Parallel end-to-end driving aims to improve the performance of end-to-end driving models using both simulated- and real-world data. However, how to efficiently utilize the data from both the simulated world and the real world remains a difficult issue, since these data are usually not well aligned. In this article, we build a data set called the parallel end-to-end driving data set (PED) for parallel end-to-end driving research. PED consists of 13 000 images from the simulated world and 13 000 images from the real world that are used to train the model, as well as 2700 images from the real world that are used to test the model. The simulated-world data in PED are constructed according to the real world, and each simulated-world image corresponds to a real-world image. PED also contains the vehicle measurement data (GPS, speed, steering angle, and heading direction of the vehicle) related to both the simulated- and real-world images, which are not available in some other data sets. We conduct two types of experiments to illustrate the effectiveness and the superiority of PED and explore some ways to mix the simulated-world data with the real-world data to improve the performance of end-to-end driving models.

中文翻译：

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从并行端到端驾驶数据集中学习驾驶模型

并行端到端驾驶旨在使用模拟和现实世界数据提高端到端驾驶模型的性能。然而，如何有效地利用来自模拟世界和现实世界的数据仍然是一个难题，因为这些数据通常不能很好地对齐。在本文中，我们构建了一个称为并行端到端驾驶数据集 (PED) 的数据集，用于并行端到端驾驶研究。PED 包含来自模拟世界的 13 000 张图像和来自用于训练模型的真实世界的 13 000 张图像，以及来自用于测试模型的来自真实世界的 2700 张图像。PED中的模拟世界数据是根据现实世界构建的，每个模拟世界图像对应一个现实世界图像。PED 还包含车辆测量数据（GPS、速度、转向角、和车辆的航向）与模拟和真实世界的图像相关，这些在其他一些数据集中不可用。我们进行了两种类型的实验来说明 PED 的有效性和优越性，并探索将模拟世界数据与现实世界数据混合以提高端到端驾驶模型性能的一些方法。