论文标题
可自动驾驶的可自定义动态场景建模和数据生成平台
A Customizable Dynamic Scenario Modeling and Data Generation Platform for Autonomous Driving
论文作者
论文摘要
与人类的安全互动是自动驾驶的重大挑战。这种互动的性能取决于自动驾驶仪的基于机器学习的模块，例如感知，行为预测和计划。这些模块需要具有高质量标签和各种逼真的动态行为的培训数据集。因此，很难培训这样的模块来处理稀有场景，因为根据定义，它们在现实世界数据集中很少代表。因此，实际上需要使用涵盖这些罕见情况的合成数据来增强数据集。在本文中，我们提出了一个平台，以建模动态和交互式场景，在具有不同标记的传感器数据模式的模拟中生成场景，并收集此信息以进行数据增强。据我们所知，这是专门针对自动驾驶领域的这些任务的第一个集成平台。
Safely interacting with humans is a significant challenge for autonomous driving. The performance of this interaction depends on machine learning-based modules of an autopilot, such as perception, behavior prediction, and planning. These modules require training datasets with high-quality labels and a diverse range of realistic dynamic behaviors. Consequently, training such modules to handle rare scenarios is difficult because they are, by definition, rarely represented in real-world datasets. Hence, there is a practical need to augment datasets with synthetic data covering these rare scenarios. In this paper, we present a platform to model dynamic and interactive scenarios, generate the scenarios in simulation with different modalities of labeled sensor data, and collect this information for data augmentation. To our knowledge, this is the first integrated platform for these tasks specialized to the autonomous driving domain.
