TY - GEN
T1 - Calibration of Controlled Markov Chains for Predicting Pedestrian Crossing Behavior Using Multi-objective Genetic Algorithms
AU - Wu, Jingyuan
AU - Ruenz, Johannes
AU - Althoff, Matthias
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Pedestrian motion prediction is a core issue in assisted and automated driving and challenging to solve. In this work, controlled Markov chains are used for predicting pedestrian crossing behavior in urban environments with and without crosswalks. Intentions, such as crossing a road, are estimated by incorporating the probability of colliding with other traffic participants. On a public dataset, we calibrate the model parameters using genetic algorithms which we formulate as a multi-objective optimization problem. Rather than only minimizing the position deviation of the prediction, we also consider the classification performance for pedestrians' crossing intention. The conducted evaluation shows benefits of our approach: it achieves comparable intention recognition performance compared to a support vector machine, while additionally achieving accurate spatiotemporal predictions.
AB - Pedestrian motion prediction is a core issue in assisted and automated driving and challenging to solve. In this work, controlled Markov chains are used for predicting pedestrian crossing behavior in urban environments with and without crosswalks. Intentions, such as crossing a road, are estimated by incorporating the probability of colliding with other traffic participants. On a public dataset, we calibrate the model parameters using genetic algorithms which we formulate as a multi-objective optimization problem. Rather than only minimizing the position deviation of the prediction, we also consider the classification performance for pedestrians' crossing intention. The conducted evaluation shows benefits of our approach: it achieves comparable intention recognition performance compared to a support vector machine, while additionally achieving accurate spatiotemporal predictions.
UR - http://www.scopus.com/inward/record.url?scp=85076822526&partnerID=8YFLogxK
U2 - 10.1109/ITSC.2019.8917317
DO - 10.1109/ITSC.2019.8917317
M3 - Conference contribution
AN - SCOPUS:85076822526
T3 - 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
SP - 1032
EP - 1038
BT - 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
Y2 - 27 October 2019 through 30 October 2019
ER -