Vehicle–pedestrian interactions into and outside of crosswalks: effects of driver assistance systems
Abstract
This study aimed to analyse the driver’s behaviour during the interaction with a pedestrian crossing into and outside the zebra crossing, and evaluate the effectiveness of two kinds of Advanced Driver Assistance System (ADAS) that provided to the driver an auditory alert, and a visual alert to detect the pedestrian. 42 participants joined the experiment conducted using the fixed-base driving simulator of the Department of Engineering (Roma Tre University). They experienced different crossing conditions (legal and illegal) and ADAS conditions (no ADAS, visual warning and auditory warning) in an urban scenario. The parameters Time-To-Arrive (TTA) and Speed Reduction Time (SRT) were obtained from the drivers’ speed profiles in the last 150 m in advance of the conflict point with the pedestrian. Results clearly showed the criticality of illegal crossings. When the pedestrian crossed outside of the crosswalk, the highest number of collision occurred and the ANalysis Of VAriance (ANOVA) returned significant effects on both the dependent variables TTA and SRT, highlighting the higher criticality of the vehicle–pedestrian interaction and the more abrupt yielding manoeuvre. Positive effects (the vehicle–pedestrian interaction was less critical and the yielding manoeuvre was smoother) emerged for both the driver assistance systems, although not statistically significant. Besides, both the driver assistance systems positively affected the behaviour of the average cautious drivers. No significant effects of the warning systems were recorded on the aggressive drivers, which because of their behavioural characteristics ignored the warning alarm. In addition, no significant effects of the warning systems were recorded for the very cautious drivers, which adjusted their behaviour even before the alarm trigger. Finally, the outcomes of the questionnaire submitted to the participants highlighted the clear preference for the auditory warning, probably because of the different physical stimuli that are solicited by the warning signal. The results confirm that adequate pedestrian paths should be planned to avoid jaywalker conditions, which induce the driver to assume critical driving behaviour and provide useful findings of the effectiveness of driver assistance systems for pedestrian detection.
Keyword : pedestrian, driver’s behaviour, driving simulator, ADAS, road safety, human factors
How to Cite
Bella, F., & Silvestri, M. (2021). Vehicle–pedestrian interactions into and outside of crosswalks: effects of driver assistance systems. Transport, 36(2), 98-109. https://doi.org/10.3846/transport.2021.14739
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Geronimo, D.; Sappa, A. D.; Ponsa, D.; Lopez, A. M. 2010. 2D–3D-based on-board pedestrian detection system, Computer Vision and Image Understanding 114(5): 583–595. https://doi.org/10.1016/j.cviu.2009.07.008
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MiMS. 2001. Decreto Ministeriale del 5/11/2001 Norme funzionali e geometriche per la costruzione delle strade. Ministero delle infrastrutture e della mobilita sostenibili (MiMS), Roma, Italia. (in Italian).
MiMS. 1992. Nuovo codice della strada, Decreto Legislativo 30 aprile 1992 N. 285 e successive modificazioni. Ministero delle infrastrutture e della mobilita sostenibili (MiMS), Roma, Italia. (in Italian).
Mitman, M. F.; Cooper, D.; Dubose, B. 2010. Driver and pedestrian behavior at uncontrolled crosswalks in Tahoe basin recreation area of California, Transportation Research Record: Journal of the Transportation Research Board 2198: 23–31. https://doi.org/10.3141/2198-04
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Obeid, H.; Abkarian, H.; Abou-Zeid, M.; Kaysi, I. 2017. Analyzing driver–pedestrian interaction in a mixed-street environment using a driving simulator, Accident Analysis & Prevention 108: 56–65. https://doi.org/10.1016/j.aap.2017.08.005
Petzoldt, T. 2014. On the relationship between pedestrian gap acceptance and time to arrival estimates, Accident Analysis & Prevention 72: 127–133. https://doi.org/10.1016/j.aap.2014.06.019
Ryan, A.; Casola, E.; Fitzpatrick, C.; Knodler, M. 2019. Flashing yellow arrows for right turn applications: A driving simulator study and static evaluation analysis, Transportation Research Part F: Traffic Psychology and Behaviour 66: 324–338. https://doi.org/10.1016/j.trf.2019.09.013
Scott, J. J.; Gray, R. 2008. A comparison of tactile, visual, and auditory warnings for rear-end collision prevention in simulated driving, Human Factors: the Journal of the Human Factors and Ergonomics Society 50(2): 264–275. https://doi.org/10.1518/001872008x250674
Shaaban, K.; Muley, D.; Mohammed, A. 2018. Analysis of illegal pedestrian crossing behavior on a major divided arterial road, Transportation Research Part F: Traffic Psychology and Behaviour 54: 124–137. https://doi.org/10.1016/j.trf.2018.01.012
Shaaban, K.; Muley, D.; Mohammed, A. 2019. Modeling pedestrian gap acceptance behavior at a six-lane urban road, Journal of Transportation Safety & Security. https://doi.org/10.1080/19439962.2019.1691100
Spicer, R.; Vahabaghaie, A.; Bahouth, G.; Drees, L.; Von Bulow, R. M.; Baur, P. 2018. Field effectiveness evaluation of advanced driver assistance systems, Traffic Injury Prevention 19: S91–S95. https://doi.org/10.1080/15389588.2018.1527030
Tezcan, H. O.; Elmorssy, M.; Aksoy, G. 2019. Pedestrian crossing behavior at midblock crosswalks, Journal of Safety Research 71: 49–57. https://doi.org/10.1016/j.jsr.2019.09.014
Toxopeus, R.; Attalla, S.; Kodsi, S.; Oliver, M. 2018. Driver response time to midblock crossing pedestrians, SAE Technical Paper 2018-01-0514. https://doi.org/10.4271/2018-01-0514
Tornros, J. 1998. Driving behaviour in a real and a simulated road tunnel – a validation study, Accident Analysis & Prevention 30(4): 497–503. https://doi.org/10.1016/S0001-4575(97)00099-7
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