A navigation apparatus and a method for providing an individualization map service of the navigation apparatus are provided. The navigation apparatus includes a detector configured to sense a vehicle state and a travelling environment during travelling, and a processor configured to recognize a drive context based on the vehicle state and the travelling environment, and to make an individualization map to be serviced based on the recognized drive context.

A method, apparatus, and computer program product are provided for anonymizing the trajectory of a vehicle. Methods may include: receiving a sequence of probe data points defining a trajectory; for a subset of the sequence of probe data points defining the trajectory beginning at an origin: updating a counter value at each probe data point, where the counter value is updated based, at least in part, on properties of a number of road links emanating from each junction through which the trajectory passed to reach a location associated with the respective probe data point; in response to the counter value satisfying a predetermined value after an update relative to a given probe data point, removing probe data points before the given probe data point in the sequence of probe data points to obtain origin-obscured probe data points; and creating a cropped trajectory including the origin-obscured probe data points.

The present disclosure is directed to using anomaly data detected in traffic data to efficiently initiate remote assistance sessions. In particular, a computing system can receive, from a computing device associated with a human-driven vehicle, travel data for the human-driven vehicle. The computer system can identify a navigation anomaly associated with the human-driven vehicle based on the travel data. The computer system can generate, based on the identified navigation anomaly, an anomaly entry for storage in an anomaly database, the anomaly entry comprising geofence data describing a geographic area associated with the navigation anomaly. The computer system can determine, based on location data received from an autonomous vehicle and the geofence data, that the autonomous vehicle is entering the geographic area associated with the navigation anomaly. The computer system can initiate a remote assistance session with the autonomous vehicle.

Systems and methods for validating drive pose refinement are provided. In some aspects, a method includes receiving image data that depicts an area of interest, and receiving a plurality of virtual points generated using the image data. The method also includes selecting at least one drive in the area of interest that captures the plurality of virtual points, and generating a refined pose track for each of the at least one drive by applying a drive alignment process to drive data from the at least one drive using the plurality virtual points. The method further includes comparing the refined pose track to a control pose track generated using control repoints, and generating, based on the comparison, a report that validates the refined pose track.

Aspects concern a method for correcting position estimates of a movable object. According to various embodiments, the method comprises establishing (1001) a hidden Markov model, HMM, instance for a movable object and, for positioning times of a sequence of positioning times, receiving (1002) a position estimate from a positioning device of the movable object for a respective positioning time, determining (1003) a set of candidate path segments for the positioning time, determining (1004) likelihoods for the candidate path segments to correspond to the position estimate by application of the Viterbi algorithm to the HMM instance, expanding (1005) the HMM instance by the determined likelihoods for the candidate path segments for the positioning time and determining (1006) a corrected position estimate from a candidate path segment of the set of candidate path segments with the highest likelihood.

The present technology provides systems, methods, and devices that can update aspects of a map as an autonomous vehicle navigates a route, and therefore avoids the need for dispatching a special purpose mapping vehicle for these updates. As the autonomous vehicle navigates the route, data captured by at least one sensor of an autonomous vehicle can indicate an inconsistency between pre-mapped from a high-resolution sensor system describing a location on a map, and current data describing a new feature of the location. The current data can be clustered together based on a threshold spatial closeness, where the clustering describes the new feature, and semantic labels of the pre-mapped data from the high-resolution sensor system can be updated based on the new feature described by the clustered current data.

A system and method of generating a two-dimensional (2D) image of an environment is provided. The system includes a scanner having a first light source, an image sensor, a second light source and a controller, the second light source emitting a visible light, the controller determining a distance to points based on a beam of light emitted by the first light source and receiving of the reflected beam of light from the points. Processors are operably coupled to the scanner execute a method comprising: generating a map of the environment; emitting light from the second light source towards an edge defined by at least a pair of surfaces; detecting the edge based on emitting a second beam of light and receiving the reflected second beam of light; and defining a room on the map based on the detecting of the corner or the edge.

A method for generating an image of a route network that is travelled through by a rail vehicle. The image is generated with the use of activities that are recorded by the rail vehicle as it travels through the route network and sorted in an activity sequence. In order to provide an improved method, patterns in the activity sequence are identified with use of a pattern detection method and the image of the route network is generated with the use of the identified patterns.

In an embodiment, a method comprises detecting, at a processor of an autonomous vehicle, a discrepancy between a map and a property sensed by at least one sensor onboard the autonomous vehicle, the property being associated with an external environment of the autonomous vehicle. In response to detecting the discrepancy, and based on the discrepancy, an annotation for the map is generated via the processor. A signal representing the annotation is caused to be transmit to a compute device that is remote from the autonomous vehicle. A signal representing a map update is received from the compute device that is remote form the autonomous vehicle. The map update is generated based on the annotation, the map update (1) including replacement information for a region of the map associated with the annotation, and (2) not including replacement information for a remainder of the map.

A method for updating a high definition map according to one embodiment comprises: obtaining a two-dimensional image that captures a target area corresponding to at least a part of an area expressed by a three-dimensional high definition map, generating a three-dimensional local landmark map of the target area from a position of a landmark in the two-dimensional image, based on a position and an orientation of a photographing device which has captured the two-dimensional image and updating the high definition map with reference to the local landmark map corresponding to the target area of the three-dimensional high definition map.