Abstract: A semiconductor includes a lower structure, an upper structure on the lower structure, and a connection pattern between the lower structure and the upper structure. The connection pattern is configured to electrically connect the lower structure and the upper structure to each other. The lower structure includes a lower base and a first lower chip on the lower base. The first lower chip includes a chip bonding pad, a pad structure, and a heat sink structure. The connection pattern is connected to the upper structure and extends away from the upper structure to be connected to the pad structure. The pad structure has a thickness greater than a thickness of the chip bonding pad. At least a portion of the heat sink structure is at a same height level as at least a portion of the pad structure.

Abstract: A semiconductor includes a lower structure, an upper structure on the lower structure, and a connection pattern between the lower structure and the upper structure. The connection pattern is configured to electrically connect the lower structure and the upper structure to each other. The lower structure includes a lower base and a first lower chip on the lower base. The first lower chip includes a chip bonding pad, a pad structure, and a heat sink structure. The connection pattern is connected to the upper structure and extends away from the upper structure to be connected to the pad structure. The pad structure has a thickness greater than a thickness of the chip bonding pad. At least a portion of the heat sink structure is at a same height level as at least a portion of the pad structure.

Abstract: There are a method and a system for extracting a region of interest in an image obtained through a high-resolution camera installed in an autonomous vehicle. The method for extracting the region of interest based on a drivable region in the high-resolution camera includes a step of acquiring an image through the high-resolution camera; and a step of extracting, by a processor, a region of interest within the acquired image by using 3D drivable region information. Accordingly, far-distance information extraction performance may be enhanced, and all of the functions of a set of a wide angle camera and a narrow angle camera of HD may be performed only with one FHD or UHD wide angle camera, so that the number of sensors may be reduced and a production cost may be reduced.

Abstract: There are provided a scenario similarity retrieval-based automatic scenario generation system and method. According to an embodiment, a scenario retrieval-based automatic scenario generation method includes: retrieving scenarios similar to a query scenario from a scenario DB for an autonomous driving test; filtering only scenarios that meet a selection condition from the retrieved scenarios; and converting components of the filtered scenarios to suit a target condition. Accordingly, a desired scenario may be automatically generated by retrieving a scenario similar to a targeted scenario, converting the retrieved scenario, and concretizing the scenario.

Abstract: There is provided a VIL system-based autonomous driving function verification method. According to embodiments of the disclosure, a VIL system enables an autonomous vehicle to verify an autonomous driving function by interlocking with a virtual road environment in any other place, without having to go to a real test road corresponding to a simulated virtual road environment. Accordingly, an autonomous driving function can be rapidly verified based on a VIL system with respect to various virtual road environments without changing a driving place, so that speed and convenience in development of autonomous driving technology can be enhanced.

Abstract: Provided are a method and a system for generating an AI training hierarchical dataset including data acquisition context information. A GT dataset generation method according to an embodiment of the present disclosure includes: acquiring and storing vehicle data; acquiring and storing sensor data generated at a sensor installed in a vehicle; and generating and storing context information which is information regarding a context at a time when the data is acquired. Accordingly, in generating a GT descriptor, various contexts, conditions at the time when data is acquired may be made to be easily analyzed, classified on the GT descriptor through a hierarchical dataset, which hierarchically describes context information at the time when sensor data is acquired on the descriptor, so that an AI network is effectively trained, and eventually, has high recognition performance.

Abstract: Provided is a method for filtering driving paths in order to stably track a path which is generated for various purposes, such as recognizing a lane and keeping or changing the lane, or avoiding an obstacle around a road. A driving path filtering system according to an embodiment of the present disclosure includes: a position recognition unit configured to recognize a position of a vehicle; a lane recognition unit configured to recognize a lane of a road; and a path generator configured to generate a path for the vehicle to travel on, based on a result of recognizing the position and a result of recognizing the lane, and to perform path noise filtering with respect to the generated path. Accordingly, a driving path can be stabilized by removing a path noise (error) by complementally filtering a lane recognition-based path generation method and a map-based path generation method according to a driving environment.

