  Data for training learning-enabled self-driving cars in the physical world
are typically collected in a safe, normal environment. Such data distribution
often engenders a strong bias towards safe driving, making self-driving cars
unprepared when encountering adversarial scenarios like unexpected accidents.
Due to a dearth of such adverse data that is unrealistic for drivers to
collect, autonomous vehicles can perform poorly when experiencing such rare
events. This work addresses much-needed research by having participants drive a
VR vehicle simulator going through simulated traffic with various types of
accidental scenarios. It aims to understand human responses and behaviors in
simulated accidents, contributing to our understanding of driving dynamics and
safety. The simulation framework adopts a robust traffic simulation and is
rendered using the Unity Game Engine. Furthermore, the simulation framework is
built with portable, light-weight immersive driving simulator hardware,
lowering the resource barrier for studies in autonomous driving research.
  Keywords: Rare Events, Traffic Simulation, Autonomous Driving, Virtual
Reality, User Studies
