用于自动驾驶的行人环境模型
Pedestrian Environment Model for Automated Driving
AI总结：
本文提出融合行人位置与骨骼姿态的自动驾驶环境模型，仅用单目图像和车辆定位，通过姿态估计、匈牙利跟踪与多帧三维聚合，在 CARLA 和 nuScenes 上实现约16%相对位置误差。
摘要：
除了与其他车辆正确交互外，自动驾驶车辆还应当能够对行人或骑行者等弱势道路使用者作出安全反应。为了实现行人与自动驾驶车辆之间的安全交互，车辆必须能够理解行人的行为。常见的环境模型并不包含用于理解行人意图的身体姿态等信息。在本研究中，我们提出了一种环境模型，该模型不仅包含行人的位置，还包含其姿态信息。我们仅使用单目相机的图像以及车辆的定位数据作为行人环境模型的输入。我们利用神经网络人体姿态估计器从图像中提取骨骼信息。此外，我们采用基于匈牙利算法和自运动补偿的简单跟踪算法对骨骼进行跟踪。为了获得位置的三维信息，我们结合车辆位置对连续帧中的数据进行聚合。我们在使用 CARLA 仿真器生成的数据以及 nuScenes 数据集上展示了所提出的行人环境模型。总体而言，我们在两个数据集上均达到了约 16% 的相对位置误差。
英文摘要：
Besides interacting correctly with other vehicles, automated vehicles should also be able to react in a safe manner to vulnerable road users like pedestrians or cyclists. For a safe interaction between pedestrians and automated vehicles, the vehicle must be able to interpret the pedestrian's behavior. Common environment models do not contain information like body poses used to understand the pedestrian's intent. In this work, we propose an environment model that includes the position of the pedestrians as well as their pose information. We only use images from a monocular camera and the vehicle's localization data as input to our pedestrian environment model. We extract the skeletal information with a neural network human pose estimator from the image. Furthermore, we track the skeletons with a simple tracking algorithm based on the Hungarian algorithm and an ego-motion compensation. To obtain the 3D information of the position, we aggregate the data from consecutive frames in conjunction with the vehicle position. We demonstrate our pedestrian environment model on data generated with the CARLA simulator and the nuScenes dataset. Overall, we reach a relative position error of around 16% on both datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
