TY - GEN
T1 - Probabilistic Map-based Pedestrian Motion Prediction Taking Traffic Participants into Consideration
AU - Wu, Jingyuan
AU - Ruenz, Johannes
AU - Althoff, Matthias
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/18
Y1 - 2018/10/18
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85056781772&partnerID=8YFLogxK
U2 - 10.1109/IVS.2018.8500562
DO - 10.1109/IVS.2018.8500562
M3 - Conference contribution
AN - SCOPUS:85056781772
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 1285
EP - 1292
BT - 2018 IEEE Intelligent Vehicles Symposium, IV 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Intelligent Vehicles Symposium, IV 2018
Y2 - 26 September 2018 through 30 September 2018
ER -