Forward Collision Warning Systems—Validating Driving Simulator Results with Field Data
Publication: International Conference on Transportation and Development 2023
ABSTRACT
With the advent of advanced driver assistance systems (ADAS), there is an increasing need to evaluate driver behavior while using such technology. In this unique study, a forward collision warning (FCW) system using connected vehicle technology was introduced in a driving simulator environment, to evaluate driver braking behavior, and then the results are validated using data from field tests. A total of 93 participants were recruited for this study, for which a virtual network of South Baltimore was created. A one sample t-test was conducted, and it was found that the mean reduction in speed of 15.07 mph post-FCW is statistically significant. A random forest machine learning algorithm was found to be the best fit for ranking the most important variables in the data set by order of importance. Field data obtained from the University of Michigan Transportation Research Institute (UMTRI) substantiated the FCW findings from this driving simulator study. Our real-world findings confirmed the conclusions drawn from the simulation experiment as it was also found that the presence of the FCW had a positive and significant impact on the change of speed, which proves the effectiveness of our simulation experiment. Moreover, this field experiment found that the majority of drivers started pressing the brake pedal after receiving the warning, which shows the effectiveness of the FCW system. Thus, from both the simulator and the field tests, it can be said that an FCW system is effective in inducing a speed reduction.
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REFERENCES
Banerjee, S., Jeihani, M., and Khadem, N. K. (2019). “Influence of work zone signage on driver speeding behavior.” Journal of Modern Transportation, 1–9.
Banerjee, S., Jeihani, M., Khadem, N. K., and Brown, D. D. (2019). “Units of information on dynamic message signs: a speed pattern analysis.” European Transport Research Review, 11(1), 15.
Banerjee, S., Jeihani, M., and Moghaddam, R. (2018). “Impact of Mobile Work Zone Barriers on Driving Behavior on Arterial Roads.” Journal of Traffic and Logistics Engineering Vol, 6(2).
Banerjee, S., Jeihani, M., and Moghaddam, R. Z. (2018). “Impact of Mobile Work Zone Barriers on Driving Behavior on Arterial Roads.” Journal of Traffic and Logistics Engineering, Vol. 6(No. 2).
Behbahani, H., Nadimi, N., and Naseralavi, S. (2015). “New time-based surrogate safety measure to assess crash risk in car-following scenarios.” Transportation Letters, 7(4), 229–238.
Bella, F., and Russo, R. (2011). “A collision warning system for rear-end collision: a driving simulator study.” Procedia-social and behavioral sciences, 20, 676–686.
Ben-Yaacov, A., Maltz, M., and Shinar, D. (2002). “Effects of an in-vehicle collision avoidance warning system on short-and long-term driving performance.” Human Factors, 44(2), 335–342.
Bezzina, D., and Sayer, J. (2014). “Safety pilot model deployment: Test conductor team report.”.
Bham, G., Mathur, D., Leu, M., and Vallati, M. (2010). “Younger driver’s evaluation of vehicle mounted attenuator markings in work zones using a driving simulator.” Transportation Letters, 2(3), 187–198.
Breiman, L. (1996). “Bagging predictors.” Machine learning, 24(2), 123–140.
Breiman, L. (2001). “Random forests.” Machine learning, 45(1), 5–32.
Burns, P. C., Knabe, E., and Tevell, M. “Driver behavioral adaptation to collision warning and avoidance information.” Proc., Proceedings of the Human Factors and Ergonomics Society… Annual Meeting, Sage Publications Ltd., 315.
Crump, C., Cades, D., Rauschenberger, R., Hildebrand, E., Schwark, J., Barakat, B., and Young, D. (2015). “Driver Reactions in a Vehicle with Collision Warning and Mitigation Technology.”.
FORUM8. “3D VR & Visual Interactive Simulation.” <http://www.forum8.com/>.
Institute, I. I. (2019). “Facts + Statistics: Highway safety.” <https://www.iii.org/fact-statistic/facts-statistics-highway-safety>. (07/01/2019, 2019).
Jamson, A. H., Lai, F. C., and Carsten, O. M. (2008). “Potential benefits of an adaptive forward collision warning system.” Transportation research part C: emerging technologies, 16(4), 471–484.
