Abstract
Turning through oncoming traffic at unsignalized intersections can lead to safety-critical situations contributing to 7.4% of all non-severe vehicle crashes. One of the main reasons for these crashes are human errors in the form of incorrect estimation of the gap size with respect to the Principle Other Vehicle (POV). Vehicle-to-vehicle (V2V) technology promises to increase safety in various traffic situations. V2V infrastructure combined with further integration of sensor technology and human intention prediction could help reduce the frequency of these safety-critical situations by predicting dangerous turning manoeuvres in advance, thus, allowing the POV to prepare an appropriate reaction.
We performed a driving simulator study to investigate turning decisions at unsignalized intersections. Over the course of the experiments, we recorded over 5000 turning decisions with respect to different gap sizes. Afterwards, the participants filled out a questionnaire featuring demographic and driving style related items. The behavioural and questionnaire data was then used to fit a Bayesian Network model to predict the turning intention of the subject vehicle. We evaluate the model and present the results of a feature importance analysis. The model is able to correctly predict the turning intention with an accuracy of 74%. Furthermore, the feature importance analysis indicates that user specific information is a valuable contribution to the model. We discuss how a working turning intension prediction could reduce the number of safety-critical situations.
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References
Hamed, M.M., Easa, S.M., Batayneh, R.R.: Disaggregate gap-acceptance model for unsignalized T-intersections. J. Transp. Eng. 123(1), 36–42 (1997)
Harding, J., et al.: Vehicle-to-vehicle communications: readiness of V2V technology for application (No. DOT HS 812 014). National Highway Traffic Safety Administration, United States (2014)
Plavšić, M., Klinker, G., Bubb, H.: Situation awareness assessment in critical driving situations at intersections by task and human error analysis. Hum. Factors Ergon. Manuf. Serv. Ind. 20(3), 177–191 (2010)
Yan, X., Radwan, E., Guo, D.: Effects of major-road vehicle speed and driver age and gender on left-turn gap acceptance. Accid. Anal. Prev. 39(4), 843–852 (2007)
Liu, Y., Ozguner, U.: Human driver model and driver decision making for intersection driving. In: 2007 IEEE Intelligent Vehicles Symposium, pp. 642–647. IEEE, June 2007
De Beaucorps, P., Streubel, T., Verroust-Blondet, A., Nashashibi, F., Bradai, B., Resende, P.: Decision-making for automated vehicles at intersections adapting human-like behavior. In 2017 IEEE Intelligent Vehicles Symposium (IV), pp. 212–217. IEEE, June 2017
Damm, W., Fränzle, M., Lüdtke, A., Rieger, J.W., Trende, A., Unni, A.: Integrating neurophysiological sensors and driver models for safe and performant automated vehicle control in mixed traffic. In: 2019 IEEE Intelligent Vehicles Symposium (IV), pp. 82–89. IEEE. June 2019
Trende, A., Unni, A., Weber, L., Rieger, J.W., Luedtke, A.: An investigation into human-autonomous vs. human-human vehicle interaction in time-critical situations. In: Proceedings of the 12th ACM International Conference on Pervasive Technologies Related to Assistive Environments, pp. 303–304, June 2019
Ihme, K., Unni, A., Zhang, M., Rieger, J.W., Jipp, M.: Recognizing frustration of drivers from face video recordings and brain activation measurements with functional near-infrared spectroscopy. Front. Hum. Neurosci. 12, 327 (2018). https://doi.org/10.3389/fnhum.2018.00327
Ragland, D.R., Arroyo, S., Shladover, S.E., Misener, J.A., Chan, C.Y.: Gap acceptance for vehicles turning left across on-coming traffic: implications for intersection decision support design (2006)
Taubman-Ben-Ari, O., Mikulincer, M., Gillath, O.: The multidimensional driving style inventory—scale construct and validation. Accid. Anal. Prev. 36(3), 323–332 (2004)
Lundberg, S., Lee, S.I.: A unified approach to interpreting model predictions. arXiv preprint arXiv:1705.07874 (2017)
McKenna, F.P., Stanier, R.A., Lewis, C.: Factors underlying illusory self-assessment of driving skill in males and females. Accid. Anal. Prev. 23(1), 45–52 (1991). https://doi.org/10.1016/0001-4575(91)90034-3
Acknowledgment
This work was supported by the DFG-grants RI 1511/3-1 to JWR and LU 1880/3-1 to AT (both “Learning from Humans – Building for Humans”) and FR 2715/4-1 (“Integrated Socio-technical Models for Conflict Resolution and Causal Reasoning”) to MF.
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Trende, A., Unni, A., Rieger, J., Fraenzle, M. (2021). Modelling Turning Intention in Unsignalized Intersections with Bayesian Networks. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2021 - Posters. HCII 2021. Communications in Computer and Information Science, vol 1421. Springer, Cham. https://doi.org/10.1007/978-3-030-78645-8_36
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DOI: https://doi.org/10.1007/978-3-030-78645-8_36
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