论文标题
自动驾驶道路上未知物体的多模式检测
Multimodal Detection of Unknown Objects on Roads for Autonomous Driving
论文作者
论文摘要
在过去几年中，深度学习的巨大进步促成了我们道路上有自动驾驶汽车的未来。然而，他们的感知系统的性能在很大程度上取决于使用的培训数据的质量。由于这些系统通常仅覆盖所有对象类别的一小部分，因此自主驾驶系统将面临，因此这些系统在处理意外事件方面遇到了困难。为了安全地在公共道路上运行，对未知类别的对象的识别仍然是一项至关重要的任务。在本文中，我们提出了一条新的管道来检测未知物体。我们没有专注于单个传感器模式，而是通过以顺序结合最先进的检测模型来利用LiDAR和相机数据。我们在Waymo开放感知数据集上评估了我们的方法，并指出了当前在异常检测中的研究差距。
Tremendous progress in deep learning over the last years has led towards a future with autonomous vehicles on our roads. Nevertheless, the performance of their perception systems is strongly dependent on the quality of the utilized training data. As these usually only cover a fraction of all object classes an autonomous driving system will face, such systems struggle with handling the unexpected. In order to safely operate on public roads, the identification of objects from unknown classes remains a crucial task. In this paper, we propose a novel pipeline to detect unknown objects. Instead of focusing on a single sensor modality, we make use of lidar and camera data by combining state-of-the art detection models in a sequential manner. We evaluate our approach on the Waymo Open Perception Dataset and point out current research gaps in anomaly detection.
