According to an embodiment, a system includes at least one change detecting device that includes a map information storage unit receiving a high-definition map including a property of each road facility object and spatial coordinates of a feature point from a map updating server and storing the received high-definition map, an object coordinates obtaining unit recognizing at least one road facility object from the road image and obtaining a property of the recognized object and spatial coordinates of a feature point. and a changed object detecting unit comparing the property of the recognized object and the spatial coordinates of the feature point with the high-definition map and, if a change object is detected, transmitting object change information including a property and feature point spatial coordinates of the change object to the map updating server.

A method for generating thumbnails is disclosed. The method may include receiving an area map, and chart data having a geographical information set. The method may include generating a plurality of panels having one or more panel corners based on the chart data. The method may include projecting the one or more panel corners onto the area map, defining one or more corners of a plurality of areas to be extracted. The method may include determining an extraction mapping for each area to be extracted, the extraction mapping configured to map the plurality of areas to be extracted to a plurality of thumbnail panels. The method may include extracting area map data from each area to be extracted to generate a plurality of thumbnail panels using the determined extraction mapping. The method may include generating a thumbnail display by combining each thumbnail panel of the plurality of thumbnail panels.

A vehicle can include an on-board data processing system that receives sensor data captured by various sensors of the vehicle. As a vehicle travels along a route, the on-board data processing system can process the captured sensor data to identify a potential vehicle stop. The on-board data processing system can then identify geographical coordinates of the location at which the potential vehicle stop occurred, use artificial intelligence to classify a situation of the vehicle at the potential stop, and determine whether the stop was caused by a road obstacle, such as a speed bump, a gutter, an unmarked crosswalk, or any other obstacle not at an intersection. If the stop was caused by the road obstacle, the on-board data processing system can generate virtual stop or yield line data corresponding to the identified geographic coordinates and transmit this data to a server over a network for processing.

Systems and methods receive an image of at least part of an environmental layout and at least two geographic feature locations associated with the image. At least two geographic locations are determined, each corresponding to a respective one of the at least two geographic feature locations and a map scale is determined for the images based on the at least two geographic feature locations and the at least two geographic locations. The map scale allows conversion between points on the image and geographic locations. The image and the map scale are used to display a location interface on a client device that allows a user to search the environmental layout for an asset associated with a tracking node, where the location interface graphically displays graphical elements indicative of communication recency between the tracking tag and one or more infrastructure nodes.

The present invention identifies a danger spot effectively. In a navigation device 10, a danger spot identification unit 34 identifies a jump-out danger spot on the basis of past information which is acquired by a past information acquisition unit 24 and indicates a situation of a jump-out accident in the past, and surrounding information which is acquired by a surrounding information acquisition unit 32 and indicates a surrounding situation of a vehicle. Here, the past information includes surrounding facility identification information (information related to an identification planimetric feature) used for identifying a jump-out danger spot and information related to a jump-out object. As a result, the danger spot identification unit 34 can identify a danger spot of a jump-out which can occur due to a planimetric feature (a facility) in a jump-out accident. Therefore, the navigation device 10 can identify a jump-out danger spot effectively.

An approach is provided for mapping based on pedestrian type. The approach, for instance, involves processing image data to a determine the pedestrian type of at least one pedestrian depicted in the image data. The approach also involves determining a classification of a geographic zone based on the detected pedestrian type. The approach further involves generating a digital map representation of the geographic zone based on the classification.

A drive assist apparatus includes a processor and a storage. The processor includes a line-of-sight detector, an emotion estimator, and a notification processing unit. The line-of-sight detector detects information on a line of sight of one or both of a vehicle occupant present in a vehicle and a traffic participant present around the vehicle. The emotion estimator estimates an emotion of the one or both of the vehicle occupant and the traffic participant. The storage stores, when the estimated emotion is detected as a positive emotion, data on a viewpoint including information on a position of the vehicle on map data and information on the line of sight. The notification processing unit makes, on the basis of the data on the viewpoint stored in the storage, a notification of information on a direction to notice to the vehicle occupant present in the vehicle traveling through a location corresponding to the viewpoint.

Systems and methods are provided for autonomous vehicle navigation. The systems and methods may map a lane mark, may map a directional arrow, selectively harvest road information based on data quality, map road segment free spaces, map traffic lights and determine traffic light relevancy, and map traffic lights and associated traffic light cycle times.

An end-to-end system for data generation, map creation using the generated data, and localization to the created map is disclosed. Mapstreams – or streams of sensor data, perception outputs from deep neural networks (DNNs), and/or relative trajectory data – corresponding to any number of drives by any number of vehicles may be generated and uploaded to the cloud. The mapstreams may be used to generate map data – and ultimately a fused high definition (HD) map – that represents data generated over a plurality of drives. When localizing to the fused HD map, individual localization results may be generated based on comparisons of real-time data from a sensor modality to map data corresponding to the same sensor modality. This process may be repeated for any number of sensor modalities and the results may be fused together to determine a final fused localization result.

A method for providing an item of localization information for the localization of a further vehicle at a localization location. An item of precision information is read in that represents a degree of localization precision achieved in the localization of another vehicle at the localization location. In a further step, the item of localization information is outputted to an interface at the further vehicle, using the item of precision information.