Dataset generation from clustered scenarios for balanced machine-learned model training

A method comprising:

• determining first log data associated with a first vehicle based on first criteria;
• determining second log data based on second criteria different from the first criteria;
• receiving an instruction to generate a dataset associated with a second vehicle, the instruction specifying proportions of scenarios having respective attributes associated with the dataset;
• parameterizing the first log data and the second log data;
• determining a similarity between the first log data and the second log data based at least in part on the parameterizing the first log data and the second log data;
• and
• determining, based at least in part on the similarity between the first log data and the second log data, to exclude at least one of the first log data or the second log data from the dataset.

A non-transitory computer-readable medium storing processor-executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• determining first log data associated with a first vehicle based on first criteria;
• determining second log data based on second criteria different from the first criteria;
• receiving an instruction to generate a dataset associated with a second vehicle, the instruction specifying proportions of scenarios having respective attributes associated with the dataset;
• parameterizing the first log data and the second log data;
• determining a similarity between the first log data and the second log data based at least in part on the parameterizing the first log data and the second log data;
• and
• determining, based at least in part on the similarity between the first log data and the second log data, to exclude at least one of the first log data or the second log data from the dataset.