论文标题
使用仿真技术的数据生成来改善自动驾驶汽车的感知机制
Data generation using simulation technology to improve perception mechanism of autonomous vehicles
论文作者
论文摘要
计算机图形技术的最新进展允许对汽车驾驶环境进行更现实的重新限制。它们使自动驾驶汽车模拟器（例如DeepGTA-V和Carla（学习采取行动））能够生成大量的合成数据，这些数据可以补充现有的现实世界数据集中，以培训自动驾驶汽车感知。此外，由于自动驾驶汽车模拟器可以完全控制环境，因此它们可以产生危险的驾驶场景，而现实世界中数据集缺乏恶劣天气和事故情况。在本文中，我们将演示将从现实世界收集的数据与模拟世界中生成的数据相结合的有效性，以训练对象检测和本地化任务的感知系统。我们还将提出一个多层次的深度学习感知框架，旨在效仿人类的学习经验，其中在某个领域中学习了一系列从简单到更困难的任务。自动驾驶汽车感知器可以从易于驱动的方案中学习，以通过模拟软件定制的更具挑战性的方案。
Recent advancements in computer graphics technology allow more realistic ren-dering of car driving environments. They have enabled self-driving car simulators such as DeepGTA-V and CARLA (Car Learning to Act) to generate large amounts of synthetic data that can complement the existing real-world dataset in training autonomous car perception. Furthermore, since self-driving car simulators allow full control of the environment, they can generate dangerous driving scenarios that the real-world dataset lacks such as bad weather and accident scenarios. In this paper, we will demonstrate the effectiveness of combining data gathered from the real world with data generated in the simulated world to train perception systems on object detection and localization task. We will also propose a multi-level deep learning perception framework that aims to emulate a human learning experience in which a series of tasks from the simple to more difficult ones are learned in a certain domain. The autonomous car perceptron can learn from easy-to-drive scenarios to more challenging ones customized by simulation software.
