DRIVER RESPOND TO PRE-COLLISION SCENARIO AT INTERSECTION IN AUTONOMOUS VEHICLE
Abstract
Research on automation system development is attracting a lot of attention from researchers, automotive industry manufacturers and leading technology brands. This study focus is on SAE level 3, the vehicle steering, accelerator pedal and brake pedal are controlled autonomously. The decrement in controlling vehicle and driving task has the possibility to reduce the road crash resulted in an essential change in driver role from active to passive. The effect of role change leads to decrement of situation awareness and reduce driver abilities to control manual vehicle at the right time and manner. Therefore, research on recognition times in complex and actual situations are critical. The primary purpose was to analyze the driver’s ability to recognize pedestrian, bicycle and vehicle pre-collision at intersection in the automated vehicle. The road conditions were complicated and imitated a real driving scenario. The statistical tools used for analysis were the F-test, t-test and ANOVA method. This finding shows that the subject can instantaneously recognize unintended acceleration at a low velocity and relative velocity in a pre-collision scenario with pedestrian. The implication of these results is in developing an automated vehicle system related to driver recognition. These findings provide insights that can be useful in developing autonomous vehicles.
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References
S. Yi, C. Kang, S. Lee and C. Chung, "Vehicle trajectory prediction for adaptive cruise control", in 2015 IEEE Intelligent Vehicles Symposium (IV), Seoul, Korea, 2015, pp. 59-64.
N. Merat, A. Jamson, F. Lai, M. Daly and O. Carsten, "Transition to manual: Driver behavior when resuming control from a highly automated vehicle", Transportation Research Part F: Traffic Psychology and Behavior, vol. 27, pp. 274-282, 2014.
J. M. Anderson, K. Nidhi, K. D. Stanley, P. Sorensen, C. Samaras and O. A. Oluwatola, Autonomous Vehicle Technology. A Guide for Policymakers. California: RAND Corporation, 2016.
D. Dambock, “Requirements for cooperative vehicle guidance,” in 16th International IEEE Conference on Intelligent Transportation Systems (ITSC), Hague, Netherlands, 2013 ,pp. 1656-1661.
N. Merat, A.H. Jamson, F.C. Lai and O. Carsten, “Highly automated driving, secondary task performance, and driver state,” Human Factors, vol. 54, no. 5, pp. 762-771, 2012.
M.R. Endsley, “Toward a theory of situation awareness in dynamic systems”, Human Factors, vol. 31, no. 1, pp. 32-64, 1995.
D.B. Kaber and M.R. Endsley, “The effects of level of automation and adaptive automation on human performance, situation awareness and workload in a dynamic control task”, Theoretical Issues in Ergonomics Science, vol. 5, no. 2, pp. 113-153, 2004.
C. Gold, D. DamboÈck, L. Lorenz and K. Bengler, “Take over: How long does it take to get the driver back into the loop?”, in the Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Los Angeles, CA, 2013, pp. 1938 – 1942.
T. Louw, G. Kountouriotis, O. Carsten and N. Merat, “Driver Inattention during Vehicle Automation: How Does Driver Engagement Affect Resumption of Control?” in 4th International Conference on Driver Distraction and Inattention, Sydney, 2015, pp. 1-14.
T. Louw, R. Madigan, O. Carsten and N. Merat, “Were they in the loop during automated driving? Links between visual attention and crash potential”, Injury Prevention, vol. 23, no. 4, pp. 281-286, 2017.
K. Zeeb, A. Buchner and M. Schrauf, “What determines the take-over time? An integrated model approach of driver take-over after automated driving”, Accident Analysis & Prevention, vol. 78, pp. 212-221, 2015.
D. De Waard, “The measurement of drivers' mental workload” Ph.D. Thesis, University of Groningen, Haren, Netherlands, 1996.
R. Hoeger, A. Amditis, M. Kunert and A. Hoess, “Highly automated vehicles for intelligent transport: HAVEit approach”, 15th World Congress on Intelligent Transport System and ITS America’s 2008 Annual Meeting, New York, USA, 2008, pp. 3186-3195.
Y. Barnard and F. Lai, “Spotting sheep in Yorkshire: Using eye-tracking for studying situation awareness in a driving simulator”, in D. de Waard, A. Axelsson, M. Berglund, B. Peters and C. Weikert, Human Factors: A system view of human, technology and organization. Maastricht: Shaker Publishing, 2010, pp. 249-261.
O. Carsten, F.C. Lai, Y. Barnard, A.H. Jamson and N. Merat, “Control Task Substitution in Semi-automated Driving: Does It Matter What Aspects Are Automated? “, Human Factors, vol. 54, no. 5, pp. 747-761, 2012.
A.H. Jamson, N. Merat, O.M. Carsten and F.C. Lai, “Behavioral changes in drivers experiencing highly-automated vehicle control in varying traffic conditions”, Transportation Research Part C: Emerging Technologies, vol. 30, pp. 116-125, 2013.
H. Jamson, N. Merat, O. Carsten and F.C.H. Lai, “Fully-automated driving: The road to future vehicles”, in 6th International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, California, USA, 2011, pp. 2-9.
N. Merat, and A.H. Jamson, “How do drivers behave in a highly-automated car?”, in 5th International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, Montana, USA, 2009, pp. 514-521.
E. Muhrer, K. Reinprecht and M. Vollrath, “Driving with a Partially Autonomous Forward Collision Warning System: How Do Drivers React?”, Human Factors, vol. 54, no. 5, pp. 698-708, 2012.
F.M.F. Verberne, J. Ham and C.J.H. Midden, “Trust in Smart Systems: Sharing Driving Goals and Giving Information to Increase Trustworthiness and Acceptability of Smart Systems in Cars”, Human Factors, vol. 54, no. 5, pp. 799-810, 2012.
N.A. Stanton, and M.S. Young, “A proposed psychological model of driving automation”, Theoretical Issues in Ergonomics Science, vol. 1, no. 4, pp. 315-331, 2000.
N.H. Mokhtar, H. Takada and Y. Matsuura, "Driver reaction towards unintended acceleration in automated and silent vehicle," International Journal of Affective Engineering, vol. 16, no. 1, pp. 43–51, 2017.
