Dissemination

This page will be updated with relevant results when available.

Scientific Publications

Berge, S. H., Hagenzieker, M., Farah, H., & de Winter, J. C. F. (2021). Do cyclists need HMIs in future automated traffic? An interview study. Manuscript submitted for review. Preprint available at: https://www.researchgate.net/publication/351229876_Do_cyclists_need_HMIs_in_future_automated_traffic_An_interview_study

Muhammad, A. P. (2021). Methods and Guidelines for Incorporating Human Factors Requirements in Automated Vehicles Development. In REFSQ Workshops. http://ceur-ws.org/Vol-2857/ds1.pdf 

Peng, C., Merat, N., Romano, R., Hajiseyedjavadi, F., Paschalidis, E., Wei, C., Radhakrishnan, V., Solernou, A., Forster, D., Boer, E. (2021). Drivers’ Evaluation of Different Automated Driving Styles: Is It both Comfortable and Natural? [Manuscript submitted for publication]. preprint:https://www.researchgate.net/publication/351786327_Drivers’_Evaluation_of_Different_Automated_Driving_Styles_Is_It_both_Comfortable_and_Natural

Tabone, W., De Winter, J. C. F., Ackermann, C., Bärgman, J., Baumann, M., Deb, S., Emmenegger, C., Habibovic, A., Hagenzieker, M., Hancock, P. A., Happee, R., Krems, J., Lee, J. D., Martens, M., Merat, N., Norman, D. A., Sheridan, T. B., & Stanton, N. A. (2021). Vulnerable road users and the coming wave of automated vehicles: Expert perspectives. Transportation Research Interdisciplinary Perspectives, 9, 100293. https://doi.org/10.1016/j.trip.2020.100293

Tabone, W., Lee, Y.M., Merat, N., Happee, R., & De Winter, J.C.F. (2021). Towards future pedestrian-vehicle interactions: Introducing theoretically-supported AR prototypes. Manuscript submitted for review. 

Zhang, C., Berger, C., & Dozza, MP(2020). Towards Understanding Pedestrian Behavior Patterns from LiDAR Data. SAIS Workshop 2020. http://www.chalmers.se/SiteCollectionDocuments/Centrum/CHAIR/SAIS2020/SAIS_2020_Zhang_et_al.pdf

Zhang, C., Berger, C., & Dozza, M. (2021). Social-IWSTCNN: A Social Interaction-Weighted Spatio-Temporal Convolutional Neural Network for Pedestrian Trajectory Prediction in Urban Traffic Scenarios. https://arxiv.org/abs/2105.12436 . Accepted by 2021 IEEE Intelligent Vehicle Symposium (IV)

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Scientific Presentations

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SHAPE-IT Deliverables

D1.1 Methodological framework for the modelling and empirical approaches

D2.1 An overview of current AV interface strategies

Other material

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