论文标题
通过系统的事故模拟增强自动驾驶的转移学习
Enhanced Transfer Learning for Autonomous Driving with Systematic Accident Simulation
论文作者
论文摘要
模拟数据可用于扩展现实世界的驾驶数据，以涵盖边缘案例，例如车辆事故。处理边缘案件的重要性可以在处理汽车事故的高社会成本中观察到，以及对人类驾驶员的潜在危险。为了涵盖所有边缘案例的广泛而多样的范围，我们从系统地进行参数化并模拟了最常见的事故情况。通过将这些数据应用于自主驾驶模型，我们表明，与随机初始化方法相比，对模拟数据集的传输学习提供了更好的概括和避免碰撞。我们的结果表明，可以推断出对模拟数据训练的模型的信息可以推断为对现实世界数据进行训练的模型，这表明模拟数据在现实世界模型中的潜在影响以及在处理异常驾驶方案方面的进步。
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.
