  Simulation data can be utilized to extend real-world driving data in order to
cover edge cases, such as vehicle accidents. The importance of handling edge
cases can be observed in the high societal costs in handling car accidents, as
well as potential dangers to human drivers. In order to cover a wide and
diverse range of all edge cases, we systemically parameterize and simulate the
most common accident scenarios. By applying this data to autonomous driving
models, we show that transfer learning on simulated data sets provide better
generalization and collision avoidance, as compared to random initialization
methods. Our results illustrate that information from a model trained on
simulated data can be inferred to a model trained on real-world data,
indicating the potential influence of simulation data in real world models and
advancements in handling of anomalous driving scenarios.
