"If You Could See Me through My Eyes": Predicting Pedestrian Perception

Pedestrians are particularly vulnerable road users in urban traffic. With the arrival of autonomous driving, novel technologies can be developed specifically to protect pedestrians. We propose a machine learning toolchain to train artificial neural networks as models of pedestrian behavior. In a pre...

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Bibliographic Details
Main Authors: Petzold, Julian, Wahby, Mostafa, Stark, Franek, Behrje, Ulrich, Hamann, Heiko
Format: Conference Proceeding
Language:English
Subjects:
Behavioral sciences
Data models
intelligent transportation systems
Machine learning
multi-agent systems
Predictive models
Roads
sensor prediction
Training
Visualization
world models
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Summary:Pedestrians are particularly vulnerable road users in urban traffic. With the arrival of autonomous driving, novel technologies can be developed specifically to protect pedestrians. We propose a machine learning toolchain to train artificial neural networks as models of pedestrian behavior. In a preliminary study, we use synthetic data from simulations of a specific pedestrian crossing scenario to train a variational autoencoder and a long short-term memory network to predict a pedestrian's future visual perception. We can accurately predict a pedestrian's future perceptions within relevant time horizons. By iteratively feeding these predicted frames into these networks, they can be used as simulations of pedestrians as indicated by our results. Such trained networks can later be used to predict pedestrian behaviors even from the perspective of the autonomous car. Another future extension will be to re-train these networks with real-world video data.
ISSN:2251-2454
DOI:10.1109/ICCAR55106.2022.9782646