Jeihani, M., Banerjee, S., Ahangari, S., and Brown, D. D. (2018). The Potential Effects of Composition and Structure of Dynamic Message Sign Messages on Driver Behavior using a Driving Simulator.
Kockelman, K., Boyles, S., Stone, P., Fagnant, D., Patel, R., Levin, M. W., Sharon, G., Simoni, M., Albert, M., and Fritz, H. (2017). An assessment of autonomous vehicles: traffic impacts and infrastructure needs. University of Texas at Austin. Center for Transportation Research.
Koustanaï, A., Cavallo, V., Delhomme, P., and Mas, A. (2012). “Simulator training with a forward collision warning system: Effects on driver-system interactions and driver trust.” Human factors, 54(5), 709–721.
Kusano, K. D., and Gabler, H. C. (2012). “Safety benefits of forward collision warning, brake assist, and autonomous braking systems in rear-end collisions.” IEEE Transactions on Intelligent Transportation Systems, 13(4), 1546–1555.
Lee, J. D., McGehee, D. V., and Brown, T. L. “Prior Exposure, Warning Algorithm Parameters and Driver Response to Imminent Rear-End Collisions on a High-Sfideltiy Simulator.” Proc., Proceedings of the human factors and ergonomics society annual meeting, SAGE Publications Sage CA: Los Angeles, CA, 3–316-313-319.
Lee, J. D., McGehee, D. V., Brown, T. L., and Reyes, M. L. (2002). Driver distraction, warning algorithm parameters, and driver response to imminent rear-end collisions in a high-fidelity driving simulator.
Lees, M. N., and Lee, J. D. (2007). “The influence of distraction and driving context on driver response to imperfect collision warning systems.” Ergonomics, 50(8), 1264–1286.
Liaw, A., and Wiener, M. (2002). “Classification and regression by randomForest.” R news, 2(3), 18–22.
Maltz, M., and Shinar, D. (2004). “Imperfect in-vehicle collision avoidance warning systems can aid drivers.” Human factors, 46(2), 357–366.
Moghaddam, Z. R., Jeihani, M., Peeta, S., and Banerjee, S. (2019). “Comprehending the roles of traveler perception of travel time reliability on route choice behavior.” Travel Behaviour and Society, 16, 13–22.
Muhrer, E., Reinprecht, K., and Vollrath, M. (2012). “Driving with a partially autonomous forward collision warning system: how do drivers react?” Human factors, 54(5), 698–708.
NACTO. Vehicle Stopping Distance and Time.
Nadimi, N., Ragland, D. R., and Mohammadian Amiri, A. (2019). “An evaluation of time-to-collision as a surrogate safety measure and a proposal of a new method for its application in safety analysis.” Transportation Letters, 1–10.
NHTSA. (2005). Automotive Collision Avoidance System Field Operational Test.
NHTSA. (2013). Preliminary statement of policy concerning automated vehicles. Washington, DC, 1–14.
NTSB. (2015). Addressing Deadly Rear-End Crashes.
Patra, S., Veelaert, P., Calafate, C., Cano, J.-C., Zamora, W., Manzoni, P., and González, F. (2018). “A Forward Collision Warning System for Smartphones Using Image Processing and V2V Communication.” Sensors, 18(8), 2672.
Shaw, F. A., Greenwood, A. T., Bae, J., Corso, G. M., Rodgers, M. O., and Hunter, M. P. (2019). “Effects of roadway factors and demographic characteristics on drivers’ perceived complexity of simulated roadway videos.” Transportation Letters, 11(10), 589–598.
Shinar, D., and Schechtman, E. (2002). “Headway feedback improves intervehicular distance: A field study.” Human factors, 44(3), 474–481.
Information & Authors
Information
Published In
International Conference on Transportation and Development 2023
Pages: 286 - 298
History
Published online: Jun 13, 2023
ASCE Technical Topics:
- Computer networks
- Computing in civil engineering
- Driver behavior
- Engineering fundamentals
- Field tests
- Highway transportation
- Infrastructure
- Internet
- Mathematics
- Methodology (by type)
- Research methods (by type)
- Statistics
- Structural engineering
- Structural systems
- Tests (by type)
- Traffic accidents
- Traffic engineering
- Traffic management
- Transportation engineering
- Validation
- Vehicles
Authors
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