Probabilistic Map-based Pedestrian Motion Prediction Taking Traffic Participants into Consideration

Jingyuan Wu, Johannes Ruenz, Matthias Althoff

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

32 Scopus citations

Abstract

As pedestrians are one of the most vulnerable traffic participants, their motion prediction is of utmost importance for intelligent transportation systems. Predicting motions of pedestrians is especially hard since they move in less structured environments and have less inertia compared to road vehicles. To account for this uncertainty, we present an approach for probabilistic prediction of pedestrian motion using Markov chains. In contrast to previous work, we not only consider motion models, constraints from a semantic map, and various goals, but also explicitly adapt the prediction based on crash probabilities with other traffic participants. Also, our approach works in any situation; this is typically challenging for pure machine learning techniques that learn behaviors for a particular road section and which might consequently struggle with a different road section. The usefulness of combining the aforementioned aspects in a single approach is demonstrated by an evaluation using recordings of real pedestrians.

Original languageEnglish
Title of host publication2018 IEEE Intelligent Vehicles Symposium, IV 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1285-1292
Number of pages8
ISBN (Electronic)9781538644522
DOIs
StatePublished - 18 Oct 2018
Event2018 IEEE Intelligent Vehicles Symposium, IV 2018 - Changshu, Suzhou, China
Duration: 26 Sep 201830 Sep 2018

Publication series

NameIEEE Intelligent Vehicles Symposium, Proceedings
Volume2018-June

Conference

Conference2018 IEEE Intelligent Vehicles Symposium, IV 2018
Country/TerritoryChina
CityChangshu, Suzhou
Period26/09/1830/09/18

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