A method for evaluating suitability route sections of a digital map storing landmarks for automated driving operation of a vehicle is provided. For each route section of the digital map a spatial density of landmarks is determined, an expected recognizability of the landmarks is determined by a vehicle sensor system under predetermined ambient conditions, a classification is performed based on the determined density and recognizability of the landmarks as to whether a vehicle can be located on the route section with a minimum accuracy required for a predetermined operating mode and/or for a predetermined driving maneuver, and a classification result is stored as a data record in a route attribute associated with the route section, the route attribute indicating for which of the predetermined operating modes and/or driving maneuvers requirements for the minimum accuracy of the landmark-based vehicle localization are met under which of the predetermined environmental conditions.

In a computer-implemented method of assessing driving performance using route scoring, driving data indicative of operation of a vehicle while the vehicle was driven on a driving route may be received. Road infrastructure data indicative of one or more features of the driving route may also be received. A route score for the driving route may be calculated using the road infrastructure data, and a driving performance score for a driver of the vehicle may be calculated using the driving data and the route score for the driving route. Data may be sent to a client device via a network to cause the client device to display the driving performance score and/or a ranking based on the driving performance score, and/or the driving performance score may be used to determine a risk rating for the driver of the vehicle.

A system for correlating drive information from multiple road segments is disclosed. In one embodiment, the system includes memory and a processor configured to receive drive information from vehicles that traversed a first road segment and vehicles that traversed a second road segment. The processor is configured to correlate the drive information from the vehicles to provide a first road model segment representative of the first road segment and a second road model segment representative of the second road segment. The processor correlates the first road model segment with the second road model segment to provide a correlated road segment model if a drivable distance between a first point associated with the first road segment and a second point associated with the second road segment is less than or equal to a predetermined distance threshold, and stores the correlated road segment model as part of a sparse navigational map.

An approach is provided for determining a bicycle lane disruption index based on vehicle sensor data. The approach, for example, involves retrieving sensor data collected from one or more devices within proximity of a bicycle lane. The sensor data is geotagged with location data. The approach also involves processing the geotagged sensor data to identify an observed obstruction to bicycle traffic on the bicycle lane. The approach further involves map-matching the location data to a bicycle lane segment of a geographic database. The approach further involves computing the bicycle lane disruption index for the bicycle lane segment based on the obstruction. The bicycle lane disruption index indicates a probability of encountering any obstruction on the bicycle lane segment. The approach further involves storing the bicycle lane disruption index as an attribute of the bicycle lane segment in the geographic database.

A method, apparatus and computer program product are provided to automatically detect changes in width of road segments in real-time or near real-time using probe data, such as probe data collected from vehicle and/or mobile devices traveling along a road segment. Probe data collected in real-time or near real-time is partitioned in order to identify width-defining portions of the probe data. The width-defining portions may be representative of the laterally-extreme lanes of the road segment, such as the left-most lane and the right-most lane. The width-defining portions are compared to corresponding width-defining portions of historical probe data to determine measures indicative of whether a road segment has expanded or narrowed. Indications of detected segment width changes may be provided to drivers and/or other systems or users. For example, map data for the road segment may be updated to reflect a detected width expansion or narrowing of the road segment.

Methods, systems and computer program products, for map data collection. An anchor point creation mode is activated on a mobile data collection device when the mobile data collection device enters a geographical anchor point creation area. In the anchor point creation mode, the mobile data collection device collects and streams mapping raw data to a remote computing device. A high definition map of the anchor point creation area is created by combining received raw data with mapping data from other data sources. After the map has been created, when a mobile data collection device enters the anchor point creation area, information from the map is used in a data capture mode to refine a determination of a position and/or orientation of the mobile data collection device, and a position and/or orientation of one or more features detected by the mobile data collection device.

A method, apparatus and computer program product are provided to automatically detect changes in width of road segments in real-time or near real-time using probe data, such as probe data collected from vehicle and/or mobile devices traveling along a road segment. Probe data collected in real-time or near real-time is partitioned in order to identify width-defining portions of the probe data. The width-defining portions may be representative of the laterally-extreme lanes of the road segment, such as the left-most lane and the right-most lane. The width-defining portions are compared to corresponding width-defining portions of historical probe data to determine measures indicative of whether a road segment has expanded or narrowed. Indications of detected segment width changes may be provided to drivers and/or other systems or users. For example, map data for the road segment may be updated to reflect a detected width expansion or narrowing of the road segment.

Systems and methods for determining passing and no-passing zones on a roadway. The systems utilize sensors such as an inertial measurement unit, a distance measuring indicator, and a GNSS unit to track the position and orientation of a vehicle as it travels along a route. The systems also utilize a 360° camera and a lidar sensor to generate 360° footage and a lidar map, respectively, of the route. The methods utilize the data and images generated by the system to make line-of-sight determinations for various locations along the route. These determinations are then utilized to determine where passing and no-passing zones should be on a roadway

Systems and methods navigate a vehicle by determining a free space region in which the vehicle can travel. In one implementation, a system may include at least one processor programmed to receive from an image capture device, a plurality of images associated with the environment of a vehicle, analyze at least one of the plurality of images to identify a first free space boundary on a driver side of the vehicle and extending forward of the vehicle, a second free space boundary on a passenger side of the vehicle and extending forward of the vehicle, and a forward free space boundary forward of the vehicle and extending between the first free space boundary and the second free space boundary. The first free space boundary, the second free space boundary, and the forward free space boundary may define a free space region forward of the vehicle. The at least one processor of the system may be further programmed to determine a navigational path for the vehicle through the free space region and cause the vehicle to travel on at least a portion of the determined navigational path within the free space region forward of the vehicle.

An autonomous vehicle includes first and second road surface image obtaining devices that are located on a bottom surface of the vehicle body and obtain images of a road surface below the vehicle body, respectively. The autonomous vehicle also includes a memory unit that stores a map image of the road surface, the map image being associated with geographical location information. The autonomous vehicle further includes first and second self-location estimating units that each compare a feature extracted from the image of the road surface obtained by corresponding one of the first and second road surface image obtaining devices with a feature extracted from the map image, thereby estimating a self-location of the autonomous vehicle.