Abstract: A system included and a computer-implemented method performed in an autonomous-driving vehicle are described. The system performs: detecting a wireless push signal transmitted from a signal transmitter accompanied by an off-vehicle passer and received by a signal receiver of the autonomous-driving vehicle, the wireless push signal including information about a motion capability level of the off-vehicle passer, determining a position and a motion capability level of the off-vehicle passer at least based on the wireless push signal, and controlling a locomotive mechanism of the autonomous-driving vehicle based on the determined position and motion capability level of the off-vehicle passer.

Abstract: Systems and methods directed to projecting a current trajectory path of the autonomous vehicle on a surface of road is disclosed. In some embodiments, an autonomous vehicle with a light projector is disclosed, where the light projector is on a top surface of an autonomous vehicle. Additionally, in some embodiments, the autonomous vehicle may include an electronic control unit for controlling an operation of the light projector, where the electronic control unit detects whether the autonomous vehicle is turned on. In further embodiments, the electronic control unit receives data of an environmental condition surrounding the autonomous vehicle and receives an upcoming trajectory path of the autonomous vehicle. The electronic control unit may also project a light from the light projector onto a surface of a road indicating the upcoming trajectory path of the autonomous vehicle.

Abstract: A computer implemented method for operating a wiper system of a sensor enclosure. The wiper system can be configured to receive a camera operating information from one or more cameras. The one or more cameras can be determined to be in an exposure mode based on the camera operating information. A first speed of one or more wipers can be adjusted such that the one or more wipers are not in field of views of the one or more cameras while the one or more cameras are in the exposure mode.

Abstract: A system included and a computer-implemented method performed in an autonomous-driving vehicle are described. The system performs: detecting a wireless push signal transmitted from a signal transmitter accompanied by an off-vehicle passer and received by a signal receiver of the autonomous-driving vehicle, the wireless push signal including information about a motion capability level of the off-vehicle passer, determining a position and a motion capability level of the off-vehicle passer at least based on the wireless push signal, and controlling a locomotive mechanism of the autonomous-driving vehicle based on the determined position and motion capability level of the off-vehicle passer.

Abstract: Systems, methods, and non-transitory computer readable media may be configured to characterize optical characteristics of optical elements. An optical element mount may be configured to carry an optical element. A calibration display may be configured to display a calibration object. The calibration object may include a known visual pattern. Multiple images of the calibration object may be obtained. The multiple images may be acquired using the optical element carried by the optical element mount. The multiple images may include different perspectives of the calibration object. Optical characteristics of the optical element may be characterized based on the known visual pattern and the different perspectives of the calibration object.

Abstract: An object recognition system is provided that includes a device configured to capture a video sequence formed from unlabeled testing video frames. The system includes a processor configured to pre-train a recognition engine formed from a reference set of CNNs on a still image domain that includes labeled training still image frames. The processor adapts the recognition engine to a video domain to form an adapted recognition engine, by applying a non-reference set of CNNs to a set of domains that include the still image and video domains and a degraded image domain. The degraded image domain includes labeled synthetically degraded versions of the labeled training still image frames included in the still image domain. The video domain includes random unlabeled training video frames. The processor recognizes, using the adapted engine, a set of objects in the video sequence. A display device displays the set of recognized objects.

Abstract: A system included and a computer-implemented method performed in an autonomous-driving vehicle are described. The system performs: detecting one or more movable traffic objects; determining one or more target movable traffic objects from the one or more detected movable traffic objects; determining a type of the one or more target movable traffic objects and an traffic object that has a right of way (ROW) in a situation involving the autonomous-driving vehicle. The system further performs: determining a manner of generating a vehicle behavior notification to the target movable traffic object based on the type of the one or more target movable traffic objects and the ROW; and causing a vehicle behavior notification of the determined manner to be generated to the one or more target movable traffic objects.

Abstract: A set of sensor information may include first sensor information generated based on a first sensor of a first vehicle and second sensor information generated based on a second sensor of a second vehicle. Individual sensor information may characterize positions of objects in an environment of individual sensors. Relevant sensor information for a vehicle may be determined based on the set of sensor information and a position of the vehicle. The relevant sensor information may characterize positions of objects in a maneuver environment of the vehicle. A desired navigation of the vehicle in the maneuver environment of the vehicle may be determined based on the relevant sensor information. An instruction may be provided to the vehicle based on the desired navigation of the vehicle. The instruction may characterize one or more maneuvers to be performed by the vehicle to execute the desired navigation.

Abstract: Systems, methods, and non-transitory computer-readable media are provided for implementing a preemptive control for an autonomous vehicle to improve ride quality. Data from one or more sensors onboard the autonomous vehicle can be acquired. A surface imperfection of a road can be identified from the data. A next action for the autonomous vehicle can be determined based on the surface imperfection. A signal can be outputted that causes the autonomous vehicle to act in accordance with the next action.

