Abstract: A computing device may obtain one or more raw images captured from sensors of an autonomous driving vehicle (ADV). The computing device may apply one or more signal processing parameters to one or more raw images, resulting in one or more processed images. The computing device may apply an object detection algorithm of the ADV to the one or more processed images. The computing device may determine a score of the object detection algorithm as applied to the one or more processed images. One or more optimized signal processing parameters may be determined based on the score of the object detection algorithm and the signal processing parameter. The one or more optimized signal processing parameters may be used to configure the ADV for use during autonomous driving.

Abstract: In one embodiment, a system configures an image signal processor (ISP) of an autonomous driving vehicle (ADV) with a first set of ISP configuration parameters, where the ISP is used to process raw image data of an image sensor of the ADV based on the first set of ISP configuration parameters. The system determines whether one or more criteria is satisfied, where the one or more criteria corresponds to an expected change in a characteristic of ambient light being perceived by the image sensor of the ADV. In response to determining that the one or more criteria is satisfied, the system configures the ISP of the ADV with a second set of ISP configuration parameters, where the ISP is used to apply an image processing algorithm to raw image data based on the second set of ISP configuration parameters to generate an image.

Abstract: The disclosure describes an autotuning framework for tuning an image signal processing (ISP) module of an autonomous driving vehicle (ADV). The autotuning framework can generate different sets of parameter values using a variety of optimization algorithms to configure the ISP module. Each processed image generated by the ISP module configured with the different sets of parameter values is compared by an ISP module testing device with a reference image stored therein to generate an objective core measuring one or more differences between each of the processed images and the reference image. The objective scores and the corresponding sets of parameter values are stored in a database. Different sets of optimal parameter values for different environments can be selected from the database, and uploaded to a cloud database for use by an ADV, which can select a different set of ISP parameter values based on an environment that the ADV is travelling in.

Abstract: In one embodiment, a system generates an image using either a first or a second image signal processing (ISP) algorithm, where the first or second ISP algorithm is applied to raw image data of a camera of an autonomous driving vehicle (ADV) to generate the image. The system applies a machine learning model to the image to identify a representation of an obstacle, where the machine learning model is generated by a few shots learning algorithm that contrasts labeled data of a positive training sample from images corresponding to the first and second ISP algorithms to labeled data of a negative training sample from images corresponding to the first and second ISP algorithm. The system determines a classification and a location of the obstacle based on the representation of the obstacle. The system plans a motion control of the ADV based on the classification and location of the detected object.

Abstract: The invention of the present disclosure provides a method for treating a viral infection in a recipient subject suffering from or at risk of a viral infection including administering to the recipient subject a pharmaceutical composition comprising a therapeutic amount of superactivated cytokine killer T cells (SCKTCs) and a pharmaceutically acceptable carrier, and mobilizing an immune response of the recipient subject to the viral pathogen. When tested in vitro, the SCKTCs are characterized by a predominant production of TH1 dominant cytokines including IFN-?; an IFN-?:IL-4 ratio of at least 500:1; and at least 50% killing of target A549 cells at an effector:target ratio of 20:1.

Abstract: The present disclosure provides, in part, a priming and boosting vector-based platform to develop vaccines against viral pathogens that is tailored to elicit a broad T cell response targeting conserved viral epitopes while including helper T cell (TH) epitopes and an adjuvant to achieve a balanced immune response consisting of both cellular immunity, coupled with a broad neutralizing antibody response in the design of a candidate universal vaccine to HIV or a human coronavirus, e.g., SARS-CoV-2. The universal vaccines are prepared against an immunogen of an infectious pathogenic virus comprising at least one nucleic acid polynucleotide comprising an open reading frame encoding at least one polypeptide antigen or an immunogenic fragment thereof, wherein the polypeptide antigen, or the immunogenic fragment thereof, comprises a conserved internal protein that is enriched in T cell recognition antigens.