Abstract: An image encoding apparatus includes: at least one processor configured to implement: a text adaptation module configured to generate relevance information indicating a relevance between an image feature and a text feature based on text caption information corresponding to an original image; and an encoding module configured to generate a latent representation which represents the text caption information and the image feature using the relevance information and the original image, wherein the text adaptation module is further configured to communicate with the encoding module while generating the relevance information.

Abstract: An image encoding apparatus may include a memory storing one or more instructions; and a processor configured to execute the one or more instructions to: obtain an initial search position from a student neural network that has been trained through a knowledge distillation from a teacher neural network by inputting a current frame and a reference frame to the student neural network; and perform motion estimation based on the initial search position.

Abstract: A video encoder is provided. The video encoder according to an example embodiment includes: a differentiable prediction (DP) module configured to output an optimal initial search position by performing full search in a predetermined area by using a pair of frames of a video as input; and a motion estimation (ME) module configured to perform motion estimation by moving a search position toward the optimal initial search position output by the DP module.

Abstract: There is provide an image encoding method including transforming an image block into a first latent representation based on an invertible neural network, transforming the first latent representation into a second latent representation based on a non-invertible neural network, estimating a probability distribution of the first latent representation, and performing entropy encoding on the first latent representation based on the probability distribution by using an entropy encoder.

Abstract: The present invention provides a surface acoustic wave filter with a mass addition film formed on a plurality of electrodes. The surface acoustic wave filter includes a substrate on which a support substrate, an energy confinement layer, and a piezoelectric layer are sequentially stacked; first and second bus bars extended in a first direction on the substrate and spaced apart from each other in a second direction perpendicular to the first direction; a plurality of IDT electrodes alternately extended from the first and second bus bars in the second direction and spaced apart from each other in the first direction; and a mass addition film extended in the first direction to cover the top surface of the plurality of IDT electrodes and the top surface of the substrate exposed between the plurality of IDT electrodes.

Abstract: A method of generating a second neural network model according to an example embodiment includes: inputting unlabeled input data to a first neural network model; obtaining prediction results corresponding to the unlabeled input data based on the first neural network model; and generating a second neural network model based on the prediction results of the first neural network model and a degree of distribution of the prediction results.

Abstract: A surface acoustic wave device according to the present disclosure includes a support substrate; an intermediate layer laminated on the support substrate; a piezoelectric layer laminated on the intermediate layer; and an IDT electrode formed on the piezoelectric layer, an Euler angle of the support substrate is (?45°±10°, ?54°±10°, 180°±30°) and an Euler angle of the piezoelectric layer is (0°±5°, 112.5°±22.5°, 0°±5°) or (0°±5°, ?67.5°±22.5°, 0°±5°).

Abstract: Provided is a method and apparatus for determining a path of a vehicle. The method includes receiving a radio frequency (RF) signal among RF signals predefined corresponding to other vehicles in front of the vehicle, and determining a path of the vehicle based on the received RF signal.

Abstract: A road line determination method and apparatus is provided. The method includes: performing detection of two road lines of a lane in which the vehicle is driving from a driving image; determining whether at least one road line of the two road lines is not detected; if it is determined that the at least one road line is not detected, determining at least one road line for the at least one undetected road line based on first information about a drivable road area comprising the road; and controlling driving of the vehicle based on the at least one determined road line.

Abstract: Systems and methods are disclosed for performing manufacturing testing on an autonomous driving vehicle (ADV) sensor board. A sensor unit of the ADV includes a plurality of sensor I/O channels that provide information to the ADV perception and planning module, to navigate the ADV. An array of sensors is emulated on a sensor unit test board. The sensor unit includes a small software that manages the flow of testing the sensor unit. The sensor unit test board provides emulated sensor data for, e.g., GPS, LIDAR, RADAR, inertial measurement, one or more cameras, humidity, temperature, and pressure, and throttle, braking, and steering inputs. Each emulated sensor includes its own data validity checker to ensure that each sensor I/O channel of the sensor unit is tested. The sensor unit test board can include an LED for each I/O channel that indicates a pass/fail status of the test for the I/O channel.

Abstract: The disclosure describes various embodiments of validating data synchronization between an active sensor and a passive sensor. According to an exemplary method of validating sensor synchronization between an active sensor and a passive sensor, a synchronization device receives a first signal from the active sensor, the first signal indicating that the active sensor has transmitted laser points to a measure board. In response to the first signal, the synchronization device transmits a second signal to the passive sensor to trigger the passive sensor to capture an image of the measure board. A synchronization validation application can perform an analysis of the image of the measure board in view of timing of the first signal and second signal to determine whether the passive sensor and the active sensor are synchronized with each other.

Abstract: Diagnosing a sensor processing unit of an autonomous driving vehicle is described. An example computer-implemented method can include transmitting an executable image of a sensor processing application from a host system to the sensor processing unit via at least one of a universal asynchronous receiver-transmitter (UART) or an Ethernet connection. The method also includes causing the sensor processing unit to execute and launch the executable image of the sensor processing application in the DRAM from the eMMC storage device. The method also includes transmitting a sequence of predetermined commands to the executed sensor processing application to perform a plurality of sensor data processing operations on sensor data obtained from a plurality of sensors or sensor simulators associated with an autonomous driving vehicle.

