  Autonomous driving research currently faces data sparsity in representation
of risky scenarios. Such data is both difficult to obtain ethically in the real
world, and unreliable to obtain via simulation. Recent advances in virtual
reality (VR) driving simulators lower barriers to tackling this problem in
simulation. We propose the first data collection framework for risky scenario
driving data from real humans using VR, as well as accompanying numerical
driving personality characterizations. We validate the resulting dataset with
statistical analyses and model driving behavior with an eight-factor
personality vector based on the Multi-dimensional Driving Style Inventory
(MDSI). Our method, dataset, and analyses show that realistic driving
personalities can be modeled without deep learning or large datasets to
complement autonomous driving research.
