Abstract: A method for driving in a blind spot of a sensor mounted on an autonomous vehicle is provided. The method includes steps of: a computing device of the autonomous vehicle running on a specific road locating the autonomous vehicle from precision map information, sensor information and GPS information, and in response to determining that the autonomous vehicle is expected to encounter a specific event, transmitting vehicle location data, travelling direction data, vehicle structure data and sensor location data and sensor's viewing angle data to the server, to determine whether a region of interest corresponding to the specific event is included in blind spot candidates; receiving blind spot stereoscopic data, computed from the data received from the autonomous vehicle and 3D occlusion environmental data corresponding to occluding static objects in the blind spot candidates, from the server; and controlling movement of the autonomous vehicle based on the blind spot stereoscopic data.

Abstract: A deep learning-based autonomous vehicle control system includes: a processor determining an autonomous driving control based on deep learning, correcting an error in determination of the deep learning-based autonomous driving control based on determination of an autonomous driving control based on a predetermined expert rule, and controlling an autonomous vehicle; and a non-transitory computer-readable storage medium storing data for the determination of the deep learning-based autonomous driving control, data for the determination of the expert rule-based autonomous driving control, and information about the error in the determination of the deep learning-based autonomous driving control.

Abstract: A deep learning-based autonomous vehicle control system includes: a processor determining an autonomous driving control based on deep learning, correcting an error in determination of the deep learning-based autonomous driving control based on determination of an autonomous driving control based on a predetermined expert rule, and controlling an autonomous vehicle; and a non-transitory computer-readable storage medium storing data for the determination of the deep learning-based autonomous driving control, data for the determination of the expert rule-based autonomous driving control, and information about the error in the determination of the deep learning-based autonomous driving control.

Abstract: A method for driving in a blind spot of a sensor mounted on an autonomous vehicle is provided. The method includes steps of: a computing device of the autonomous vehicle running on a specific road locating the autonomous vehicle from precision map information, sensor information and GPS information, and in response to determining that the autonomous vehicle is expected to encounter a specific event, transmitting vehicle location data, travelling direction data, vehicle structure data and sensor location data and sensor's viewing angle data to the server, to determine whether a region of interest corresponding to the specific event is included in blind spot candidates; receiving blind spot stereoscopic data, computed from the data received from the autonomous vehicle and 3D occlusion environmental data corresponding to occluding static objects in the blind spot candidates, from the server; and controlling movement of the autonomous vehicle based on the blind spot stereoscopic data.

Abstract: A putter-chipper golf club may include: a body 110 including a coupling groove portion 110a having a through-hole formed therein; a variable face 120 disposed on one side of the body 110 and configured to function as a putter face or a chipper face; a spring 130 disposed on the circumference of a part of the coupling protrusion portion 120a; and a face cap 150 disposed adjacent to the spring 130. When the coupling groove portion 110a of the body 110 and the coupling protrusion portion 120a of the variable face 120 are disengaged from each other by pulling the variable face 120 from the body 110 toward one side, the golf club may be switched to a putter or a chipper through rotation of the variable face 120.

Abstract: An apparatus and method for controlling a vehicle speed based on information about forward vehicles that travel in the same lane may be acquired using Vehicle to Everything (V2X) communications in a cooperative adaptive cruise control (CACC) system. The CACC system includes a communication unit receiving vehicle information from neighboring vehicles using V2V communications; an information collection unit collecting vehicle information of the neighboring vehicles and the subject vehicle using sensors; and a control unit determining a forward vehicle and a far-forward vehicle using the sensors, selecting first and second target vehicles for being followed by the subject vehicle based on the vehicle information of the forward vehicle and the far-forward vehicle and the vehicle information of the neighboring vehicles, and controlling the driving speed of the subject vehicle based on speed information of the first and second target vehicles.

Abstract: A putter-chipper golf club may include: a body 110 including a coupling groove portion 110a having a through-hole formed therein; a variable face 120 disposed on one side of the body 110 and configured to function as a putter face or a chipper face; a spring 130 disposed on the circumference of a part of the coupling protrusion portion 120a; and a face cap 150 disposed adjacent to the spring 130. When the coupling groove portion 110a of the body 110 and the coupling protrusion portion 120a of the variable face 120 are disengaged from each other by pulling the variable face 120 from the body 110 toward one side, the golf club may be switched to a putter or a chipper through rotation of the variable face 120.

Abstract: An apparatus and method for controlling a vehicle speed based on information about forward vehicles that travel in the same lane may be acquired using Vehicle to Everything (V2X) communications in a cooperative adaptive cruise control (CACC) system. The CACC system includes a communication unit receiving vehicle information from neighboring vehicles using V2V communications; an information collection unit collecting vehicle information of the neighboring vehicles and the subject vehicle using sensors; and a control unit determining a forward vehicle and a far-forward vehicle using the sensors, selecting first and second target vehicles for being followed by the subject vehicle based on the vehicle information of the forward vehicle and the far-forward vehicle and the vehicle information of the neighboring vehicles, and controlling the driving speed of the subject vehicle based on speed information of the first and second target vehicles.

