Pedestrian Behavior Prediction for Automated Driving: Requirements, Metrics, and Relevant Features

Automated vehicles require a comprehensive understanding of traffic situations to ensure safe and anticipatory driving. In this context, the prediction of pedestrians is particularly challenging as pedestrian behavior can be influenced by multiple factors. In this paper, we thoroughly analyze the re...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2022-09, Vol.23 (9), p.14922-14937
Main Authors: Herman, Michael, Wagner, Jorg, Prabhakaran, Vishnu, Moser, Nicolas, Ziesche, Hanna, Ahmed, Waleed, Burkle, Lutz, Kloppenburg, Ernst, Glaser, Claudius
Format: Article
Language:English
Subjects:
Ablation
automated driving
Automation
Autonomous vehicles
Driving
machine learning
Mathematical models
Measurement
Pedestrians
Performance measurement
prediction methods
Predictive models
Probabilistic logic
Task analysis
Trajectory
Vehicles
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Summary:Automated vehicles require a comprehensive understanding of traffic situations to ensure safe and anticipatory driving. In this context, the prediction of pedestrians is particularly challenging as pedestrian behavior can be influenced by multiple factors. In this paper, we thoroughly analyze the requirements on pedestrian behavior prediction for automated driving via a system-level approach. To this end we investigate real-world pedestrian-vehicle interactions with human drivers. Based on human driving behavior we then derive appropriate reaction patterns of an automated vehicle and determine requirements for the prediction of pedestrians. This includes a novel metric tailored to measure prediction performance from a system-level perspective. The proposed metric is evaluated on a large-scale dataset comprising thousands of real-world pedestrian-vehicle interactions. We furthermore conduct an ablation study to evaluate the importance of different contextual cues and compare these results to ones obtained using established performance metrics for pedestrian prediction. Our results highlight the importance of a system-level approach to pedestrian behavior prediction.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2021.3135136