Gm Global Technolgoy Operations, Inc.

Carl P Darukhanavala

Bo Yu

Vivek Vijaya Kumar

Joon Hwang

Gui Chen

Fan Bai

Donald K Grimm

Shu Chen

Shuqing Zeng

Carolyn Heather MacLeod

Kausalya Singuru

Kevin A O'dea

Muhammad Rehan

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.

System for fusing two or more versions of map data

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.

Crowd-sourcing lane line maps for a vehicle

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.

Global positioning system bias detection and reduction

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.

System and method for correcting in real-time an estimated position of a road object

A method of creating a high-definition (HD) map of a roadway includes receiving a multi-layer probability density bitmap. The multi-layer probability density bitmap represents a plurality of lane lines of the roadway sensed by a plurality of sensors of a plurality of vehicles. The multi-layer probability density bitmap includes a plurality of points. The method further includes recursively conducting a hill climbing search using the multi-layer probability density bitmap to create a plurality of lines. In addition, the method includes creating the HD map of the roadway using the plurality of lines determined by the hill climbing search.

Hill climbing algorithm for constructing a lane line map

A method includes obtaining information about a lead vehicle. The lead vehicle is traveling directly ahead of the host vehicle and the information includes velocity of the lead vehicle and a gap g between the host vehicle and the lead vehicle. Based on detecting a change in the velocity of the lead vehicle in the information, a corresponding desired acceleration is computed for the host vehicle using perceived acceleration of the lead vehicle to maintain the gap g within a specified range of gap values. The perceived acceleration of the lead vehicle is based on the change in the velocity indicated by the information. Based on a check of parameters involved in the computing the corresponding desired acceleration, a modified desired acceleration is computed for the host vehicle using a lag for the lead vehicle that results in an intended acceleration of the lead vehicle differing from the perceived acceleration.

Consideration of acceleration lag in lead vehicle to enhance host vehicle operation

A method includes receiving sensor data from a plurality of sensors of a plurality of vehicles. The sensor data includes vehicle GPS data and sensed lane line data of the roadway. The method further includes creating a plurality of multi-layer bitmaps for each of the plurality of vehicles using the sensor data, fusing the plurality of the multi-layer bitmaps of each of the plurality of vehicles to create a fused multi-layer bitmap, creating a plurality of multi-layer probability density bitmaps using the fused multi-layer bitmap, extracting lane line data from the plurality of multi-layer probability density bitmaps, and creating the high-definition (HD) map of the roadway using the multi-layer probability density bitmaps and the lane line data extracted from the plurality of multi-layer probability density bitmaps.

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Royal Oak, MI, United States of America

Carl P Darukhanavala