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Generating multi-vehicle trajectories from existing limited data can providerich resources for autonomous vehicle development and testing. This paperintroduces a multi-vehicle trajectory generator (MTG) that can encodemulti-vehicle interaction scenarios (called driving encounters) into aninterpretable representation from which new driving encounter scenarios aregenerated by sampling. The MTG consists of a bi-directional encoder and amulti-branch decoder. A new disentanglement metric is then developed for modelanalyses and comparisons in terms of model robustness and the independence ofthe latent codes. Comparison of our proposed MTG with $\beta$-VAE and InfoGANdemonstrates that the MTG has stronger capability to purposely generaterational vehicle-to-vehicle encounters through operating the disentangledlatent codes. Thus the MTG could provide more data for engineers andresearchers to develop testing and evaluation scenarios for autonomousvehicles.

A New Multi-vehicle Trajectory Generator to Simulate Vehicle-to-Vehicle Encounters