Pedestrian Behavior Prediction for Automated Driving: Requirements, Metrics, and Relevant Features
| Autor: | Michael Herman, Jorg Wagner, Vishnu Prabhakaran, Nicolas Moser, Hanna Ziesche, Waleed Ahmed, Lutz Burkle, Ernst Kloppenburg, Claudius Glaser |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | IEEE Transactions on Intelligent Transportation Systems. 23:14922-14937 |
| ISSN: | 1558-0016 1524-9050 |
| DOI: | 10.1109/tits.2021.3135136 |
| Popis: | 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. Comment: This work has been submitted to the IEEE Transactions on Intelligent Transportation Systems for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Revision: Extended requirement analysis and evaluation. 16 pages |
| Databáze: | OpenAIRE |
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