Citation

Goldhammer M, Köhler S, Zernetsch S, Doll K, Sick B, Dietmayer K. IEEE Trans. Intel. Transp. Syst. 2020; 21(7): 3035-3045.

Copyright

(Copyright © 2020, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2019.2923319

PMID

unavailable

Abstract

Avoiding collisions with vulnerable road users (VRUs) using sensor-based early recognition of critical situations is one of the manifold opportunities provided by the current development in the field of intelligent vehicles. As, especially, pedestrians and cyclists are very agile and have a variety of movement options, modeling their behavior in traffic scenes becomes a challenging task. In this paper, we propose movement models based on machine learning methods, in particular, artificial neural networks, in order to classify the current motion state and to predict the future trajectory of the VRUs. Both model types are also combined to enable the application of specifically trained motion predictors based on a continuously updated pseudo probabilistic state classification. Furthermore, the architecture is used to evaluate motion-specific physical models for starting and stopping and video-based pedestrian motion classification. A comprehensive dataset consisting of a total of 1068 pedestrian and 494 cyclist scenes acquired at an urban intersection is used for optimization, training, and evaluation of the different models. The results show substantially higher classification rates and the ability, through the machine learning approaches, to earlier recognize motion state changes than by the way of interacting multiple model (IMM) Kalman filtering. The trajectory prediction quality has also been improved for all kinds of test scenes, especially when starting and stopping motions are included. Here, 37% and 41% fewer position errors were achieved on average, respectively.

Language: en