Abstract: A sensor unit includes a sensor interface, host interface, and pre-processing hardware. The sensor interface is coupled to a plurality of cameras configured to capture images around an autonomous driving vehicle (ADV). The host interface is coupled to a perception and planning system. The pre-processing hardware is coupled to the sensor interface to receive images from the plurality of cameras and to perform one or more pre-processing functions on the images and to transmit pre-processed images to the perception and planning system via the host interface. The perception and planning system is configured to perceive a driving environment surrounding the ADV based on the pre-processed images and to plan a path to control the ADV to navigate through the driving environment. The pre-processing functions can adjust for different calibrations and formats across the plurality of cameras.

Abstract: In one embodiment, a sensor unit receives a first GPS message from a GPS sensor, where the sensor unit is coupled between sensors and a perception and planning system of an autonomous driving vehicle (ADV). The sensor unit determines a type of the first GPS message by matching a predetermined field of the first GPS message with a list of predetermined data patterns. Each of the predetermined data patterns corresponds to one of the predetermined types of GPS messages and decodes a payload of the first GPS message using a decoding algorithm associated with the type of the first GPS message.

Abstract: The disclosure describes various embodiments of validating data synchronization between an active sensor and a passive sensor. According to an exemplary method of validating sensor synchronization between an active sensor and a passive sensor, a synchronization device receives a first signal from the active sensor, the first signal indicating that the active sensor has transmitted laser points to a measure board. In response to the first signal, the synchronization device transmits a second signal to the passive sensor to trigger the passive sensor to capture an image of the measure board. A synchronization validation application can perform an analysis of the image of the measure board in view of timing of the first signal and second signal to determine whether the passive sensor and the active sensor are synchronized with each other.

Abstract: Diagnosing a sensor processing unit of an autonomous driving vehicle is described. An example computer-implemented method can include transmitting an executable image of a sensor processing application from a host system to the sensor processing unit via at least one of a universal asynchronous receiver-transmitter (UART) or an Ethernet connection. The method also includes causing the sensor processing unit to execute and launch the executable image of the sensor processing application in the DRAM from the eMMC storage device. The method also includes transmitting a sequence of predetermined commands to the executed sensor processing application to perform a plurality of sensor data processing operations on sensor data obtained from a plurality of sensors or sensor simulators associated with an autonomous driving vehicle.

Abstract: Systems and methods are disclosed for performing manufacturing testing on an autonomous driving vehicle (ADV) sensor board. A sensor unit of the ADV includes a plurality of sensor I/O channels that provide information to the ADV perception and planning module, to navigate the ADV. An array of sensors is emulated on a sensor unit test board. The sensor unit includes a small software that manages the flow of testing the sensor unit. The sensor unit test board provides emulated sensor data for, e.g., GPS, LIDAR, RADAR, inertial measurement, one or more cameras, humidity, temperature, and pressure, and throttle, braking, and steering inputs. Each emulated sensor includes its own data validity checker to ensure that each sensor I/O channel of the sensor unit is tested.

Abstract: Flexible hardware designs for camera calibration and image pre-processing are disclosed for vehicles including autonomous driving (AD) vehicles. For one example, a sensor unit includes a sensor interface, host interface, and pre-processing hardware. The sensor interface is coupled to a plurality of cameras configured to capture images around an autonomous driving vehicle (ADV). The host interface is coupled to a perception and planning system. The pre-processing hardware is coupled to the sensor interface to receive images from the plurality of cameras and to perform one or more pre-processing functions on the images and to transmit pre-processed images to the perception and planning system via the host interface. The perception and planning system is configured to perceive a driving environment surrounding the ADV based on the pre-processed images and to plan a path to control the ADV to navigate through the driving environment.

Abstract: In one embodiment, a sensor unit receives a first GPS message from a GPS sensor, where the sensor unit is coupled between sensors and a perception and planning system of an autonomous driving vehicle (ADV). The sensor unit determines a type of the first GPS message by matching a predetermined field of the first GPS message with a list of predetermined data patterns. Each of the predetermined data patterns corresponds to one of the predetermined types of GPS messages and decodes a payload of the first GPS message using a decoding algorithm associated with the type of the first GPS message.

Abstract: A multispectral optical sensor is disclosed. In one embodiment, the multispectral optical sensor includes a piezoelectric material, a first sensing layer and a second sensing layer spaced apart from each other on the piezoelectric material and configured to change the propagation speed of the acoustic wave propagated through the piezoelectric material by receiving ultraviolet light and visible light, respectively. The multiple optical sensor further includes a first acoustic wave output part and a second acoustic wave output part disposed on the piezoelectric material respectively corresponding to the first and second sensing layers and configured to generate an electrical signal based on the changed acoustic wave. The multiple optical sensor measures the intensity of ultraviolet and visible light using a single sensor by detecting the change in frequency, and measures the frequency change in the acoustic wave using zinc oxide, gallium nitride), or cadmium sulfide nanoparticles.