A method is provided for gathering probe data and using the gathered data to create map intersection turn paths. Methods may include: receiving probe data from a plurality of probes passing through an intersection; identifying a turn maneuver of the intersection including an incoming road link and an outgoing road link; generating a curve of the turn maneuver based on a geometry of the intersection and a scaling factor; filtering the probe data based on the generated curve to obtain remaining probe data; fitting a spline to the remaining probe data to generate a turn path representative of the turn maneuver; and providing for guidance of a vehicle based on the spline of the turn path. Embodiments including apparatuses and computer program products for creating map intersection turn paths are also provided.

A method for providing vehicle information includes: receiving first vehicle data encoded according to a first protocol and corresponding to an environment external to a vehicle; receiving high definition mapping data corresponding to objects in the environment external to the vehicle; generating position information for objects indicated in the high definition mapping data by correlating locations of objects indicated by the high definition mapping data with objects in the environment external to the vehicle detected by at least one sensor; generating second vehicle data by correlating the high definition mapping data, the position information, and the first vehicle data; and encoding the second vehicle data according to a second protocol.

The present invention is related to a vehicle positioning device being connected to a first sensor outputting satellite positioning data, and a second sensor detecting a state amount of a vehicle and outputting the state amount as state amount data, and being connected to at least one of a third sensor detecting a terrestrial object and outputting data of a relative relationship between the terrestrial object and the vehicle, and a fourth sensor detecting a road line shape and outputting road line shape data. The vehicle positioning device includes: observed value processing circuitry configured to generate an actual observed value; sensor correction circuitry; inertial positioning circuitry; and observed value prediction circuitry configured to predict an observed value and output the observed value as a predicted observed value. Positioning calculation is performed by using the predicted observed value and the actual observed value and results are output as positioning results.

A moving body control device expands an autonomously movable region, appropriately determines reliability of the expanded region, and realizes stable autonomous movement at low cost. The device includes: an external map acquisition unit acquiring an external map; a sensor acquiring external environment information around the moving body; a movement information acquisition unit acquiring movement information indicating a position or an advancing angle of the moving body; an autonomous movement range management unit generating an autonomous movement map based on the external information or the movement information; and a control unit controlling movement of the moving body based on the autonomous movement map, the external environment information, or the movement information, the autonomous movement range management unit comparing the autonomous movement map with the external map, the control unit controlling the moving body based on a comparison result in the autonomous movement range management unit.

Proposed is a GPS error correction method performed through comparison of three-dimensional HD-maps in a duplicate area, and more particularly, a method that can calculate a correction value for a GPS error by comparing three-dimensional HD-maps of a corresponding duplicate area when the duplicate area is generated on a GPS route in the process of acquiring raw data to be used in a HD-map for autonomous driving. Particularly, in the method, an accurate correction value for the GPS error can be calculated by comparing three-dimensional point cloud data acquired by utilizing basically installed LiDAR, an Inertial Measurement Unit (IMU) and the like, without using expensive equipment such as a plurality of high-precision GPS receivers, stereo cameras or the like.

There are provided a non-transitory computer readable storage medium, a road estimation method, and a road estimation apparatus. The storage medium stores a program causing a computer to execute a road estimation process, the road estimation process including: receiving, as an input step, positioning data obtained by a plurality of runs of at least one movable body; generating, as a running lines generation step, a plurality of running lines respectively indicating running routes of the plurality of runs, on the basis of the positioning data; and generating, as a road determination step, a representative line from among a set of the plurality of running lines on the basis of passage frequencies of the plurality of running lines, to determine the generated representative line as a road line.

An error diagnosis device diagnosing if location measurement error has occurred in a location measurement sensor measuring a self-location of a vehicle has a storing part storing map information divided in every road sections; a location acquiring part acquiring self-location information of the vehicle measured by the location measurement sensor; a drive section identifying part identifying road sections on which the vehicle has been driven in the map information, based on the self-location information; and an error diagnosis part. The error diagnosis part judges that there is location measurement error in the location measurement sensor when a first and second road sections of one of the road sections identified as having been driven on by the vehicle are not consecutive, and judges that there is no location measurement error in the location measurement sensor when the first road section and the second road section are consecutive.

Systems and methods are provided for vehicle navigation. In one implementation, at least one processor may receive, from a camera of a vehicle, at least one image captured from an environment of the vehicle. The processor may analyze the at least one image to identify a road topology feature in the environment of the vehicle represented in the at least one image and at least one point associated with the at least one image. Based on the identified road topology feature, the processor may determine an estimated path in the environment of the vehicle associated with the at least one point. The processor may further cause the vehicle to implement a navigational action based on the estimated path.

Systems and methods are provided for vehicle navigation. In one implementation, at least one processor may receive, from a camera of a vehicle, at least one image captured from an environment of the vehicle. The processor may analyze the at least one image to identify a road topology feature in the environment of the vehicle represented in the at least one image and at least one point associated with the at least one image. Based on the identified road topology feature, the processor may determine an estimated path in the environment of the vehicle associated with the at least one point. The processor may further cause the vehicle to implement a navigational action based on the estimated path.

A method for detecting changes in road information includes converting, by a domain adapter, a captured road image and a projected road-layout map into first intermediate data and second intermediate data of a same feature space, respectively, calculating, by a similarity determiner, a similarity between the captured road image and the projected road-layout map based on the first intermediate data and the second intermediate data, and detecting, by a change detector, presence or absence of changes in road information on the projected road-layout map based on the calculated similarity.