Abstract: An apparatus and method for controlling a vehicle speed based on information about forward vehicles that travel in the same lane may be acquired using Vehicle to Everything (V2X) communications in a cooperative adaptive cruise control (CACC) system. The CACC system includes a communication unit receiving vehicle information from neighboring vehicles using V2V communications; an information collection unit collecting vehicle information of the neighboring vehicles and the subject vehicle using sensors; and a control unit determining a forward vehicle and a far-forward vehicle using the sensors, selecting first and second target vehicles for being followed by the subject vehicle based on the vehicle information of the forward vehicle and the far-forward vehicle and the vehicle information of the neighboring vehicles, and controlling the driving speed of the subject vehicle based on speed information of the first and second target vehicles.

Abstract: Provided are a golf putter head and a golf putter including the same, the golf putter head comprising: a top head including an upper face made of a convex curved surface; and a bottom head including a lower face made of a convex curved surface positioned below the top head, in order to provide a golf putter head having improved straightness of a golf ball, and a golf putter comprising the same.

Abstract: A deep learning-based autonomous vehicle control system includes: a processor determining an autonomous driving control based on deep learning, correcting an error in determination of the deep learning-based autonomous driving control based on determination of an autonomous driving control based on a predetermined expert rule, and controlling an autonomous vehicle; and a non-transitory computer-readable storage medium storing data for the determination of the deep learning-based autonomous driving control, data for the determination of the expert rule-based autonomous driving control, and information about the error in the determination of the deep learning-based autonomous driving control.

Abstract: A deep learning-based autonomous vehicle control system includes: a processor determining an autonomous driving control based on deep learning, correcting an error in determination of the deep learning-based autonomous driving control based on determination of an autonomous driving control based on a predetermined expert rule, and controlling an autonomous vehicle; and a non-transitory computer-readable storage medium storing data for the determination of the deep learning-based autonomous driving control, data for the determination of the expert rule-based autonomous driving control, and information about the error in the determination of the deep learning-based autonomous driving control.

Abstract: An autonomous driving control method of a vehicle is provided. The method includes recognizing accuracy of a map within the vehicle and determining an available autonomous driving level. Sensor coverage of the vehicle is recognized and an available autonomous driving level for each section among the available autonomous driving levels is determined. A path with autonomous driving level indicated thereon among driving paths is then recommended according to a destination set in the map, and autonomous driving level calculation information is updated in real-time according to a selected path and autonomous information is provided to a driver.

Abstract: A deep learning-based autonomous vehicle control system includes: a processor determining an autonomous driving control based on deep learning, correcting an error in determination of the deep learning-based autonomous driving control based on determination of an autonomous driving control based on a predetermined expert rule, and controlling an autonomous vehicle; and a non-transitory computer-readable storage medium storing data for the determination of the deep learning-based autonomous driving control, data for the determination of the expert rule-based autonomous driving control, and information about the error in the determination of the deep learning-based autonomous driving control.

Abstract: An apparatus for preventing a pedestrian collision accident, a system having the same, and a method thereof are provided. The apparatus includes a pedestrian sensing unit that senses a pedestrian moving into a dangerous area and calculates velocity and direction information of the pedestrian. A communication unit transmits the velocity and direction information of the pedestrian to a surrounding vehicle. A time-to-collision (TTC) calculating unit calculates a TTC using velocity and direction information of a subject vehicle and second velocity and direction information of the pedestrian, when the TTC calculating unit receives the second velocity and direction information of the pedestrian from another vehicle. A controller outputs based on the TTC a warning to a driver of the subject vehicle or the pedestrian.

Abstract: An apparatus for preventing a pedestrian collision accident, a system having the same, and a method thereof are provided. The apparatus includes a pedestrian sensing unit that senses a pedestrian moving into a dangerous area and calculates velocity and direction information of the pedestrian. A communication unit transmits the velocity and direction information of the pedestrian to a surrounding vehicle. A time-to-collision (TTC) calculating unit calculates a TTC using velocity and direction information of a subject vehicle and second velocity and direction information of the pedestrian, when the TTC calculating unit receives the second velocity and direction information of the pedestrian from another vehicle. A controller outputs based on the TTC a warning to a driver of the subject vehicle or the pedestrian.

Abstract: An adaptive cruise control apparatus includes a sensor device for acquiring information on vehicles around a subject vehicle including information on a distance between a forward vehicle and the subject vehicle, and a controller for calculating an acceleration of the subject vehicle based on the information on vehicles around the subject vehicle, determining a traffic condition around the subject vehicle based on the information on vehicles around the subject vehicle, limiting the acceleration of the subject vehicle according to the determined traffic condition, and controlling a power train of the subject vehicle according to the limited acceleration.

Abstract: An apparatus for controlling narrow road driving of a vehicle includes: an image transform unit generating a depth map using depth information of an object in a front image of a road on which the vehicle travels and generating a height map of the front image by transforming the generated depth map; a map analysis unit recognizing the object and calculating a driving allowable area of the road based on the generated height map; a determination unit determining whether the road is a narrow road based on the calculated driving allowable area and, when the road is determined to be the narrow road, determining whether the vehicle is able to pass through the narrow road; and a signal processing unit controlling driving of the vehicle on the narrow road based on the determination of whether the vehicle is able to pass through the narrow road.

Abstract: A method and an apparatus for detecting a pedestrian by a vehicle during night driving are provided, in which the apparatus includes: a first camera configured to take a first image including color information of a vicinity of the vehicle during night driving; a second camera configured to take a second image including thermal distribution information of the vicinity of the vehicle; a pedestrian detector configured to detect a non-pedestrian area by using the color information from the first image and detect a pedestrian area by excluding the non-pedestrian area from the second image; and a display configured to match and display the pedestrian area on the second image.