Systems and methods for generating lane line maps for a vehicle

A system for crowd-sourcing lane line map data for a vehicle, the system comprising:

• a server communication system;
• a map database;
• a server controller in electrical communication with the server communication system and the map database, wherein the server controller is programmed to:
• receive a plurality of observations using the server communication system, wherein the plurality of observations includes at least a first observation and a second observation, wherein receiving the plurality of observations includes receiving the plurality of observations from one or more vehicles, wherein each of the plurality of observations includes a vehicle location trajectory and a plurality of points positioned relative to the vehicle location trajectory, wherein each point of the plurality of points includes a plurality of point characteristics, wherein one of the plurality of point characteristics is a location of each point relative to the vehicle location trajectory, and wherein each of the plurality of points corresponds to an object in an environment surrounding the one or more vehicles;
• generate a point cloud alignment vector using a point cloud registration algorithm based at least in part on the plurality of observations, wherein using the point cloud registration algorithm includes determining the point cloud alignment vector such that shifting the vehicle location trajectory of the second observation by the point cloud alignment vector aligns the vehicle location trajectory of the second observation with the vehicle location trajectory of the first observation;
• determine confidence scores for the plurality of observations based at least in part on the point cloud alignment vector;
• designate a subset of the plurality of 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, wherein generating the optimized aligned point cloud includes:
• determining a plurality of correction vectors, wherein each of the plurality of correction vectors corresponds to one of the plurality of points of the second observation, wherein determining the plurality of correction vectors includes minimizing an objective function to determine the plurality of correction vectors, wherein the objective function includes at least a plurality of cost functions, wherein each of the plurality of cost functions depends at least in part on the plurality of correction vectors, wherein each of the plurality of cost functions corresponds to one of a plurality of optimization constraints, and wherein the plurality of optimization constraints includes at least:
• a trajectory pose constraint, a location vicinity constraint, and optionally an anchor point constraint, wherein the trajectory pose constraint limits a change in shape of the trajectory of the plurality of points of the second observation resulting from applying the plurality of correction vectors to the plurality of points of the second observation, wherein the location vicinity constraint limits movement of the second observation from an original location thereof resulting from applying the plurality of correction vectors to the plurality of points of the second observation, wherein the anchor point constraint limits movement of the second observation towards the first observation resulting from applying the plurality of correction vectors to the plurality of points of the second observation;
• and
• shifting each of the plurality of points of the second observation to generate the optimized aligned point cloud, wherein each of the plurality of points of the second observation is shifted based at least in part on one of the plurality of correction vectors;
• 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.

A system for crowd-sourcing lane line map data for a vehicle, the system comprising:

• a server communication system;
• a map database;
• a server controller in electrical communication with the server communication system and the map database, wherein the server controller is programmed to:
• receive a plurality of observations from one or more vehicles using the server communication system, wherein the plurality of observations includes at least a first observation and a second observation, wherein each of the plurality of observations includes a vehicle location trajectory and a plurality of points positioned relative to the vehicle location trajectory, wherein each point of the plurality of points includes a plurality of point characteristics, wherein one of the plurality of point characteristics is a location of each point relative to the vehicle location trajectory, and wherein each of the plurality of points corresponds to an object in an environment surrounding the one or more vehicles;
• generate a point cloud alignment vector using a point cloud registration algorithm based at least in part on the plurality of observations, wherein using the point cloud registration algorithm includes determining the point cloud alignment vector such that shifting the vehicle location trajectory of the second observation by the point cloud alignment vector aligns the vehicle location trajectory of the second observation with the vehicle location trajectory of the first observation;
• determine confidence scores for the plurality of observations based at least in part on the point cloud alignment vector;
• designate a subset of the plurality of 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, wherein generating the optimized aligned point cloud includes:
• determining a plurality of correction vectors, wherein each of the plurality of correction vectors corresponds to one of the plurality of points of the second observation, wherein determining the plurality of correction vectors includes minimizing an objective function to determine the plurality of correction vectors, wherein the objective function includes at least a plurality of cost functions, wherein each of the plurality of cost functions depends at least in part on the plurality of correction vectors, wherein each of the plurality of cost functions corresponds to one of a plurality of optimization constraints, and wherein the plurality of optimization constraints includes at least:
• a trajectory pose constraint, a location vicinity constraint, and optionally an anchor point constraint, wherein the trajectory pose constraint limits a change in shape of the trajectory of the plurality of points of the second observation resulting from applying the plurality of correction vectors to the plurality of points of the second observation, wherein the location vicinity constraint limits movement of the second observation from an original location thereof resulting from applying the plurality of correction vectors to the plurality of points of the second observation, wherein the anchor point constraint limits movement of the second observation towards the first observation resulting from applying the plurality of correction vectors to the plurality of points of the second observation;
• and
• shifting each of the plurality of points of the second observation to generate the optimized aligned point cloud, wherein each of the plurality of points of the second observation is shifted based at least in part on one of the plurality of correction vectors;
• determine a lane line map based at least in part on the optimized aligned point cloud;
• update the map database based at least in part on the lane line map;
• and
• transmit the lane line map to the one or more vehicles, wherein the one or more vehicles are configured to adjust an operation thereof at least in part based on the lane line map.