Methods and systems for generating lane line and road edge data using empirical path distributions

A vehicle, comprising:

• an autonomous driving system configured to control the vehicle based on map data;
• and
• a map definition module configured to, by a processor:
• receive telemetry data;
• determine distribution data of a path based on the telemetry data, including by:
• determining information for a transition graph by counting a number of paths visit each pair of nodes in sequence, and including an edge for each pair of nodes that occurs more than a threshold number of times relative to a total number of path;
• and
• initializing, for each node, a conditional distribution to describe a log likelihood of each successor node, and initializing, for each edge between a source node and a target node, a conditional distribution to describe a log likelihood of a position, a speed, a heading, and a time difference at the target node according to a position, a speed, and a heading at the source node;
• determine a plurality of sample data based on a first machine learning model and the distribution data;
• generate both line data and road edge data based on the sample data and a second machine learning model;
• assemble the telemetry data into a path of points that the vehicle has travelled along a road segment and the path is associated with a plurality of lines that are normal to the road segment, wherein the normal lines are equally spaced according to defined increments along the road segment;
• define each intersection of a path with a normal line as a node of the transition graph;
• generate the transition graph by evaluating multiple paths generated for the road segment from a plurality of vehicles;
• generate the map data using the transition graph;
• and
• store the map data including the lane line data and road edge data and control the vehicle using the map data.

A computer implemented system for generating map data used in controlling a vehicle, the system comprising:

• a non-transitory computer readable medium configured to store instructions;
• and
• a processor configured to perform the instructions in order to carry out a method, the method comprising:
• receiving, by a processor, telemetry data;
• determining, by the processor, distribution data of a path based on the telemetry data, including by:
• determining information for a transition graph by counting a number of paths visit each pair of nodes in sequence, and including an edge for each pair of nodes that occurs more than a threshold number of times relative to a total number of path;
• and
• initializing, for each node, a conditional distribution to describe a log likelihood of each successor node, and initializing, for each edge between a source node and a target node, a conditional distribution to describe a log likelihood of a position, a speed, a heading, and a time difference at the target node according to a position, a speed, and a heading at the source node;
• determining, by the processor, a plurality of sample data based on a first machine learning model and the distribution data;
• generating, by the processor, both lane line data and road edge data based on the sample data and a second machine learning model;
• assembling, the via the processor, the telemetry data into a path of points that the vehicle has travelled along a road segment and the path is associated with a plurality of lines that are normal to the road segment, wherein the normal lines are equally spaced according to defined increments along the road segment;
• defining, via the processor, each intersection of a path with a normal line as a node of the transition graph;
• generating, via the processor, the transition graph by evaluating multiple paths generated for the road segment from a plurality of vehicles;
• generating, via the processor, the map data using the transition graph;
• and
• storing, by the processor, the map data including the lane line data and road edge data and controlling the vehicle based on the map data.