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.
翻译：与人类安全互动是自主驾驶的一大挑战。 这种互动的性能取决于自动驾驶的机器学习模块,如感知、行为预测和规划。 这些模块需要高质量的标签和各种现实动态行为的培训数据集。 因此,培训这些模块难以处理稀有情景,因为根据定义,它们很少出现在现实世界数据集中。 因此,实际需要增加包含这些罕见情景的合成数据的数据集。 在本文中,我们提出了一个平台,用于模拟动态和互动情景,以不同模式模拟有标签的传感器数据,并收集这种信息用于数据扩增。 据我们所知,这是这些任务专门用于自主驱动领域的首个综合平台。
相关内容
微信扫码咨询专知VIP会员