A map information update method includes: (a) obtaining map information; (b) obtaining landmark observed positions indicating positions of one or more landmarks in a captured image; (c) adding that includes (i) generating added map information by adding information pertaining to the landmark observed positions to the map information, and (ii) updating the map information obtained in (a) to the added map information; (d) predicting that includes (i) calculating predicted map information based on the map information updated in (c), by using a neural network inference engine that has been trained, and (ii) updating the map information to the predicted map information; and updating information that includes (i) calculating updated map information based on the map information updated in (d), by using a gradient method, and (ii) updating the map information to the updated map information.

Embodiments described herein relate to predicting the utilization of electric vehicle (EV) charge points. Methods may include: receiving an indication of a plurality of candidate locations for EV charge points; determining static map features of the plurality of candidate locations; inputting the plurality of candidate locations and static map features into a machine learning model, where the machine learning model is trained on existing EV charge point locations, existing EV charge point static map features, and existing EV charge point utilization; determining, based on the machine learning model, a predicted utilization of an EV charge point at the plurality of candidate locations; and generating a representation of a map including the plurality of candidate locations, where candidate locations of the plurality of candidate locations are visually distinguished based on a respective predicted utilization of an EV charge point at the candidate locations.

A method comprises receiving, at a vehicle system of a vehicle, sensor data from one or more sensors; detecting an object based on the sensor data; determining whether a dynamic map maintained by a remote computing device includes the detected object; upon determination that the dynamic map includes the detected object, determining a remaining time-to-live associated with the detected object based on data associated with the dynamic map; and transmitting data about the detected object to the remote computing device if the determined remaining time-to-live associated with the detected object is less than a predetermined threshold time.

Described herein are methods of generating learning data to facilitate de-biasing the labeled location of an object of interest within an image. Methods may include: receiving sensor data, where the sensor data is a first image; determining reference corner locations of an object in the first image using image processing; generating observed corner locations of the object in the first image from the determined reference corner locations; generating a bias transformation based, at least in part, on a difference between the reference corner locations and the observed corner locations of the object in the first image; receiving sensor data from another image sensor of a second image; receiving observed corner locations of an object in the second image from a user; and applying the bias transformation to the observed corner locations of the object in the second image to generate de-biased corners for the object in the second image.

An approach is provided for pattern-based map matching of a probe trace to a digital map. The approach involves querying the digital map for a set of road links within a threshold distance of a probe point. The approach also involves determining a match starting point for each road link of a set of road links. The approach further involves selecting a sampled probe point from the probe trace. The approach also involves generating one or more patterns for said each road link based on the match starting point, the sampled probe point, the topology polyline, or a combination thereof. The approach further involves selecting a matched road link from among the set of road links based on the one or more patterns. The probe point is then snapped to the matched road link.

The present disclosure provides a method and apparatus for automatically producing map data. The method includes: performing track rectification on crowdsourcing tracks based on corresponding standard tracks, and locating each map element included, based on depth information of track point images included in the rectified crowdsourcing tracks; comparing a latest map element obtained based on the rectified crowdsourcing tracks locating and an old map element at a corresponding locating position using a pre-built entity semantic map; determining, in response to a change in the latest map element compared to the old map element, a target processing method according to a processing standard of a changed map element pre-abstracted from a map element update specification; and processing the latest map element according to the target processing method to obtain a processed latest map.

An apparatus for updating a map detects the positions of reference points corresponding to a feature on a road being traveled by a vehicle from surrounding data representing features around the vehicle, and updates probability distributions associated with the respective reference points so that the probabilities of existence of the reference points at the detected positions of the reference points increase. Each of the probability distributions indicates the probability of existence of the corresponding reference point as a function of position.

Methods, apparatuses, systems, and computer program products are provided. An example method comprises receiving a change trigger; and providing two or more map versions to a plurality of vehicle apparatuses. The map versions may comprise a stable map version and a changed map version. The example method further comprises receiving two or more responses from at least two of the plurality of vehicle apparatuses. A response comprises an indicator of a preferred map version selected by a vehicle apparatus of the plurality of vehicle apparatuses from the two or more map versions. The example method further comprises analyzing the responses to determine a most preferred map version; and when it is determined that the most preferred map version is a changed map version, updating one or more map databases based at least in part on the changed map version.

Provided is a computer-implemented method of determining a point of interest and/or a road type in a map, comprising the steps of: acquiring processed sensor data collected from one or more vehicles; extracting from the processed sensor data a set of classification parameters; and determining based on the set of classification parameters one or more points of interest (POI) and its geographic location and/or one or more road types.

Techniques for determining a location and type of traffic-related features and using such features in route planning are discussed herein. The techniques may include determining that sensor data represents a feature such as a traffic light, road segment, building type, and the like. The techniques further include determining a cost of a feature, whereby the cost is associated with the effect of a feature on a vehicle traversing an environment. A feature database can be updated based on features in the environment. A cost of the feature can be used to update costs of routes associated with the location of the feature.