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 is provided for generating and revising map geometry based on a received image and probe data. A method may include: receiving probe data from a first period of time, where the probe data from a first period of time is from a plurality of probes within a predefined geographic region; generating a first image of the predefined geographic region based on the probe data from the first period of time; receiving probe data from a second period of time different from the first period of time, where the probe data from the second period of time is from a plurality of probes within the predefined geographic region; generating a second image based on the probe data from the second period of time; comparing the first image to the second image; and generating a revised route geometry based on changes detected between the first image and the second image.

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.

A map construction method and a related apparatus are provided. The method includes: obtaining, based on manual driving track data and/or an obstacle grid map, road information, intersection information, and lane information of a region through which a vehicle has traveled; obtaining road traffic direction information based on the manual driving track data and the road information, and obtaining lane traffic direction information based on the lane information and the road traffic direction information; obtaining intersection entry and exit point information based on the intersection information and the lane traffic direction information; and performing, based on the intersection entry and exit point information, an operation of generating a virtual topology center line to obtain an autonomous driving map of the region through which the vehicle has traveled, where the virtual topology center line is a traveling boundary line of the vehicle in an intersection region.

The present disclosure provides a method for optimizing map data, a device and a storage medium, and in particular relates to artificial intelligence, intelligent transportation, smart cities and smart cockpits. A specific implementation is: performing a completeness detection of a lane line on received local map data comprising road condition information to obtain a detection result, the detection result comprising the lane line being complete or is the lane line having a missing area; in a case where the detection result is the lane line having the missing area, performing completion processing on the lane line having the missing area to obtain completed local map data; and performing a rationality detection on the completed local map data, and in a case of passing the detection, synthesizing the completed local map data with global map data to obtain an optimization result of map data.

A method that includes obtaining vehicle sensed environment information by at least one sensor of a vehicle; determining, by an initial location estimate module of the vehicle, an initial location estimate of the vehicle; obtaining, by processor of the vehicle, aerial map segment information related to a segment of an aerial map, the segment comprises an environment of the initial location estimate of the vehicle; determining, based on the vehicle sensed information and on the aerial map segment information, to perform the driving related operation within at least the environment of the initial location estimate of the vehicle; and performing the driving related operation.

A host vehicle-based feature harvester is disclosed. In one implementation, the feature harvester includes memory and a processor configured to receive a plurality of images captured by a camera onboard the host vehicle, the plurality of images being representative of an environment of the host vehicle; analyze at least one image from the plurality of images to identify a representation of a lane mark; select at least one sample area of the representation of the lane mark, wherein the at least one sample area is associated with an image location of at least a portion of the representation of lane mark; determine a location identifier of the at least one sample area; determine a surface quality indicator associated with the at least one sample area; and cause transmission of the location identifier and the surface quality indicator to an entity remotely-located relative to the host vehicle.

Aspects of the disclosure provide a method of providing a destination to an autonomous vehicle in order to enable the autonomous vehicle to collect data according to a targeted driving goal. For instance, a current location of an autonomous vehicle may be received. A set of destinations may be selected from a plurality of predetermined destinations. A route may be determined for each destination. A relevance score may be determined for each destination based on the determined routes and the targeted driving goal. Each destination may be assigned to one of a set of two or more buckets based on the relevance scores. A destination of the set may be selected based on a predetermined sampling probability. The selected destination is sent to the autonomous vehicle in order to cause the autonomous vehicle to travel to the selected destination in an autonomous driving mode.