  Generating multi-vehicle trajectories from existing limited data can provide
rich resources for autonomous vehicle development and testing. This paper
introduces a multi-vehicle trajectory generator (MTG) that can encode
multi-vehicle interaction scenarios (called driving encounters) into an
interpretable representation from which new driving encounter scenarios are
generated by sampling. The MTG consists of a bi-directional encoder and a
multi-branch decoder. A new disentanglement metric is then developed for model
analyses and comparisons in terms of model robustness and the independence of
the latent codes. Comparison of our proposed MTG with $\beta$-VAE and InfoGAN
demonstrates that the MTG has stronger capability to purposely generate
rational vehicle-to-vehicle encounters through operating the disentangled
latent codes. Thus the MTG could provide more data for engineers and
researchers to develop testing and evaluation scenarios for autonomous
vehicles.
