Method and system of a machine learning model for detection of physical dividers

A system for prediction of a physical divider on a road, comprising:

• at least one memory configured to store instructions;
• and
• at least one processor configured to execute the instructions to:
• retrieve vehicular sensor data and map data captured over a predefined period of time for at least one segment of the road, wherein the vehicular sensor data comprises continuous vehicular sensor data including physical divider average distance, physical divider maximum distance, physical divider minimum distance, or a combination thereof;
• aggregate the respective vehicular sensor data and map data for the at least one segment of the road to generate one or more aggregated values;
• determine at least one of a standard deviation and a mean of each of physical divider average distance, physical divider maximum distance, physical divider minimum distance, or the combination thereof;
• generate the one or more aggregated values based on the determined at least one of the standard deviation and the mean of each of physical divider average distance, physical divider maximum distance, physical divider minimum distance, or the combination thereof;
• and
• generate, output data corresponding to presence of the physical divider in the at least one segment of the road, by a machine learning model based on the one or more aggregated values as an input to the machine learning model.

A non-transitory computer-readable storage medium having stored thereon, computer-executable instructions, which when executed by a computer, cause the computer to perform operations for predicting a physical divider on a road, the operations comprising:

• retrieving vehicular sensor data, map data, or a combination thereof captured over a predefined period of time for at least one segment of the road, wherein the vehicular sensor data comprises continuous vehicular sensor data including physical divider average distance, physical divider maximum distance, physical divider minimum distance, or a combination thereof;
• aggregating the respective vehicular sensor data, map data, or the combination thereof for the at least one segment of the road to generate one or more aggregated values;
• selectively determining at least one of a standard deviation and a mean of each of physical divider average distance, physical divider maximum distance, physical divider minimum distance, or the combination thereof;
• selectively generating the one or more aggregated values based on the determined at least one of the standard deviation and the mean of each of physical divider average distance, physical divider maximum distance, physical divider minimum distance, or the combination thereof;
• and
• generating, output data corresponding to presence of the physical divider in the at least one segment of the road, by a machine learning model based on the one or more aggregated values as an input to the machine learning model.