PersonaDrive proposes a way to make simulated traffic agents behave with more realistic human style variation.
The system retrieves demonstrations from a dataset where people drove under style instructions such as aggressive, neutral, or conservative behavior, then conditions a vision-language-action agent on those examples.
That could improve closed-loop autonomous driving tests, where unrealistic surrounding traffic can hide failure modes or make simulation results less transferable.