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Analysis of different visual strategies of 'isolated vehicle' and 'disturbed vehicle'

Abstract

This paper analyses the driver’ visual behaviour in the different conditions of ‘isolated vehicle’ and ‘disturbed vehicle’. If the meaning of the former is clear, the latter condition considers the influence on the driving behaviour of various objects that could be encountered along the road. These can be classified in static (signage, stationary vehicles at the roadside, etc.) and dynamic objects (cars, motorcycles, bicycles). The aim of this paper is to propose a proper analysis regarding the driver’s visual behaviour. In particular, the authors examined the quality of the visually information acquired from the entire road environment, useful for detecting any critical safety condition. In order to guarantee a deep examination of the various possible behaviours, the authors combined the several test outcomes with other variables related to the road geometry and with the dynamic variables involved while driving. The results of this study are very interesting. As expected, they obviously confirmed better performances for the ‘isolated vehicle’ in a rural two-lane road with different traffic flows. Moreover, analysing the various scenarios in the disturbed condition, the proposed indices allow the authors to quantitatively describe the different influence on the visual field and effects on the visual behaviour, favouring critical analysis of the road characteristics. Potential applications of these results may contribute to improve the choice of the best maintenance strategies for a road, to select the optimal signage location, to define forecasting models for the driving behaviour and to develop useful instruments for intelligent transportation systems.


First Published Online: 4 Sept 2017

Keyword : visual behaviour, road safety, isolated vehicle, disturbed vehicle, driving behaviour, traffic

How to Cite
Bongiorno, N., Bosurgi, G., Pellegrino, O., & Sollazzo, G. (2017). Analysis of different visual strategies of ’isolated vehicle’ and ’disturbed vehicle’. Transport, 33(3), 853-860. https://doi.org/10.3846/16484142.2017.1343750
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Jan 4, 2017
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This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Bongiorno, N.; Bosurgi, G.; Pellegrino, O. 2016. A procedure for evaluating the influence of road context on drivers’ visual behaviour, Transpor t 31(2): 233–241. https://doi.org/10.3846/16484142.2016.1188852

Bosurgi, G.; D’Andrea, A.; Pellegrino, O. 2015. Prediction of drivers’ visual strategy using an analytical model, Journal of Transportation Safety & Security 7(2): 153–173. https://doi.org/10.1080/19439962.2014.943866

Bosurgi, G.; D’Andrea, A.; Pellegrino, O. 2010. Could drivers’ visual behaviour influence road design?, Advances in Transportation Studies: an International Journal 22: 17–30.

Bosurgi, G.; D’Andrea, A.; Pellegrino, O. 2013. What variables affect to a greater extent the driver‘s vision while driving?, Transport 28(4): 331–340. https://doi.org/10.3846/16484142.2013.864329

Dijksterhuis, C.; Brookhuis, K.A.; De Waard, D. 2011. Effects of steering demand on lane keeping behaviour, self-reports, and physiology. A simulator study, Accident Analysis & Prevention 43(3): 1074–1081. https://doi.org/10.1016/j.aap.2010.12.014

Donges, E. 1978. A Two-level model of driver steering behavior, Human Factors: the Journal of the Human Factors and Ergonomics Society 20(6): 691–707.

Habibovic, A.; Tivesten, E.; Uchida, N.; Bärgman, J.; Aust, M. L. 2013. Driver behavior in car-to-pedestrian incidents: an application of the driving reliability and error analysis method (DREAM), Accident Analysis & Prevention 50: 554–565. https://doi.org/10.1016/j.aap.2012.05.034

Jamson, A. H.; Merat, N.; Carsten, O. M. J.; Lai, F. C. H. 2013. Behavioural changes in drivers experiencing highly-automated vehicle control in varying traffic conditions, Transportation Research Part C: Emerging Technologies 30: 116–125. https://doi.org/10.1016/j.trc.2013.02.008

Land, M. F 2010. The visual control of steering, in L. R. Harris, M. Jenkin (Eds.). Vision and Action, 163–180.

Land, M. F. 2006. Eye movements and the control of actions in everyday life, Progress in Retinal and Eye Research 25(3): 296–324. https://doi.org/10.1016/j.preteyeres.2006.01.002

Lehtonen, E.; Lappi, O.; Kotkanen, H.; Summala, H. 2013. Look-ahead fixations in curve driving, Ergonomics 56(1): 34–44. https://doi.org/10.1080/00140139.2012.739205

Martens, M. H. 2011. Change detection in traffic: Where do we look and what do we perceive?, Transportation Research Part F: Traffic Psychology and Behaviour 14(3): 240–250. https://doi.org/10.1016/j.trf.2011.01.004

Minin, L.; Benedetto, S.; Pedrotti, M.; Re, A.; Tesauri, F. 2012. Measuring the effects of visual demand on lateral deviation: A comparison among driver’s performance indicators, Applied Ergonomics 43(3): 486–492. https://doi.org/10.1016/j.apergo.2011.08.001

Pellegrino, O. 2009. An analysis of the effect of roadway design on driver’s workload, The Baltic Journal of Road and Bridge Engineering 4(2): 45–53. https://doi.org/10.3846/1822-427X.2009.4.45-53

Pellegrino, O. 2012. Prediction of driver’s workload by means of fuzzy techniques, The Baltic Journal of Road and Bridge Engineering 7(2): 120–128. https://doi.org/10.3846/bjrbe.2012.1

Wege, C.; Will, S.; Victor, T. 2013. Eye movement and brake reactions to real world brake-capacity forward collision warnings: a naturalistic driving study, Accident Analysis &Prevention 58: 259–270. https://doi.org/10.1016/j.aap.2012.09.013