Abstract: A mechanical joint configured for providing dissipation of heat generated is provided. The mechanical joint includes a housing containing a motor assembly configured to drive a gear assembly for driving the mechanical joint, the motor assembly configured to be controlled by a control assembly for controlling rotation of a rotor of the motor assembly; wherein a brake disk of the control assembly is configured to increase air flow within the housing. A robot and a robotic system are also disclosed.

Abstract: A method of vehicle navigation using terrain text recognition includes receiving, via an electronic controller arranged on a vehicle and having access to a map of the terrain, a navigation route through the terrain. The method also includes receiving, via the controller, a signal from a global positioning system (GPS) to determine a current position of the vehicle relative to the terrain. The method additionally includes determining, via the controller, a location of a next waypoint on the navigation route and relative to the current vehicle position. The method also includes detecting and communicating to the controller, via a vehicle sensor an image frame displaying a text indicative of the next waypoint and correlating, via the controller, the detected text to the next waypoint on the map. Furthermore, the method includes setting, via the controller, an in-vehicle alert indicative of the detected text having been correlated to the next waypoint.

Abstract: A method in a multiprocessor system for processing multiple perception streams is disclosed. The method comprises: reading data from a plurality of perception streams according to a reading schedule determined by a predetermined policy, each perception stream comprising perception data from a different perception sensor; assigning a unique identification tag to each perception stream; writing each perception stream with its unique identification tag to a server input queue based on the predetermined policy; and processing the tagged perception streams using a server. The processing includes: retrieving tagged perception streams from the server input queue; applying a processing algorithm to process the retrieved tagged perception streams; and outputting the processed perception streams to a server output queue.

Abstract: Presented are embedded control systems with logic for computation and data sharing, methods for making/using such systems, and vehicles with distributed sensors and embedded processing hardware for provisioning automated driving functionality. A method for operating embedded controllers connected with distributed sensors includes receiving a first data stream from a first sensor via a first embedded controller, and storing the first data stream with a first timestamp and data lifespan via a shared data buffer in a memory device. A second data stream is received from a second sensor via a second embedded controller. A timing impact of the second data stream is calculated based on the corresponding timestamp and data lifespan. Upon determining that the timing impact does not violate a timing constraint, the first data stream is purged from memory and the second data stream is stored with a second timestamp and data lifespan in the memory device.

Abstract: A comparing module receives first data regarding surroundings of a vehicle from a plurality of sensors in the vehicle, receives second data regarding the surroundings from the plurality of sensors after receiving the first data, compares the first data to the second data, and determines a first difference between the first data and the second data based on the comparison of the first data to the second data. A perception module generates a first set of perception results based on the first data, generates a second set of perception results based on the second data, and determines a second difference between the first data and the second data based on the first set of perception results and the second set of perception results. A diagnostics module determines whether one of the sensors or the perception module is faulty based on a combination of the first difference and the second difference.

Abstract: A perception system includes a perception module configured to capture first sensor data that includes data from at least one of an external sensor and a camera captured in a first period, a prediction module configured to receive the first sensor data, generate, based on the first sensor data, predicted sensor data for a second period subsequent to the first period, receive second sensor data for the second period, and output results of a comparison between the predicted sensor data and the second sensor data, and a diagnostic module configured to selectively identify a fault in the perception system based on the results of the comparison.

Abstract: Systems and methods to identify an attention region in sensor-based detection involve obtaining a detection result that indicates one or more detection areas where one or more objects of interest are detected. The detection result is based on using a first detection algorithm. The method includes obtaining a reference detection result that indicates one or more reference detection areas where one or more objects of interest are detected. The reference detection result is based on using a second detection algorithm. The method also includes identifying the attention region as one of the one or more reference detection areas without a corresponding one or more detection areas. The first detection algorithm is used to perform detection in the attention region.

Abstract: A method of on-line diagnostic and prognostic assessment of an autonomous vehicle perception system includes detecting, via a sensor, a physical parameter of an object external to the vehicle. The method also includes communicating data representing the physical parameter via the sensor to an electronic controller. The method additionally includes comparing the data from the sensor to data representing the physical parameter generated by a geo-source model. The method also includes comparing results generated by a perception software during analysis of the data from the sensor to labels representing the physical parameter from the geo-source model. Furthermore, the method includes generating a prognostic assessment of a ground truth for the physical parameter of the object using the comparisons of the sensor data to the geo-source model data and of the software results to the geo-source model labels. A system for on-line assessment of the vehicle perception system is also disclosed.

Abstract: Systems and methods are provided for controlling an autonomous vehicle (AV). A scene understanding module of a high-level controller selects a particular combination of sensorimotor primitive modules to be enabled and executed for a particular driving scenario from a plurality of sensorimotor primitive modules. Each one of the particular combination of the sensorimotor primitive modules addresses a sub-task in a sequence of sub-tasks that address a particular driving scenario. A primitive processor module executes the particular combination of the sensorimotor primitive modules such that each generates a vehicle trajectory and speed profile. An arbitration module selects one of the vehicle trajectory and speed profiles having the highest priority ranking for execution, and a vehicle control module processes the selected one of vehicle trajectory and speed profiles to generate control signals used to execute one or more control actions to automatically control the AV.