Abstract: Provided is a deep learning-based panoptic segmentation accelerated processing technique using a complexity-based RPN skip method. An image segmentation system includes: a first processing unit configured to extract dynamic objects in an instance segmentation method by using an extracted feature; a calculation unit configured to control to skip some areas of the feature extracted at the network by the first processing unit, on the basis of complexity of the input image; a second processing unit configured to extract static objects in a semantic segmentation method by using the feature extracted at the network; and a fusion unit configured to fuse a result of extracting by the first processing unit and a result of extracting by the second processing unit. Accordingly, the panoptic segmentation method can be easily performed even in an embedded environment by reducing complexity for panoptic segmentation processing by reducing a calculation burden.

Abstract: There are provided an apparatus and a method for LiDAR data conversion for training various types of autonomous vehicles by using pre-acquired data. A method for converting LiDAR data according to an embodiment includes: receiving an input of first LiDAR data which is pre-acquired through a first LiDAR sensor mounted in a first vehicle; converting the inputted first LiDAR data into second LiDAR data which is acquired through a second LiDAR sensor mounted in a second vehicle; and outputting the converted second LiDAR data, and converting includes converting the first LiDAR data into LiDAR data on a reference coordinate system, and converting the converted LiDAR data into the second LiDAR data which is LiDAR data on a coordinate system of the second LiDAR sensor.

Abstract: There are provided a behavior recognition method for efficient recognition of hand signals and gesture and a behavior recognition AI network system using the same. The behavior recognition method for efficient recognition of hand signals and gestures according to an embodiment includes: an input feature extraction step of extracting, by a behavior recognition AI network system, key points F1 from bounding box data of an object which makes hand signals to be inputted by sequence, and generating skeleton data of the object; and a spatial feature extraction step of calculating, by the behavior recognition AI network system, a length F2 and an angle F3 of a bone vector based on the key points F1 and the skeleton data, and extracting a spatial feature. Accordingly, performance of recognition of hand signals and gestures may be enhanced.

Abstract: Provided is a traffic simulation for controlling a motion of an object, such as a vehicle, a pedestrian moving on a road or a pavement, in a driving simulation, an autonomous driving simulation, or the like. A traffic simulation method according to an embodiment of the present disclosure includes the steps of: importing a new moving object into a simulation environment of a simulator; retrieving data of a moving path and a start point of the moving object which is created based on a function, among pre-stored data; calculating 3D coordinates regarding a position of the moving object; moving the moving object along the moving path in the simulation environment, based on the calculated 3D coordinates; and calculating a next position of the moving object. Accordingly, a motion of an object within a simulator may be precisely created and reliability of validation regarding an operation of an algorithm mounted in an autonomous driving vehicle may be enhanced.

Abstract: Provided is an air housing apparatus for protecting, from contamination, scratches, damage and the like, a lens and a cover glass which affect the performance of an optical device installed on a vehicle such as a camera and LIDAR. The air housing apparatus according to an embodiment of the present invention comprises: a cover into which high pressure air is fed; an air guide for injecting the supplied high pressure air toward the front surface of a lens or a sensor; and an air housing provided so as to connect the cover and the air guide and transferring the high pressure air supplied from the cover to the air guide. Therefore, a lens, sensor, or cover glass of a vehicle-installed optical device such as a camera and LIDAR can be continually protected from contamination and scratches by means of the injected air.

Abstract: A semiconductor device includes a pattern data generation circuit generating pattern data, a data comparison circuit receiving read data which are outputted from cell arrays included in a core area by a read operation and comparing the read data with the pattern data to generate a fail code, and a fail flag generation circuit comparing the fail code with a set code to generate a fail flag.

