An example control system includes a memory and at least one processor to obtain image data from a given region and perform image analysis on the image data to detect a set of objects in the given region. For each object of the set, the example control system may classify each object as being one of multiple predefined classifications of object permanency, including (i) a fixed classification, (ii) a static and fixed classification, and/or (iii) a dynamic classification. The control system may generate at least a first layer of a occupancy map for the given region that depicts each detected object that is of the static and fixed classification and excluding each detected object that is either of the static and unfixed classification or of the dynamic classification.

An information processing apparatus includes: a point group data acquisition unit configured to acquire, based on information from a sensor configured to detect an object existing in surroundings of a vehicle, point group data related to a plurality of points representing the object; a movement amount estimation unit configured to estimate a movement amount of the vehicle; a storage unit configured to store, as a point group map recorded in association with position information including a latitude and a longitude, relative positions of the plurality of points relative to a first reference position that is a place on a travel path of the vehicle; and a position estimation unit configured to estimate a position of the vehicle based on the point group map, the point group data, and the movement amount.

Training an autonomous machine in a work region for navigation in various lighting conditions includes determining a feature detection range based on an environmental lighting parameter, determining a feature detection score for each of one or more positions in the containment zone based on the feature detection range, determining one or more localizable positions in the containment zone based on the corresponding feature detection scores, and updating the navigation map to include a localization region within the containment zone based on the one or more localizable positions. Navigation may use one or more of an uncertainty area, the localization region, and one or more buffer zones to navigate based on lighting conditions.

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.

A vehicle and control method thereof are intended to promote safe driving of a driver by securing a map of surroundings around the vehicle and displaying a guide line for safe driving when the driver needs to display a guide line for safe driving while the vehicle is driving. The control method of the vehicle includes: checking whether a preset condition for generating a map and displaying a guide line is satisfied while the vehicle is driving; and generating a new map of the surroundings around a place where the vehicle is located and displaying the guide line for safe driving on the map when the preset condition for generating the map and displaying the guide line is satisfied.

Provided are systems and methods for controlling a vehicle based on a map that designed using a factor graph. Because the map is designed using a factor graph, positions of the road can be modified in real-time while operating the vehicle. In one example, the method may include storing a map which is associated with a factor graph of variable nodes representing a plurality of constraints that define positions of lane lines in a road and factor nodes between the variable nodes on the factor graph which define positioning constraints amongst the variable nodes, receiving an indication from the road using a sensor of a vehicle, updating positions of the variable nodes based on the indication and an estimated location of the vehicle within the map, and issue commands capable of controlling a steering operation of the vehicle based on the updated positions of the factor nodes.

A vehicle trajectory prediction device is provided. The vehicle trajectory prediction device includes a transceiver, at least one processor, and at least one memory operatively connected with the at least one processor to store at least one instruction causing the at least one processor to perform operations. The operations receive first trajectory data for an ego-vehicle and second trajectory data for at least one neighbor-vehicle, obtain a first feature vector from a first extractor and obtain a second feature vector from a second extractor, obtain an interdependency feature vector between the ego-vehicle and the at least one neighbor-vehicle from a third extractor having mapping data generated by mapping the second feature vector to the second trajectory data as input data, and generate predicted trajectory data of the ego-vehicle from a trajectory generator having the first feature vector and the interdependency feature vector as input data.

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

Systems, methods, and computer-readable media are provided for detecting a pavement marking around an autonomous vehicle, comparing the detected pavement marking with a pavement marking present in a semantic data map, determining whether a change has occurred between the detected pavement marking and the pavement marking present in the semantic data map, and updating the semantic data map based on the determining of whether the change has occurred between the detected pavement marking and the pavement marking present in the semantic data map.