We develop a generic mechanism for generating vehicle-type specific sequences of waypoints from a probabilistic foundation model of driving behavior. Many foundation behavior models are trained on data that does not include vehicle information, which limits their utility in downstream applications such as planning. Our novel methodology conditionally specializes such a behavior predictive model to a vehicle-type by utilizing byproducts of the reinforcement learning algorithms used to produce vehicle specific controllers. We show how to compose a vehicle specific value function estimate with a generic probabilistic behavior model to generate vehicle-type specific waypoint sequences that are more likely to be physically plausible then their vehicle-agnostic counterparts.
@article{arxiv.2208.04987,
title = {Vehicle Type Specific Waypoint Generation},
author = {Yunpeng Liu and Jonathan Wilder Lavington and Adam Scibior and Frank Wood},
journal= {arXiv preprint arXiv:2208.04987},
year = {2023}
}