Systems and methods for map construction using a video sequence captured on a camera of a vehicle in an environment, comprising: receiving a video sequence from the camera, the video sequence including a plurality of image frames capturing a scene of the environment of the vehicle; using a neural camera model to predict a depth map and a ray surface for the plurality of image frames in the received video sequence; and constructing a map of the scene of the environment based on image data captured in the plurality of frames and depth information in the predicted depth maps.

A generation device includes processing circuitry configured to receive a plurality of inputs of road map data including longitude/latitude data on a road shoulder line and longitude/latitude data on a lane marker, and refer to the road map data, set a region surrounded by the road shoulder line to be a non-road region, and generate a first polygon indicating a lane region using data on two adjacent non-road regions and on a plurality of lane markers positioned between the two non-road regions.

A device for searching a parking space includes at least one space detection sensor mounted on a vehicle, and a controller that analyzes sensed information obtained through the space detection sensor to recognize space and object information within a parking lot, predicts a distribution of available parking spaces in the parking lot based on the recognized space and object information, and determines an optimal available parking space based on the distribution of the available parking spaces and characteristics of a driver.

A method and an assistance device assist automated driving operation of a motor vehicle. Surroundings raw data recorded by way of a surroundings sensor system of the motor vehicle are processed by the assistance device in order to generate semantic surroundings data. This is accomplished by carrying out semantic object recognition. Further, a comparison of predefined semantically annotated map data against the semantic surroundings data is performed. This involves static objects indicated in the map data being identified in the semantic surroundings data as far as possible. Discrepancies detected during the process are used to recognize perception errors of the assistance device. A recognized perception error prompts a predefined safety measure to be carried out.

The disclosure relates to a method of providing a current local environment status map for a motor vehicle, and to a motor vehicle and a system for carrying out the method. The method includes generating own driving situation data which describe a current, position-related vehicle parameter of the motor vehicle, and generating environment situation data which describe a current arrangement of a further motor vehicle located in a predefined environment of the motor vehicle. The method also includes generating, based on these data, a vehicle environment map which describes a current local traffic situation in the predefined environment. The further vehicle environment maps of the environment of the vehicle are received from at least one other the further motor vehicle and are combined with the generated vehicle environment map using a map data evaluation criterion in order to generate an improved current local environment status map for the motor vehicle.

The present disclosure relates to an information processing apparatus, an information processing method, and a program that allow for appropriately pulling over to a safe road shoulder when an emergency occurs during automated driving. On the basis of distance information from a depth sensor or the like, a travelable region available for a vehicle to travel is set like an occupancy map, and image attribute information is generated from an image by semantic segmentation. On the basis of the image attribute information, an evacuation space is set in the travelable region in accordance with the situation of the road surface of the travelable region, and thus an evacuation space map is created. The present disclosure can be applied to a mobile object.

System, methods, and other embodiments detecting and localizing objects within an image in a wide-view format using a synthetic representation. The method includes converting a real image in a wide-view format to a synthetic representation using a style model, wherein the synthetic representation depicts a distorted view of an object. The method also includes identifying features of the object using an extraction model that distinguishes different scales of the synthetic representation and a simulated scene to define structures associated with the distorted view. The method also includes detecting the object using a decoder model that identifies an attribute and a bounding box of the object from the features. The method also includes executing a task using the attribute and the bounding box to localize the object in the simulated scene.

A method for constructing a lane level map using a three-dimensional point cloud map is provided.

According to the method, during scan matching for estimating the location of a vehicle in the process of automatically constructing a 3D high-definition map, the amount of computation is reduced by reducing the size of a target 3D map. Thereby the method is performed fast and accurate position estimation. In addition, even if the position estimation by scan matching fails, more robust position estimation is possible by estimating the location of the vehicle using LiDAR odometry performed in parallel. The method builds and merges a precise lane map with a pre-built 3D point cloud map using such robust localization to build a more precise 3D precise map and a lane node-link map. By using these three-dimensional precise maps and maps that generate node-links in lanes, a more effective route planning algorithm that can be provided.

According to an aspect of an embodiment, operations may comprise receiving, from a LIDAR mounted on a vehicle, a first 3D point cloud comprising points of a region around the vehicle as observed by the LIDAR. The operations may also comprise accessing an HD map comprising a second 3D point cloud comprising points of the region around the vehicle. The operations may also comprise segmenting LIDAR ground points from LIDAR non-ground points in the first 3D point cloud. The operations may also comprise segmenting map ground points from map non-ground points in the second 3D point cloud. The operations may also comprise determining a pose of the vehicle by matching the LIDAR ground points to the map ground points and by matching the LIDAR non-ground points to the map non-ground points.

Systems and methods for detection and display of marine objects for an aircraft. One example system includes a transceiver configured to communicate with an Automatic Identification System (AIS) server and an electronic controller located within an aircraft. The electronic controller is configured to provide on a display an interface comprising a map representing a travel area. The electronic controller is configured to provide, on the map, a first graphical representation of the aircraft within the travel area. The electronic controller is configured to receive, via the transceiver, marine object data from the AIS server. The electronic controller is configured to periodically update, on the map, a second graphical representation of a first marine object within the travel area based on the marine object data.