Abstract: A method for estimating lane lines receiving, by a controller, telemetry data and map data. The method further includes matching the telemetry data with the map data to generate map-matched telemetry data and determining, using a gaussian mixture model (GMM), a first set of lane line points relating to the lane lines of the roadway within the predetermined region. Also, the method includes determining, using a lane-point detection (LPD) model, a second set of lane line points relating to the lane lanes of the roadway within the predetermined region and determining, using a lane-point clustering (LPC) model, a plurality of lane lines of the roadway based on the first set of lane line points and the second set of lane line points.

Abstract: A method and system of aligning multiple vehicle perception data. The system is configured to execute the method including the steps of receiving perception data observed by a plurality of vehicles, processing the perception data to create feature points observed by the plurality of vehicles, determining matching feature points observed by each of the plurality of vehicles, determining an average offset between the matching feature points observed by each of the plurality of vehicles, reiterating perception data alignment until the average offset is below a predetermined threshold, using the average offset in a factor graph optimization to align multiple vehicle perception data, and updating a map database with the aligned perception data. The updated map data may be communicated to an autonomous vehicle for vehicle operations.

Abstract: A method and system of feature matching for associating vehicle perception data for crowd sourcing map data. The system is configured to execute the method including the steps of receiving perception data from a plurality of vehicles; processing the perception data to identify first feature points observed by a first vehicle and second feature points observed by a second vehicle, respectively; creating feature descriptors using multilayer bitmaps for each of the first feature points and for each of the second feature points; determining pairs of matching first and second feature points by comparing histograms of the feature descriptors; associating pairs of matching first and second feature points; updating a map database with the associated pairs of matching first and second feature points; and operating an autonomous vehicle based on the updated map database.

Abstract: A vehicle system includes a vehicle control module of a vehicle and a control module. The control module is configured to receive data from vehicles over a period of time, cluster the received data into groups, detect at least one set of duplicate groups of the groups, merge the duplicate groups into a single group or remove at least one of the duplicate groups to form a set of deduplicated groups associated with the at least one object, and determine an expected location of the at least one object based on the set of deduplicated groups. The vehicle control module is configured to receive the expected location of the at least one object and generate a control signal for controlling at least one operation of the vehicle based on the expected location of the at least one object. Other example systems and methods are also disclosed.

Abstract: A method for crowdsourced detection of a dynamic road feature includes detecting the dynamic road feature using a sensor of a vehicle and communicating with a dynamic road feature database of a remote server. Further, the method includes uploading a feature data to the dynamic road feature database of the remote server in response to detecting the dynamic road feature. The feature data includes information about the road dynamic feature previously detected by the sensor of the vehicle. Also, the method further includes controlling the movement of the vehicle using the feature data.

Abstract: Systems and methods are provided for crowd-sourcing lane line map data for a vehicle. The systems include a server communication system, a map database, a server controller that is programmed to: receive observations using the server communication system, generate a point cloud alignment vector based at least in part on the observations, determine confidence scores for the observations based at least in part on the point cloud alignment vector, designate a subset of the observations having confidence scores in excess of a confidence score threshold as anchor points, generate an optimized aligned point cloud based at least in part on the point cloud alignment vector and the anchor points, determine a lane line map based at least in part on the optimized aligned point cloud, and update the map database based at least in part on the lane line map.

Abstract: A method for crowd-sourcing lane line map data for a vehicle includes receiving a plurality of observations. The method also includes classifying the plurality of observations into a plurality of observation categories. Each of the plurality of observation categories includes at least one of the plurality of observations. The method also includes determining a plurality of aligned point clouds based at least in part on the plurality of observations. One of the plurality of aligned point clouds corresponds to each of the plurality of observation categories. The method also includes determining a plurality of lane line maps based at least in part on the plurality of aligned point clouds. One of the plurality of lane line maps corresponds to each of the plurality of aligned point clouds. The method also includes updating a map database based at least in part on the plurality of lane line maps.

Abstract: A system includes a control module in communication with one or more sensors of a plurality of vehicles. The control module is configured to receive a bitmap including a plurality of pixels representing a plurality of lane lines of a roadway sensed by the one or more sensors, extract a plurality of line components of the plurality of lane lines, detect whether a junction exists in a line component of the plurality of line components, in response to detecting the junction in the line component, split the line component into two or more subcomponents, generate a plurality of line points for each of the subcomponents using a regression model, create at least one line based on the plurality of line points, and generate a map of the roadway including the at least one line. Other example systems and methods for creating maps of roadways are also disclosed.

Abstract: A system for fusing two or more versions of map data together includes one or more central computers that receive road network data representing a road network for a predefined geofenced area. The central computers compute a plurality of points that are each positioned at a predetermined distance from one another. The central computers create a plurality of bounding boxes for the road network based on the plurality of points and create a set of closest matched map data points for each bounding box that is part of the road network by executing a map-matching registration algorithm to align the two or more versions of map data with one another. The central computers execute a maximum likelihood estimation algorithm to determine probability distribution parameters of the set of closest matched map data points compared to the ground truth map data.

Abstract: A method for merging lane line maps includes determining a road topology of a road segment. The method also includes identifying a plurality of fused points based at least in part on the road topology and based at least in part on a first lane line map of the road segment and a second lane line map of the road segment. The method also includes forming a fused lane line map based at least in part on the plurality of fused points. The method also includes performing a first action based at least in part on the fused lane line map.

