Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation. (arXiv:2205.03195v1 [cs.LG])

Simulation is a crucial tool for accelerating the development of autonomous
vehicles. Making simulation realistic requires models of the human road users
who interact with such cars. Such models can be obtained by applying learning
from demonstration (LfD) to trajectories observed by cars already on the road.
However, existing LfD methods are typically insufficient, yielding policies
that frequently collide or drive off the road. To address this problem, we
propose Symphony, which greatly improves realism by combining conventional
policies with …

agents arxiv autonomous autonomous driving driving learning simulation