Towards Driving Policies with Personality: Modeling Behavior and Style in Risky Scenarios via Data Collection in Virtual Reality
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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.
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