Abstract: A method for crowd-sourcing lane line map data for a vehicle may include receiving a plurality of observations. The plurality of observations includes at least a first observation and a second observation. The method further may include generating a point cloud alignment vector based at least in part on the plurality of observations. The method further may include generating an optimized aligned point cloud based at least in part on the point cloud alignment vector. The method further may include determining a lane line map based at least in part on the optimized aligned point cloud. The method further may include updating a map database based at least in part on the lane line map.

Abstract: A map quality assessment system that evaluates error associated with primary map data includes one or more central computers that execute instructions to determine the error associated with the primary map data, compare the error associated with the primary map data with a range of values defined by one or more quality metric values, and in response to determining the error associated with the primary map data falls within the range of values defined by the one or more quality metric values, retain a template for selecting the primary map data. In response to determining the error associated with the primary map data falls outside the range defined by the one or more quality metric values, the one or more central computers evaluate the primary map data for real-life anomalies within one or more roadways represented by the primary map data.

Abstract: A system for rasterizing telemetry data collected from a plurality of vehicles into map content includes one or more central computers in wireless communication with the plurality of vehicles that are each situated within a predefined geofenced area. The one or more central computers receive the telemetry data from each of the plurality of vehicles. The one or more central computers determine a two-dimensional probability distribution indicating a probability density of the plurality of vehicles over a predefined period of time along a particular road segment located within the predefined geofenced area.

Abstract: A method for estimating lane lines receiving, by a controller, telemetry data and map data. The method further includes matching the telemetry data with the map data to generate map-matched telemetry data and determining, using a gaussian mixture model (GMM), a first set of lane line points relating to the lane lines of the roadway within the predetermined region. Also, the method includes determining, using a lane-point detection (LPD) model, a second set of lane line points relating to the lane lanes of the roadway within the predetermined region and determining, using a lane-point clustering (LPC) model, a plurality of lane lines of the roadway based on the first set of lane line points and the second set of lane line points.

Abstract: A method and system of map data reconciliation in connected vehicles. The method includes processing information to detect a same object; determining an identification of the same object; determining a confidence level of the identification of the same object; determining a probability mass function (PMF) of the identification of the same object using Dempster-Shafer Theory; determining a most probably identification of the same object based on the PMF; and communicating the most probably identification of the same object to a vehicle to implement a vehicle function. The system includes a conflict resolution module configured to receive confidence levels from multiple vehicles detecting the same object and to determine a reconciled identification of the same object using an algorithm based on Dempster-Shafer Theory, and a weight calculation module configured to apply a weight function based on a scene context weight function and on a historical score of the vehicles.

Abstract: A method for crowd-sourcing lane line map data for a vehicle may include receiving a plurality of observations. The plurality of observations includes at least a first observation and a second observation. The method further may include generating a point cloud alignment vector based at least in part on the plurality of observations. The method further may include generating an optimized aligned point cloud based at least in part on the point cloud alignment vector. The method further may include determining a lane line map based at least in part on the optimized aligned point cloud. The method further may include updating a map database based at least in part on the lane line map.

Abstract: A method for crowdsourced detection of a dynamic road feature includes detecting the dynamic road feature using a sensor of a vehicle and communicating with a dynamic road feature database of a remote server. Further, the method includes uploading a feature data to the dynamic road feature database of the remote server in response to detecting the dynamic road feature. The feature data includes information about the road dynamic feature previously detected by the sensor of the vehicle. Also, the method further includes controlling the movement of the vehicle using the feature data.

Abstract: A system for resolving discrepancies in map data includes one or more central computers in wireless communication with one or more vehicles. The one or more central computers are programmed to receive a first map dataset and a second map dataset. The one or more central computers are further programmed to receive a plurality of crowdsourced map datasets. Each of the plurality of crowdsourced map datasets represents the predefined geographical area. The one or more central computers are further programmed to compare each of the plurality of crowdsourced map datasets with the first map dataset and the second map dataset to determine one or more common lane lines. The one or more central computers are further programmed to determine a fused map dataset based on the first map dataset, the second map dataset, the plurality of crowdsourced map datasets, and the one or more common lane lines.

Abstract: A global positioning system (GPS)-bias detection and reduction system including a GPS-bias model having GPS statistical data creating a database representing data collected from a vehicle group having thousands or multiple thousands of vehicles saved in a database. At least one newly collected vehicle GPS data point is compared to the GPS statistical data to reduce negative effects of GPS-bias and to update the vehicle GPS-bias correction based on a previous GPS-bias model. A selected road node and a segment of a roadway have a map matching performed using a nearest service from a collection location of the GPS statistical data. A GPS-bias is calculated using a look-up of the database. An estimated horizontal position error (EHPE) defining a quality indicator is applied to distinguish a good quality GPS statistical data from a poor quality GPS statistical data.

Abstract: A vehicle system is provided for correcting in real-time a camera-based estimated position of a road object. The system includes a camera for generating an image input signal including image sensor data associated with the road object. The system further includes one or more input devices for generating a vehicle input signal including vehicle sensor data associated with a position, a speed, and a heading of the vehicle. The system further includes a computer, which includes one or more processors and a non-transitory computer readable medium (CRM) storing instructions. The processor is programmed to match the image sensor data and the vehicle sensor data to one another based on a common time of collection. The processor is further programmed to determine an error model and a deviation of a current camera-based position from a predicted position. The processor is further programmed to update the error model based on the deviation.