Provided are a mapping apparatus, a tracker, a mapping method, and a program that can accurately map a given geographic position on an environmental map. An environmental map data storing unit (80) stores environmental map data indicating an environmental map in which a position expressed by a Euclidean coordinate system is associated with a position expressed by a geographic coordinate system. A SLAM processing executing unit (84) estimates, on the basis of sensing data acquired by a tracker and the environmental map data, a position of the tracker (12) expressed by the Euclidean coordinate system. A target position data acquiring unit (88) acquires target position data indicating a target position expressed by the geographic coordinate system. A coordinate converting unit (90) identifies the target position expressed by the Euclidean coordinate system based on the target position data and the environmental map data.

In a vehicle remote instruction system, a remote commander issues a remote instruction relating to travel of an autonomous driving vehicle based on sensor information from an external sensor that detects an external environment of the autonomous driving vehicle. The vehicle remote instruction system sets a range of information to be transmitted to the remote commander among the sensor information detected by the external sensor, as a limited information range, based on the external situation or an external situation obtained based on map information and a trajectory of the autonomous driving vehicle.

Aspects of the disclosure provide for the generation of a service area map for autonomous vehicles. For instance, graph nodes of a road network may be iterated through in order to identify a set of reachable graph nodes based on a set of routing parameters that define driving limits for the autonomous vehicles. The road network may include the graph nodes as well as edges connecting ones of the graph nodes. A set of S2 cells may be identified based on the set of reachable graph nodes. Vertices of each S2 cell of the set of S2 cells may be determined based on whether each S2 cell of the set of S2 cells is occupied by any of the graph nodes of the set of reachable graph nodes. Contours through cells may be drawn based on the scores. The service area map may be generated using the contours.

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.

Systems and methods for projecting a three-dimensional (3D) surface to a two-dimensional (2D) surface for use in autonomous driving are disclosed. In one aspect, a control system for an autonomous vehicle includes a processor and a computer-readable memory in communication with the processor and having stored thereon computer-executable instructions to cause the processor to: receive a 3D map including a plurality of objects, determine a base point in the 3D map, shift the objects in the 3D map based on the base point, project the objects in the shifted 3D map to a 2D map, and output the 2D map.

An information processing apparatus capable of integrating maps with high accuracy while maintaining the degree of freedom of the movement route in map creation, acquires map data generated in accordance with movement of a movable apparatus, acquires characteristic information for identifying a partial region of a movement route of the movable apparatus, records the characteristic information in association with the map data, recognizes a common region by comparing the characteristic information recorded in association with the map data at a predetermined point in time with a plurality of pieces of the characteristic information recorded in association with the map data prior to the predetermined point in time, and integrates a plurality of pieces of the map data on the basis of the recognized common region.

In a vehicle remote instruction system, a remote commander issues a remote instruction relating to travel of an autonomous driving vehicle based on sensor information from an external sensor that detects an external environment of the autonomous driving vehicle. The vehicle remote instruction system sets a range of information to be transmitted to the remote commander among the sensor information detected by the external sensor, as a limited information range, based on the external situation or an external situation obtained based on map information and a trajectory of the autonomous driving vehicle.

In a vehicle remote instruction system, a remote commander issues a remote instruction relating to travel of an autonomous driving vehicle based on sensor information from an external sensor that detects an external environment of the autonomous driving vehicle. The vehicle remote instruction system sets a range of information to be transmitted to the remote commander among the sensor information detected by the external sensor, as a limited information range, based on the external situation or an external situation obtained based on map information and a trajectory of the autonomous driving vehicle.

Enclosed are embodiments of an ML-based framework for drivable surface annotation. In an embodiment, a method comprises: obtaining, using at least one processor, multimodal map data for a geographic region; and automatically annotating, using the at least one processor, one or more semantic masks of the map data using a machine learning model.

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.