  Generating multi-vehicle interaction scenarios can benefit motion planning
and decision making of autonomous vehicles when on-road data is insufficient.
This paper presents an efficient approach to generate varied multi-vehicle
interaction scenarios that can both adapt to different road geometries and
inherit the key interaction patterns in real-world driving. Towards this end,
the available multi-vehicle interaction scenarios are temporally segmented into
several interpretable fundamental building blocks, called traffic primitives,
via the Bayesian nonparametric learning. Then, the changepoints of traffic
primitives are transformed into the desired road to generate collision-free
interaction trajectories through a sampling-based path planning algorithm. The
Gaussian process regression is finally introduced to control the variance and
smoothness of the generated multi-vehicle interaction trajectories. Experiments
with simulation results of three typical multi-vehicle trajectories at
different road conditions are carried out. The experimental results demonstrate
that our proposed method can generate a bunch of human-like multi-vehicle
interaction trajectories that can fit different road conditions remaining the
key interaction patterns of agents in the provided scenarios, which is import
to the development of autonomous vehicles.
