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.

Abstract: Described herein are systems, methods, and computer-readable media for generating and training a high precision low bit convolutional neural network (CNN). A filter of each convolutional layer of the CNN is approximated using one or more binary filters and a real-valued activation function is approximated using a linear combination of binary activations. More specifically, a non-1×1 filter (e.g., a k×k filter, where k>1) is approximated using a scaled binary filter and a 1×1 filter is approximated using a linear combination of binary filters. Thus, a different strategy is employed for approximating different weights (e.g., 1×1 filter vs. a non-1×1 filter). In this manner, convolutions performed in convolutional layer(s) of the high precision low bit CNN become binary convolutions that yield a lower computational cost while still maintaining a high performance (e.g., a high accuracy).

Abstract: A polycarbonate composition includes the following components in parts by weight: a. 30 to 87 parts of a PC resin; b. 8 to 50 parts of a rubber-modified graft polymer; c. 5 to 25 parts of a fire retardant; and d. 0 to 10 parts of other aids; wherein a sum of parts by weight of the four components a, b, c, and d is 100. Phenols which are added in a polycarbonate composition formula, based on a total weight of the polycarbonate composition does not exceed 100 ppm. A K value of a component I of the PC resin is adjusted between 300000 and 500000 and the K value of a component II is adjusted to be less than 300000.

Abstract: A system and method to perform dynamic bandwidth adjustment among two or more vehicle sensors includes receiving input. The input includes data from each of the two or more vehicle sensors. The two or more vehicle sensors include a camera, an audio detector, a radar system, or a lidar system. The method also includes determining a bandwidth at which each of the two or more vehicle sensors should provide the data, and providing a control signal to each of the two or more vehicle sensors to adjust the bandwidth based on the determining.

Abstract: Methods and apparatus are provided for controlling a vehicle. In one embodiment, a method includes: receiving, by a processor, object detection data that indicates a plurality of objects detected in an environment of the vehicle; computing, by the processor, a correction value associated with at least one of range, roll, and pitch of the plurality of objects based on a likelihood function; applying, by the processor, the correction value to the object detection data to obtain corrected object detection data; and controlling, by the processor, the vehicle based on the corrected object detection data.

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.

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: 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: One general aspect includes a memory configured to include a program and a processor configured to execute the program, where the program enables the processor to: after a first vehicle ingress/egress event, activate a sensor to capture a reference image of a portion of a vehicle body; after a second vehicle ingress/egress event, activate the sensor to capture a test image of the portion of the vehicle body; determine whether the test image includes one or more objects not found in the reference image; and release the door from the body or produce a notification or prevent vehicle movement or some combination thereof, based on the determination of whether the test image includes one or more objects not found in the reference image.

Abstract: Technical solutions are described for controlling an automated driving system of a vehicle. An example method includes computing a complexity metric of an upcoming region along a route that the vehicle is traveling along. The method further includes, in response to the complexity metric being below a predetermined low-complexity threshold, determining a trajectory for the vehicle to travel in the upcoming region using a computing system of the vehicle. Further, the method includes in response to the complexity metric being above a predetermined high-complexity threshold, instructing an external computing system to determine the trajectory for the vehicle to travel in the upcoming region. If the trajectory cannot be determined by the external computing system a minimal risk condition maneuver of the vehicle is performed.

Abstract: An autonomous vehicle (AV) perception system and method of determining an autonomous vehicle (AV) action for a host vehicle. The method includes: obtaining onboard vehicle sensor data from at least one onboard vehicle sensor, the onboard vehicle sensor is a part of vehicle electronics of the host vehicle; obtaining edge sensor data from at least one edge sensor, the edge sensor is a part of an edge layer; generating a unified perception output based on the onboard vehicle sensor data and the edge sensor data; determining an AV action for the host vehicle based on the unified perception output; and providing the AV action to the host vehicle, wherein the host vehicle is configured to carry out the AV action.

Abstract: A system and method for generating a range image using sparse depth data is disclosed. The method includes receiving, by a controller, image data of a scene. The image data includes a first set of pixels. The method also includes receiving, by the controller, a sparse depth data of the scene. The sparse depth data includes a second set of pixels, and the number of the second set of pixels is less than the number of first set of pixels. The method also includes combining the image data and the sparse depth data into a combined data. The method also includes generating a range image using the combined data.

Abstract: A method and apparatus for updating an application are disclosed. According to the method, an update tag corresponding to a target application is pushed to a terminal when determining that the target application is required to be updated, the target application being an application downloaded and installed in the background of the terminal. An acquisition request sent by the terminal based on the update tag is received, the acquisition request being used for requesting to acquire target update content corresponding to the target application. The target update content is pushed to the terminal based on the update tag, such that the terminal opens the target application of a latest version by loading the target update content.

Abstract: A polycarbonate composition includes the following components in parts by weight: a. 30 to 86 parts of polycarbonate; b. 0.0001 to 1 part of phenols; c. 0.01 to 5 parts of hindered phenol substance; d. 8 to 50 parts of a rubber-modified graft polymer; e. 5 to 25 parts of a fire retardant; and f. 0 to 10 parts of a fire retardant synergist; wherein a sum of parts by weight of the six components a, b, c, d, e and f is 100.

Abstract: An apparatus and method that detect a wheel alignment condition are provided. The method includes receiving a dataset comprising one or more from among a steering wheel angle parameter, a speed parameter, a lateral acceleration parameter, a self-aligning torque parameter and a power steering torque parameter, normalizing the received dataset, analyzing the normalized dataset according to a model for determining a wheel alignment condition, and outputting a value indicating whether the wheel alignment is within a predetermined value based on the model.