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.

使用单目相机图像和车辆定位数据作为输入，我们提出了一个环境模型，该模型包括行人的位置和姿态信息。通过神经网络人体姿势估计器从图像中提取骨架信息，并通过基于匈牙利算法和自车运动补偿的简单跟踪算法跟踪骨架。为了获得位置的三维信息，我们结合车辆位置从连续帧中聚合数据。我们在 CARLA 模拟器和 nuScenes 数据集上展示了我们的行人环境模型，相对位置误差均达到约 16%。