This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Analysis of Human Driver Behavior in Highway Cut-in Scenarios
ISSN: 0148-7191, e-ISSN: 2688-3627
Published March 28, 2017 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
The rapid development of driver assistance systems, such as lane-departure warning (LDW) and lane-keeping support (LKS), along with widely publicized reports of automated vehicle testing, have created the expectation for an increasing amount of vehicle automation in the near future. As these systems are being phased in, the coexistence of automated vehicles and human-driven vehicles on roadways will be inevitable and necessary. In order to develop automated vehicles that integrate well with those that are operated in traditional ways, an appropriate understanding of human driver behavior in normal traffic situations would be beneficial.
Unlike many research studies that have focused on collision-avoidance maneuvering, this paper analyzes the behavior of human drivers in response to cut-in vehicles moving at similar speeds. Both automated and human-driven vehicles are likely to encounter this scenario in daily highway driving. This research has identified several possible cut-in scenario configurations that can be experienced on the highway. Data have been collected from a diverse pool of human subjects using a driving simulator with preprogrammed scenarios. To understand each driver’s behavior in response to cut-in vehicles, a novel means of visualization and analysis based on relative positions is proposed in this paper. In addition, the paper provides information on driver decision making when encountering cut-in vehicles. The results could be employed as a set of guidelines for vehicle automation system behavior to ensure that they act in a manner consistent with human-driven vehicles.
- SeHwan Kim - The Ohio State University
- Junmin Wang - The Ohio State University
- Dennis Guenther - The Ohio State University
- Gary Heydinger - The Ohio State University
- Joshua Every - Transportation Research Center Inc
- M. Kamel Salaani - Transportation Research Center Inc
- Frank Barickman - National Highway Traffic Safety Administration
CitationKim, S., Wang, J., Guenther, D., Heydinger, G. et al., "Analysis of Human Driver Behavior in Highway Cut-in Scenarios," SAE Technical Paper 2017-01-1402, 2017, https://doi.org/10.4271/2017-01-1402.
Data Sets - Support Documents
|[Unnamed Dataset 1]|
|[Unnamed Dataset 2]|
|[Unnamed Dataset 3]|
|[Unnamed Dataset 4]|
|[Unnamed Dataset 5]|
- NTSB, “The Use of Forward Collision Avoidance Systems to Prevent and Mitigate Rear-End Crashes,” Special Investigation Report, PB2015-104098, 2015.
- Rau, P., Yanagisawa, M, and Najm, W., “Target Crash Population of Automated Vehicles,” presented at 24th International Technical Conference on the Enhanced Safety of Vehicles, Sweden, June 8-11, 2015.
- Gibson, M., Lee, J., Venkatraman, V., Price, M. et al., "Situation Awareness, Scenarios, and Secondary Tasks: Measuring Driver Performance and Safety Margins in Highly Automated Vehicles," SAE Int. J. Passeng. Cars - Electron. Electr. Syst. 9(1):2016, doi:10.4271/2016-01-0145.
- SAE International Surface Vehicle Information Report, “Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems,” SAE Standard J3016, Rev. Jan. 2014.
- SAE International Surface Vehicle Information Report, “Guidelines for Safe On-Road Testing of SAE Level 3, 4, and 5 Prototype Automated Driving Systems (ADS),” SAE Standard J3018, Rev. Mar. 2015.
- NHTSA, “Federal Automated Vehicle Policy - Accelerating the Next Revolution in Roadway Safety,” Sep. 2016.
- MacAdam, C. C., "Understanding and Modeling the Human Driver," Veh. Syst. Dyn. Int. J. Veh. Mech. Mobility, vol. 40, nos. 1-3, pp. 101-134, 2003, doi:10.1076/vesd.22.214.171.12475.
- Plöchl, M., and Edelmann, J., “Driver Models in Automobile Dynamics Application,” Veh. Syst. Dyn. Int. J. Veh. Mech. Mobility, vol. 45, nos. 7-8, pp. 699-741, 2007, doi:10.1080/00423110701432482.
- Jurecki, R., Stariczyk. T.L., “Driver Model for the Analysis of Pre-Accident Situations,” Veh. Syst. Dyn. Int. J. Veh. Mech. Mobility, vol. 47, pp. 589-612, 2009, doi:10.1080/00423110802276028.
- Hong, T., Kwon, J., Park, K., Lee, K. et al., "Development of a Driver's Intention Determining Algorithm for a Steering System Based Collision Avoidance System," SAE Technical Paper 2013-01-0054, 2013, doi:10.4271/2013-01-0054.
- Zhang, Y., Lin, W.C., and Chin, Y.K.S., "A Pattern-Recognition Approach for Driving Skill Characterization," IEEE Transactions on Intelligent Transportation Systems, vol. 11, no. 4, pp. 905-916, 2010, doi:10.1109/TITS.2010.2055239.
- Wang, C., Zhang, X., Guo, K., Ma, F. et al., "Application of Stochastic Model Predictive Control to Modeling Driver Steering Skills," SAE Int. J. Passeng. Cars - Mech. Syst. 9(1):116-123, 2016, doi:10.4271/2016-01-0462.
- Schnelle, S., Wang, J., Su, H., and Jagacinski, R., “A Driver Steering Model with Personalized Desired Path Generation,” IEEE Transactions on Systems, Man and Cybernetics: Systems, Vol. 47, No. 1, pp. 111-120, 2017, doi:10.1109/TSMC.2016.2529582.
- Jula, H., Kosmatopoulos, E.B., and Ioannou, P.A., "Collision Avoidance Analysis for Lane Changing and Merging," IEEE Transactions on Vehicular Technology, vol. 49, no. 6, pp. 2295-2308, 2000, doi:10.1109/25.901899.
- NHTSA, “Traffic Safety Facts 2012: A Compilation of Motor Vehicle Crash Data from the Fatality Analysis Reporting System and the General Estimates System,” DOT HS 812 032, 2012. URL:https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812032.
- NHTSA, “Forward Collision Warning System Confirmation Test,” Feb. 2013.
- Forkenbrock, G. J., and Snyder, A. S., “NHTSA’s 2014 Automatic Emergency Braking Test Track Evaluations,” NHTSA, DOT HS 812 166, June 2014.
- Gaspar, J., Brown, T., Schwarz, C., Chrysler, S. et al., "Driver Behavior in Forward Collision and Lane Departure Scenarios," SAE Technical Paper 2016-01-1455, 2016, doi:10.4271/2016-01-1455.
- WorldViz Virtual Reality Software, “Vizard Virtual Reality Software,” http://www.worldviz.com/virtual-reality-software-downloads, accessed Nov. 2015.
- Berntorp, K., “Derivation of a Six Degrees-of-Freedom Ground-Vehicle Model for Automotive Applications,” Department of Automotive Control, Lund University, Sweden, Feb. 2013. URL: http://lup.lub.lu.se/record/3459785.
- Pacejka, H. B. and Bakker, E., “The Magic Formula Tyre Model,” Proceedings 1st Int. Colloquium on Tyre Models for Vehicle Dynamics Analysis, vol. 21, pp. 1-18, Delft, Oct. 1991.
- Burgett, A., Carter, A., Miller, R., Najm, W., and Smith, D., “A Collision Warning Algorithm for Rear-End Collisions,” presented at 16th International Technical Conference on Enhanced Safety of Vehicles, Washington, D.C., May 1998.