Abstract: A method is performed by an electronic device of an ego vehicle that includes a camera, and the method includes: determining, from an image frame of a target vehicle captured by the camera, a rear box of the target vehicle, which is located in front of the ego vehicle, the rear box corresponding to a rear of the target vehicle; and determining a heading direction of the target vehicle based on the rear box and a vanishing point detected in the image frame.

Abstract: A method of determining a final path of a moving object includes acquiring pieces of sensor data, determining a first path of the moving object based on the pieces of sensor data, inputting at least one piece of sensor data among the pieces of sensor data into an encoder that encodes the at least one piece of sensor data, inputting the encoded at least one piece of sensor data into a generative neural network model that generates guide information on a path of the moving object, and determining the final path of the moving object, based on the first path and the guide information.

Abstract: A method and apparatus for determining a slope of a road using a side view camera of a vehicle, the method includes identifying a road image collected from a side view camera, dividing the road image into a plurality of regions, calculating a slope of a road for each of the plurality of regions based on driving lanes comprised in each of the plurality of regions, and determining a slope between the side view camera and the road using the slope of the road calculated for each of the plurality of regions.

Abstract: A method and apparatus for determining a slope of a road using a side view camera of a vehicle, the method includes identifying a road image collected from a side view camera, dividing the road image into a plurality of regions, calculating a slope of a road for each of the plurality of regions based on driving lanes comprised in each of the plurality of regions, and determining a slope between the side view camera and the road using the slope of the road calculated for each of the plurality of regions.

Abstract: Provided are an object detection method and apparatus for an autonomous vehicle. A method of controlling a vehicle includes: detecting a surrounding environment using pieces of data on a driving environment of the vehicle and generating an indication of the surrounding environment; determining, among trained visual prompts received via a network from a server, a target visual prompt corresponding to the pieces of data; generating a merged image by combining a driving image of the autonomous vehicle with the target visual prompt using a predetermined operation; and performing object detection by inputting the merged image into a neural network model of the vehicle, the neural network model configured to infer objects from images inputted thereto.

Abstract: A method of generating lane information using a neural network includes generating a lane probability map based on an input image, generating lane feature information and depth feature information by applying the lane probability map to a second neural network, generating depth distribution information by applying the depth feature information to a third neural network, generating spatial information based on the lane feature information and the depth distribution information, generating offset information including a displacement between a position of a lane and a reference line by applying the spatial information to a fourth neural network, and generating three-dimensional (3D) lane information using the offset information.

Abstract: A processor-implemented method including embedding sensing information, from a plurality of sensors, at a current time point with a previous path of a vehicle at a previous time point and topology information for a topology including plural nodes at the previous time point, determining, by inputting the embedded sensing information to a topology-based neural network, a first distribution area and a second distribution area within a search area, where the first distribution area includes a determined feasible path and the second distribution area includes disconnected nodes that are determined to be reconnected or rewired due to an obstacle, and generating a current path at the current time point from a target location of the vehicle to a current position of the vehicle using topology information at the current time point determined based on the determining.

Abstract: A method is performed by an electronic device of an ego vehicle that includes a camera, and the method includes: determining, from an image frame of a target vehicle captured by the camera, a rear box of the target vehicle, which is located in front of the ego vehicle, the rear box corresponding to a rear of the target vehicle; and determining a heading direction of the target vehicle based on the rear box and a vanishing point detected in the image frame.

Abstract: A method and apparatus for calibrating a camera are provided. The method and apparatus include obtaining a driving image captured by a camera mounted on a vehicle, segmenting line regions including straight lines from the captured driving image, extracting feature points of the straight lines from the line regions, projecting the feature points of the straight lines into a world coordinate system, and estimating an error for a calibration parameter of the camera including at least one of a pitch, a roll, or a yaw so that the feature points projected into the world coordinate system satisfy a line parallel condition.

Abstract: A method of generating lane information using a neural network includes generating a lane probability map based on an input image, generating lane feature information and depth feature information by applying the lane probability map to a second neural network, generating depth distribution information by applying the depth feature information to a third neural network, generating spatial information based on the lane feature information and the depth distribution information, generating offset information including a displacement between a position of a lane and a reference line by applying the spatial information to a fourth neural network, and generating three-dimensional (3D) lane information using the offset information.

Abstract: A method and apparatus for determining a slope of a road using a side view camera of a vehicle, the method includes identifying a road image collected from a side view camera, dividing the road image into a plurality of regions, calculating a slope of a road for each of the plurality of regions based on driving lanes comprised in each of the plurality of regions, and determining a slope between the side view camera and the road using the slope of the road calculated for each of the plurality of regions.