Abstract: A semiconductor device includes: a non-volatile memory including a normal region, a self-repair region and a redundancy region, each having a plurality of cells; a first boot-up control block suitable for controlling a first boot-up operation to detect defective cells of the normal region and store a defective address in a first latch unit; a self-program control block suitable for controlling a self-program operation to program the defective address stored in the first latch unit into the self-repair region; and a second boot-up control block suitable for controlling a second boot-up operation to read out data of the normal region based on an input address while reading out data of the redundancy region instead of the data of the normal region when data of the self-repair region coincides with the input address.

Abstract: The described technology relates to a light detection and ranging (LIDAR) device. The LIDAR device can include a transmission unit configured to emit a first signal, a first lens unit configured to convert the first signal into parallel light, a reflection unit configured to adjust a direction of the converted first signal and a second lens unit configured to enable the first signal to have the same focal plane even when a reflection angle of the reflection unit changes. The LIDAR device can also include a third lens unit configured to convert the first signal passing through the second lens unit into parallel light, a fourth lens unit configured to increase an angle of the first signal passing through the third lens unit and a reception unit configured to receive a second signal reflected by an object after passing through the fourth lens unit.

Abstract: A semiconductor device includes: a non-volatile memory including a normal region, a self-repair region and a redundancy region, each having a plurality of cells; a first boot-up control block suitable for controlling a first boot-up operation to detect defective cells of the normal region and store a defective address in a first latch unit; a self-program control block suitable for controlling a self-program operation to program the defective address stored in the first latch unit into the self-repair region; and a second boot-up control block suitable for controlling a second boot-up operation to read out data of the normal region based on an input address while reading out data of the redundancy region instead of the data of the normal region when data of the self-repair region coincides with the input address.

Abstract: The described technology relates to a light detection and ranging (LIDAR) device. The LIDAR device can include a transmitter configured to emit an optical signal, a first lens section configured to convert the optical signal into collimated light, a reflector configured to adjust a direction of the converted optical signal, a second lens section configured to allow the adjusted optical signal to have the same focal plane even though a reflection angle of the reflector is varied and a third lens section configured to convert the optical signal passed through the second lens section into collimated light. The LIDAR device can also include a fourth lens section configured to allow the optical signal, and a receiver configured to receive the optical signal passed through the fourth lens section. The third lens section and the fourth lens section are positioned on the same line in a first direction.

Abstract: A semiconductor device includes: a non-volatile memory including a normal region, a self-repair region and a redundancy region, each having a plurality of cells; a first boot-up control block suitable for controlling a first boot-up operation to detect defective cells of the normal region and store a defective address in a first latch unit; a self-program control block suitable for controlling a self-program operation to program the defective address stored in the first latch unit into the self-repair region; and a second boot-up control block suitable for controlling a second boot-up operation to read out data of the normal region based on an input address while reading out data of the redundancy region instead of the data of the normal region when data of the self-repair region coincides with the input address.

Abstract: A light detection and ranging (LIDAR) time of flight (TOF) sensor for inputting and outputting simultaneously and 3-dimensional laser scanning system including the same are disclosed. In one aspect, the sensor includes a substrate and a light receiving element array provided on the substrate and including a plurality of light receiving elements. The sensor also includes readout circuits configured to receive electrical signals from the light receiving elements and perform signal processing on the electrical signals. The sensor further includes metal lines disposed on the light receiving element array in parallel, provided to correspond to the number of the light receiving elements, and configured to connect the light receiving elements to the readout circuits in one-to-one correspondence.

Abstract: A semiconductor device includes: a non-volatile memory including a normal region, a self-repair region and a redundancy region, each having a plurality of cells; a first boot-up control block suitable for controlling a first boot-up operation to detect defective cells of the normal region and store a defective address in a first latch unit; a self-program control block suitable for controlling a self-program operation to program the defective address stored in the first latch unit into the self-repair region; and a second boot-up control block suitable for controlling a second boot-up operation to read out data of the normal region based on an input address while reading out data of the redundancy region instead of the data of the normal region when data of the self-repair region coincides with the input address.