Examples disclosed herein may involve a computing system configured to (i) maintain a map that is representative of a real-world environment, the map including a plurality of layers that are each encoded with a different type of map data, (ii) obtain sensor data indicative of a given area of the real-world environment, (iii) based on an evaluation of the obtained sensor data and map data corresponding to the given area, detect that a change has occurred in the given area, (iv) based on the collected sensor data, derive information about the detected change including at least a type of the change and a location of the change, (v) based on the derived information about the detected change, determine that one or more layers of the map is impacted by the detected change, and (vi) effect an update to the one or more layers of the map based on the derived information about the change.

Methods, systems and apparatus, including computer programs encoded on computer storage media for determining asset efficiency. Unmanned Aerial Vehicles (UAVs) may be used to obtain aerial images of locations, property or structures. The aerial images may be geo-rectified, and a ortho-mosaic, digital surface model, or a point cloud may be created. In the context of an operation where mobile assets are used, such as construction or earth moving equipment, location-based event information may be obtained. The location-based event information may be used to determine road segment conditions or road topology where problematic road conditions likely exist.

A flood display device includes: a processor with hardware, the processor being provided to: acquire flood point information in which a detection result of a flood point of a road and a classification of a flood situation at the flood point determined based on traveling state data of a vehicle traveling at the detected flood point are associated with each other based on the traveling state data related to the traveling of the vehicle, generate flood detection information in which a display mode of the detection result is changed based on the classification of the flood situation as flood detection information obtained by superimposing the detection result on a position on a map corresponding to the flood point based on the flood point information, and output the flood detection information to a display.

A method, apparatus and computer program product are provided for constructing a high definition map from crowd sourced data using semantic attributes to bootstrap map construction. Methods may include: receiving first sensor data from a first vehicle having traversed a first path along a first lane of a first road segment; identifying features from the first sensor data of the first road segment; receiving second sensor data from a second vehicle having traversed a second path along a second lane of the first road segment; identifying features from the second sensor data of the first road segment; aligning the identified features from the second sensor data with the identified features from the first sensor data of the first road segment; and combining the identified features from the first sensor data and the second sensor data based, at least in part, on the confidence of the respective sensor data.

A method, apparatus and computer program product are provided for predicting the likelihood that road infrastructure has changed. Methods may include: receiving sensor data from a sensor network of a first environment; identifying first map data of an environment, where the first map data is identified based on visual similarity to the first environment, where the first map data includes a history of map updates; identifying second map data of an environment, where the second map data is identified based on visual similarity to the first environment; analyzing the sensor data against the first map data and the second map data to establish correspondence between the sensor data and the first map data and between the sensor data and the second map data; and identifying the first environment as a location of at-risk infrastructure based on the sensor data corresponding to the first map data.

Methods and systems to crowd source road related information. The crowd sourced road related information may include lane-specific data related to a lane-level police presence and/or a disabled roadside vehicle presence. The crowd sourced road related information may include lane-level vehicle spacing information. The communication of the lane-specific data to a crowd sourced road condition server may occur automatically in one mode and may occur only after confirmation from a user in another mode.

To implement a configuration to calculate a manual driving recoverable time required for a driver who is executing automatic driving in order to achieve a requested recovery ratio (RRR) for each road section, and issue a manual driving recovery request notification on the basis of the calculated time. A data processing unit is included, which calculates a manual driving recoverable time required for a driver who is executing automatic driving in order to achieve a predefined requested recovery ratio (RRR) from automatic driving to manual driving and determines notification timing of a manual driving recovery request notification on the basis of the calculated time. The data processing unit acquires the requested recovery ratio (RRR) for each road section set as ancillary information of a local dynamic map (LDM), and calculates the manual driving recoverable time for each road section scheduled to travel, using learning data for each driver.

A map data collection device collects information for locations and attributes of road features contained in map data used for automatic operation control of a moving object, the map data collection device has a memory unit that stores map data containing information for the locations and attributes of one or more first road features, an input device that inputs information for the location and attributes of a second road feature detected using a sensor and a processor configured to refer to the map data stored in the memory unit, to select one first road feature present at the nearest location to the location of the second road feature, and to assess whether map data should be updated based on the attribute information for the first road feature, the attribute information for the second road feature and the distance between the first road feature and the second road feature.

A method, apparatus and computer program product are provided to predict a map object based at least in part on audio data. With respect to predicting a map object, audio data created by a vehicle while driving over a road surface is obtained. The audio data includes one or more audio amplitude features, one or more audio frequency features or a combination of audio amplitude features and audio frequency features. The audio data including the one or more audio amplitude features is provided to a machine learning model and the map object that created the audio data while the vehicle interacted therewith is predicted utilizing the machine learning model. A method, apparatus and computer program product are also provided for training the machine learning model to predict the map object based at least in part on audio data.

Provided are methods, systems, devices, and tangible non-transitory computer readable media for mapping geographical surfaces. The disclosed technology can access image data and sensor data. The image data can include a plurality of images of one or more locations and semantic information associated with the one or more locations. The sensor data can include sensor information associated with detection of one or more surfaces at the one or more locations by one or more sensors. One or more irregular surfaces can be detected based at least in part on the image data and the sensor data. The one or more irregular surfaces can include the one or more surfaces associated with the image data and the sensor data that satisfies one or more irregular surface criteria at each of the one or more locations respectively. Map data including information associated with the one or more irregular surfaces can be generated.