Pedestrian Environment Model for Automated Driving
This work addresses the safety challenge for automated driving systems in interpreting pedestrian intent, though it is incremental as it builds on existing pose estimation and tracking methods.
The paper tackled the problem of enabling automated vehicles to safely interact with pedestrians by interpreting their behavior, proposing an environment model that incorporates pedestrian pose information from monocular camera images and vehicle localization, achieving a relative position error of around 16% on CARLA simulator and nuScenes dataset data.
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.