An apparatus for updating map information for a vehicle includes a vehicle information detecting device that detects information of a surrounding vehicle which accompanies a vehicle, when the vehicle travels through an intersection, a line analyzing device that analyzes line information based on information of the surrounding vehicle which accompanies the vehicle, a reliability determining device that determines reliability of the line information, and a controller that extracts a change point on a map based on the reliability and update map information based on the change point.

Methods, systems, and products for generating an updated map for use with an autonomous vehicle driving operation or a simulation thereof may include obtaining first map data associated with a first map of a geographic location including a roadway, and the first map data may include at least one first lane segment. Second map data associated with a second map of the geographic location may be obtained, and the second map data may include at least one second lane segment. A plurality of non-overlapping areas may be determined based on the first lane segment(s) and the second lane segment(s). A first non-overlapping and/or a first warp point within the first non-overlapping area may be selected. The first lane segment(s) may be warped around the first warp point to increase a total overlapping area based on the based on the second lane segment(s) and the first lane segment(s) after warping.

Techniques for collecting, synchronizing, and displaying various types of data relating to a road segment enable, via one or more local or remote processors, servers, transceivers, and/or sensors, (i) enhanced and contextualized analysis of vehicle events by way of synchronizing different data types, relating to a monitored road segment, collected via various different types of data sources; (ii) enhanced and contextualized analysis of filed insurance claims pertaining to a vehicle incident at a road segment; (iii) advantageous machine learning techniques for predicting a level of risk assumed for a given vehicle event or a given road segment; (iv) techniques for accounting for region-specific driver profiles when controlling autonomous vehicles; and/or (v) improved techniques for providing a GUI to display collected data in a meaningful and contextualized manner.

A method of updating a road information, an electronic device, and a storage medium, which relate to an artificial intelligence technology field, in particular to fields of computer vision, deep learning, big data, high-definition map, intelligent transportation, automatic driving and autonomous parking, cloud service, Internet of Vehicles and intelligent cabin technologies. The method includes: processing image data corresponding to a target road region to obtain a set of first road lines; obtaining a set of second road lines according to a trajectory map corresponding to the target road region; calibrating the set of first road lines by using the set of second road lines to obtain a set of third road lines; combining the set of third road lines and a set of historical road lines corresponding to the target road region to obtain a combination result; and updating the set of historical road lines according to the combination result.

Systems and methods for correcting mapping information inaccuracies are described. In some aspects, the method includes receiving terrestrial data captured in an area of interest, and detecting features in the terrestrial data identifying ground points in the area of interest. The method also includes correlating the ground points with ground control points in the area of interest to determine a correspondence, and computing an aggregate of positional differences between corresponding points. The method further includes generating a report indicating a quality of the terrestrial data captured in the area of interest based on the aggregate.

This disclosure provides a method and apparatus for recognizing a parking space.

An aspect of the present disclosure provides a method, performed by an apparatus of a host vehicle, for recognizing a parking space, the method including: recognizing another vehicle and a pillar based on data acquired from a light detection and ranging (lidar) device; recognizing a parking slot marking based on an image captured by a camera; and generating a map of an indoor parking lot based on information on the another vehicle, the pillar, and the parking slot marking.

A server-based system for generating a map for storing a turn signal activation location along a road segment may include at least one processor comprising circuitry and a memory. The memory may include instructions that when executed by the circuitry cause the at least one processor to receive drive information from each of a plurality of vehicles that traversed a road segment, wherein the drive information includes turn signal activation information indicating a detected change in state of a turn signal of at least one target vehicle and a location where the detected change in state of the turn signal of the target vehicle occurred; aggregate the turn signal activation information from two or more of the plurality of vehicles to generate a refined location of a turn signal activation location associated with the road segment; store an indicator of the refined location of the turn signal activation location in a map; store an indicator of the refined location of the turn signal activation location in a map; and distribute the map to one or more vehicles that later traverse the road segment.

The present disclosure relates to real-time localization error correction of an autonomous vehicle (AV). A processor for real-time localization error correction of the AV is provided. The processor is configured to retrieve a reference landmark around the AV from a map aggregating server (MAS), wherein the AV is configured to interact with the MAS for real-time localization; detect, in real time, a ground truth landmark corresponding to the reference landmark, according to image data captured by one or more image capture devices installed on the AV; and determine a deviation between the ground truth landmark and the reference landmark as a real-time correction value for the real-time localization of the AV.

A method for regulating traffic emissions across a street network comprises calculating, by an external control entity, a real-time location-dependent immission load across the street network based on at least one of environmental data, traffic data and configuration data of the street network, providing, by motor vehicles using the street network, navigation data characterizing a route of each respective motor vehicle along the street network and emission data characterizing exhaust emission levels of each respective motor vehicle along its route, and calculating an optimized driving route for each motor vehicle along the street network based on the calculated immission load and the exhaust emission levels of the motor vehicles. The optimized driving route is calculated by the external control entity and transmitted to each motor vehicle via a wireless communication network, or wherein the optimized driving route is calculated by an internal control unit of each respective motor vehicle.

A map (1) to be used by a vehicle which can measure magnetism acting from a road surface side forming a surface of a road to estimate an own vehicle position includes a structure map (M1) which represents a road structure and has position data indicating an absolute position linked to each point and a road-surface magnetic distribution (M2) which is magnetic data at each point on the road surface and has position data indicating an absolute position linked to each point. In the map (1), the structure map (M1) and the road-surface magnetic distribution (M2) are associated with each other via the position data indicating the absolute position.