Related papers: Enhanced Transfer Learning for Autonomous Driving with Systematic Accident Simulation

Enhanced Transfer Learning for Autonomous Driving with Systematic Accident Simulation

URL: http://arxiv.org/abs/2007.12148v1
Date: Thu, 23 Jul 2020 17:27:00 GMT
Title: Enhanced Transfer Learning for Autonomous Driving with Systematic Accident Simulation
Authors: Shivam Akhauri, Laura Zheng, Ming Lin
Abstract summary: We show that transfer learning on simulated data sets provide better generalization and collision avoidance. Our results illustrate that information from a model trained on simulated data can be inferred to a model trained on real-world data.
Score: 3.2456691142503256
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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