Pedestrian Environment Model for Automated Driving

• URL: http://arxiv.org/abs/2308.09080v1
• Date: Thu, 17 Aug 2023 16:10:58 GMT
• Title: Pedestrian Environment Model for Automated Driving
• Authors: Adrian Holzbock, Alexander Tsaregorodtsev, and Vasileios Belagiannis
• Abstract summary: We propose an environment model that includes the position of the pedestrians as well as their pose information. We extract the skeletal information with a neural network human pose estimator from the image. To obtain the 3D information of the position, we aggregate the data from consecutive frames in conjunction with the vehicle position.
• Score: 54.16257759472116
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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