Enhancing JAAD with Knowledge Graphs for Improved Pedestrian Crossing Predictions
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-67868-4_21
Reference19 articles.
1. Abughalieh, K.M., Alawneh, S.G.: Predicting pedestrian intention to cross the road. IEEE Access 8, 72558–72569 (2020). https://doi.org/10.1109/ACCESS.2020.2987777. Conference Name: IEEE Access
2. Brouwer, N., Kloeden, H., Stiller, C.: Comparison and evaluation of pedestrian motion models for vehicle safety systems. In: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pp. 2207–2212, November 2016. https://doi.org/10.1109/ITSC.2016.7795912
3. Cole, V., Boutet, M.: ResearchRabbit. J. Can. Health Libr. Assoc. 44(2), 43 (2023)
4. Fang, Z., López, A.M.: Is the pedestrian going to cross? Answering by 2D pose estimation. In: 2018 IEEE Intelligent Vehicles Symposium (IV), pp. 1271–1276, June 2018. https://doi.org/10.1109/IVS.2018.8500413
5. Fuest, T., Schmidt, E., Bengler, K.: Comparison of methods to evaluate the influence of an automated vehicle’s driving behavior on pedestrians: wizard of OZ, virtual reality, and video. Information 11(6), 291 (2020). https://doi.org/10.3390/info11060291. https://www.mdpi.com/2078-2489/11/6/291. Number: 6 Publisher: Multidisciplinary Digital Publishing Institute
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