Abstract: Example packet control methods and apparatus are described. One example method includes detecting a packet flow causing a congestion status change. A congestion isolation message is generated and is used to change a priority of a packet in the packet flow. The congestion isolation message includes description information of the packet flow. The congestion isolation message is sent to at least one node.

Abstract: An access control system includes a processor configured to provide a trusted execution environment isolated from a rich execution environment. A rich OS operates in the rich execution environment while a trusted OS operates in the trusted execution environment. A plurality of protected data files are stored in non-volatile memory. When a process requests access to a protected data file, the computer system can permit the requesting process to access the requested data file only if a validated application token is present that corresponds to the requesting process. An application token is generated for the associated application by: detecting initiation of a first process associated with the associated application; determining that a valid user code is available within the trusted execution environment; and generating the application token using the valid user code upon determining that the valid user code is available within the trusted execution environment.

Abstract: Systems, methods, and non-transitory computer readable media may be configured to calibrate sensor measurements based on detection of brake light. Acceleration information of a first vehicle may be obtained. The acceleration information may define an acceleration probability distribution of the first vehicle. Image information may be obtained. The image information may define an image of the first vehicle. Whether a brake light of the first vehicle is on or off may be determined based on the image of the first vehicle. Based on a determination that the brake light of the first vehicle is on, a calibrated acceleration probability distribution of the first vehicle may be generated based on the acceleration probability distribution of the first vehicle and a braking-calibration curve.

Abstract: A system is provided for pose-variant 3D facial attribute generation. A first stage has a hardware processor based 3D regression network for directly generating a space position map for a 3D shape and a camera perspective matrix from a single input image of a face and further having a rendering layer for rendering a partial texture map of the single input image based on the space position map and the camera perspective matrix. A second stage has a hardware processor based two-part stacked Generative Adversarial Network (GAN) including a Texture Completion GAN (TC-GAN) stacked with a 3D Attribute generation GAN (3DA-GAN). The TC-GAN completes the partial texture map to form a complete texture map based on the partial texture map and the space position map. The 3DA-GAN generates a target facial attribute for the single input image based on the complete texture map and the space position map.

Abstract: A system included and a computer-implemented method performed in an autonomous-driving vehicle are described. The system performs: determining a region of interest (RoI) for processing images for an autonomous driving operation; determining an illumination condition for illuminating the determined RoI based on the determined RoI; and illuminating the determined RoI according to the determined illumination condition as the autonomous-driving vehicle travels.

Abstract: A computer implemented method for controlling camera exposure to augment a wiper system of a sensor enclosure. The computer implemented method can detect a presence of a wiper in one or more images captured by one or more cameras. An exposure time of the one or more cameras can be adjusted. Wiper speed can be adjusted such that wipers move in and out of one or more field of views of the one or more cameras while the one or more cameras are capturing images.

Abstract: A computer implemented method for operating a wiper system of a sensor enclosure. The wiper system can be configured to receive a camera operating information from one or more cameras. The one or more cameras can be determined to be in an exposure mode based on the camera operating information. A first speed of one or more wipers can be adjusted such that the one or more wipers are not in field of views of the one or more cameras while the one or more cameras are in the exposure mode.

Abstract: A sectional packaging box for packaging a product includes two side panels and two bendable end connectors. The two side panels each define a notch and are oppositely arranged. The two bendable end connectors each define two long slots and are oppositely arranged. The two side panels and the two end connectors cooperatively form an enclosed space. The two side panels latch within the long slots, and the two end connectors. An end of the two end connectors latch within the notches.

Abstract: A computer implemented method for operating a wiper system of a sensor enclosure. The wiper system can be configured to receive a camera operating information from one or more cameras. The one or more cameras can be determined to be in an exposure mode based on the camera operating information. A first speed of one or more wipers can be adjusted such that the one or more wipers are not in field of views of the one or more cameras while the one or more cameras are in the exposure mode.

Abstract: A surveillance system is provided that includes a device configured to capture a video sequence, formed from a set of unlabeled testing video frames, of a target area. The surveillance system further includes a processor configured to pre-train a recognition engine formed from a reference set of CNNs on a still image domain that includes labeled training still image frames. The processor adapts the recognition engine to a video domain to form an adapted recognition engine, by applying a non-reference set of CNNs to domains including the still image and video domains and a degraded image domain. The degraded image domain includes labeled synthetically degraded versions of the frames included in the still image domain. The video domain includes random unlabeled training video frames. The processor recognizes, using the adapted engine, at least one object in the target area. A display device displays the recognized objects.

Abstract: A system included and a computer-implemented method performed in an autonomous-driving vehicle are described. The system performs: detecting one or more movable traffic objects; determining one or more target movable traffic objects from the one or more detected movable traffic objects; determining a type of the one or more target movable traffic objects and an traffic object that has a right of way (ROW) in a situation involving the autonomous-driving vehicle. The system further performs: determining a manner of generating a vehicle behavior notification to the target movable traffic object based on the type of the one or more target movable traffic objects and the ROW; and causing a vehicle behavior notification of the determined manner to be generated to the one or more target movable traffic objects.