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 . 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 training on . real world data, indicating the potential influence of simulation data in real world models and advancements in . handling of anomalous driving scenarios.

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Keywords : data - driving - real - world - handling -

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