on-board function module receives the sensor data from the sensors module and generates local state information. A state fusion and prediction module receives the remote station information and the local state information and updates the local state information with the remote state information. The state fusion and prediction module uses checkpoints in a state history data structure to update the local state information with the remote state information.

Abstract: A vehicle communication and control system includes a servicing host capable of exchanging data with a vehicle. The servicing host provides a vehicle service and includes a service identifier (ID) that indicates the vehicle service. The vehicle is configured to actively detect the service ID and to determine the vehicle service in response to detecting the service ID. The vehicle and the servicing host establish a wireless connection to exchange data and automatically initiate the vehicle service in response to detecting the service ID.

Abstract: A system includes a queue, a memory and a controller. The queue is configured to transfer a message between a first thread and a second thread, where the first thread and the second thread are implemented as part of a single process, and where an amount of data corresponding to the message is less than a set amount of data. The memory is configured for sharing data between the first thread and the second thread, wherein an amount of the data shared between the first thread and the second thread is greater than the set amount of data. The controller is configured to execute the single process including concurrently executing (i) a first middleware node process as the first thread, and (ii) a second middleware node process as the second thread.

Abstract: A vehicle includes a body supporting at least one camera. The at least one camera is positioned to collect images of objects outside of the vehicle. The vehicle also includes a selectively operable vehicle system and a controller operatively connected to the at least one camera and the selectively operable vehicle system. The controller includes a gesture recognition system operable to process a gesture made by a person associated with the vehicle collected by the at least one camera and activate the selectively operable vehicle system associated with the gesture.

Abstract: A signal processing system includes a central processing unit (CPU) in communication with an accelerator, and an instruction scheduler in communication with the accelerator. A first memory device including a first instruction set is configured to operate the accelerator, a second instruction set is configured to operate the CPU, and a second memory device is configured to receive a datafile. The accelerator includes a plurality of processing engines (PEs) and an instruction scheduler, the instruction set includes a plurality of operators, and the instruction scheduler is configured to implement the operators in the accelerator employing the PEs. The CPU employs the operators implemented in the accelerator to analyze the datafile to extract a feature therefrom.

Abstract: A system includes first and second sensors and a controller. The first sensor is of a first type and is configured to sense objects around a vehicle and to capture first data about the objects in a frame. The second sensor is of a second type and is configured to sense the objects around the vehicle and to capture second data about the objects in the frame. The controller is configured to down-sample the first and second data to generate down-sampled first and second data having a lower resolution than the first and second data. The controller is configured to identify a first set of the objects by processing the down-sampled first and second data having the lower resolution. The controller is configured to identify a second set of the objects by selectively processing the first and second data from the frame.