Abstract: A controlling method of a user interface and an electronic device are provided. A touch element includes a start area, a trigger area and a track area connecting the start area and the trigger area. The controlling method of the user interface includes following steps: entering a startup interface display mode according to the touch behavior performed on the touch element; generating continuous touch data in response to the touch behavior when the touch behavior moves from the start area to the track area and an animation trigger condition is satisfied, activating an animation mode according to the continuous touch data; and generating the continuous touch data in response to the touch behavior when the touch behavior moves from the start area to the track area and from the track area to the trigger area, and opening a user interface according to the continuous touch data.

Abstract: Systems and methods to generate an adversarial attack on a black box object detection algorithm of a sensor involve obtaining an initial training data set from the black box object detection algorithm. The black box object detection algorithm performs object detection on initial input data to provide black box object detection algorithm output that provides the initial training data set. A substitute model is trained with the initial training data set such that output from the substitute model replicates the black box object detection algorithm output that makes up the initial training data set. Details of operation of the black box object detection algorithm are unknown and details of operation of the substitute model are known. The substitute model is used to perform the adversarial attack. The adversarial attack refers to identifying adversarial input data for which the black box object detection algorithm will fail to perform accurate detection.

Abstract: A method of controlling a vehicle includes determining a current operating situation of the vehicle, and identifying a subset of a plurality of sensors of the vehicle needed to provide data to enable a vehicle control function for the current operating situation of the vehicle. A remainder of the plurality of sensors is disengaged to reduce electric energy usage by the vehicle while the vehicle is operating in the current operating situation of the vehicle. A sampling rate for the selected subset of sensors may be reduced to further reduce energy usage of the vehicle. Additionally, an energy reduction processing strategy may be implemented to reduce a processor frequency or a voltage of a computing device used to provide the vehicle control function to further reduce energy usage of the vehicle.

Abstract: A method in a multiprocessor system for processing multiple perception streams is disclosed. The method comprises: reading data from a plurality of perception streams according to a reading schedule determined by a predetermined policy, each perception stream comprising perception data from a different perception sensor; assigning a unique identification tag to each perception stream; writing each perception stream with its unique identification tag to a server input queue based on the predetermined policy; and processing the tagged perception streams using a server. The processing includes: retrieving tagged perception streams from the server input queue; applying a processing algorithm to process the retrieved tagged perception streams; and outputting the processed perception streams to a server output queue.

Abstract: A diagnostic system includes: a first feature extraction module configured to extract features present in an image captured by a camera of a vehicle; a second feature extraction module configured to extract features present in the image captured by the camera of the vehicle; a first feature matching module configured to match the image captured by the camera with first images stored in a database and to output one of the first images based on the matching; a second feature matching module configured to, based on a comparison of the one of the first images with the image captured by the camera, determine a score value that corresponds to closeness between the one of the first images and the image captured by the camera; and a fault module configured to selectively diagnose a fault based on the score value and output a fault indicator based on the diagnosis.

Abstract: Examples of techniques for using fixed-point quantization in deep neural networks are disclosed. 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 quantizing a fixed-point value based on an activation input and a synapse weight. The method further includes controlling the vehicle based at least in part on a result of performing the vehicle perception tasks.

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: There is provided in the present application use of a peptide comprising the amino acid sequence YEKLLDTEI (SEQ ID NO: 1) or a functional variant thereof, or a pharmaceutical composition comprising the peptide in the manufacture of a medicament for preventing, alleviating or treating pain, in particular nociceptive pain, and a method for preventing, alleviating or treating pain.

Abstract: Systems and methods for depth estimation of images from a mono-camera by use of radar data by: receiving, a plurality of input 2-D images from the mono-camera; generating, by the processing unit, an estimated depth image by supervised training of an image estimation model; generating, by the processing unit, a synthetic image from a first input image and a second input image from the mono-camera by applying an estimated transform pose; comparing, by the processing unit, an estimated three-dimensional (3-D) point cloud to radar data by applying another estimated transform pose to a 3-D point cloud wherein the 3-D point cloud is estimated from a depth image by supervised training of the image estimation model to radar distance and radar doppler measurement; correcting a depth estimation of the estimated depth image by losses derived from differences: of the synthetic image and original images; of an estimated depth image and a measured radar distance; and of an estimated doppler information and measured radar d

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 percpetion 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 method for image style transfer using a Semantic Preserved Generative Adversarial Network (SPGAN) includes: receiving a source image; inputting the source image into the SPGAN; extracting a source-semantic feature data from the source image; generating, by the first decoder, a first synthetic image including the source semantic content of the source image in a target style of a target image using the source-semantic feature data extracted by the first encoder of the first generator network, wherein the first synthetic image includes first-synthetic feature data; determining a first encoder loss using the source-semantic feature data and the first-synthetic feature data; discriminating the first synthetic image against the target image to determine a GAN loss; determining a total loss as a function of the first encoder loss and the first GAN loss; and training the first generator network and the first discriminator network.