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 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 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 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.

Abstract: Methods and systems to implement sensor fusion to determine collision potential for a vehicle include identifying a specific intersection that the vehicle is approaching, and identifying collision potential scenarios associated with one or more paths through the specific intersection. Each collision potential scenario defines a risk of a collision between the vehicle and an object in a specified area. A weight with which one or more information sources of the vehicle are considered is adjusted for each collision potential scenario such that a highest weight is given to one or more of the one or more information sources that provide most relevant and reliable information about the specified area. Sensor fusion is implemented based on the adjusting the weight of the one or more information sources and performing detection based on the sensor fusion, and an alert is provided or actions are implemented according to the detection.

Abstract: Methods and systems are provided for detecting objects within an environment of a vehicle. In one embodiment, a method includes: receiving, by a processor, image data sensed from the environment of the vehicle; determining, by a processor, an area within the image data that object identification is uncertain; controlling, by the processor, a position of a lighting device to illuminate a location in the environment of the vehicle, wherein the location is associated with the area; controlling, by the processor, a position of one or more sensors to obtain sensor data from the location of the environment of the vehicle while the lighting device is illuminating the location; identifying, by the processor, one or more objects from the sensor data; and controlling, by the processor, the vehicle based on the one or more objects.

Abstract: In one example implementation according to aspects of the present disclosure, a computer-implemented method includes capturing a plurality of images at a camera associated with a vehicle and storing image data associated with the plurality of images to a memory. The method further includes dispatching vehicle perception tasks to a plurality of processing elements of an accelerator in communication with the memory. The method further includes performing, by at least one of the plurality of processing elements, the vehicle perception tasks for the vehicle perception using a neural network, wherein performing the vehicle perception tasks comprises performing an activation bypass for values below a first threshold, and performing weight pruning of synapses and neurons of the neural network based at least in part on a second threshold. The method further includes controlling the vehicle based at least in part on a result of performing the vehicle perception tasks.

Abstract: The vehicle mounted perception sensor gathers environment perception data from a scene using first and second heterogeneous (different modality) sensors, at least one of the heterogeneous sensors is directable to a predetermined region of interest. A perception processor receives the environment perception data and performs object recognition to identify objects each with a computed confidence score. The processor assesses the confidence score vis-à-vis a predetermined threshold, and based on that assessment, generates an attention signal to redirect the one of the heterogeneous sensors to a region of interest identified by the other heterogeneous sensor. In this way information from one sensor primes the other sensor to increase accuracy and provide deeper knowledge about the scene and thus do a better job of object tracking in vehicular applications.

Abstract: A risk maneuver assessment system and method to generate a perception of an environment of a vehicle and a behavior decision making model for the vehicle; a sensor system configured to provide the sensor input in the environment for filtering target objects; one or more modules configured to map and track target objects to make a candidate detection from multiple candidate detections of a true candidate detection as the tracked target object; apply a Markov Random Field (MRF) algorithm for recognizing a current situation of the vehicle and predict a risk of executing a planned vehicle maneuver at the true detection of the dynamically tracked target; apply mapping functions to sensed data of the environment for configuring a machine learning model of decision making behavior of the vehicle; and apply adaptive threshold to cells of an occupancy grid for representing an area of tracking of objects within the vehicle environment.

Abstract: A system and method to resolve ambiguity in a radar system involve detecting one or more objects with the radar system. The detecting includes obtaining range, azimuth, and an ambiguous range rate of a first object of the one or more objects. A plurality of Kalman filters are generated with state variables that include parameters based on the range, the azimuth, and the ambiguous range rate. Each of the plurality of Kalman filters provides a different estimate for an unambiguous range rate. The method also includes updating the plurality of Kalman filters using additional detections by the radar system, selecting a selected Kalman filter from among the plurality of Kalman filters that exhibits a highest probability mass among a plurality of probability mass corresponding with and derived from the plurality of Kalman filters, and determining the unambiguous range rate of the object using the selected Kalman filter.

Abstract: Methods and systems to implement sensor fusion to determine collision potential for a vehicle include identifying a specific intersection that the vehicle is approaching, and identifying collision potential scenarios associated with one or more paths through the specific intersection. Each collision potential scenario defines a risk of a collision between the vehicle and an object in a specified area. A weight with which one or more information sources of the vehicle are considered is adjusted for each collision potential scenario such that a highest weight is given to one or more of the one or more information sources that provide most relevant and reliable information about the specified area. Sensor fusion is implemented based on the adjusting the weight of the one or more information sources and performing detection based on the sensor fusion, and an alert is provided or actions are implemented according to the detection.

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: 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.