Skip to main content

Factors Influencing Pre-Crash Swerving Responses

Pre-Crash Swerving Responses

Authors: Jeffrey W Muttart

Published on: April 14, 2015

Citation: Muttart, J. (2015). Influence of Age, Secondary Tasks and Other Factors on Drivers’ Swerving Responses before Crash or Near-Crash Events (No. 2015-01-1417). SAE Technical Paper.

Introduction:

The study addresses a critical gap in understanding how drivers execute emergency steering maneuversโ€”specifically swervingโ€”prior to crashes or near-crashes. While controlled studies have identified several factors influencing steering, this research utilizes naturalistic data to determine how variables such as driver age, time-to-contact (TTC), secondary task distraction, lighting, and available buffer space affect swerving performance. The primary purpose was to provide actionable data for crash reconstructionists, vehicle safety designers (e.g., for stability control and assisted steering), and driver trainers. The study hypothesized that drivers under age 25 would show a lack of hazard anticipation, manifesting as impulsive swerving in risky conditions like rain, nighttime, or while distracted, whereas older drivers would swerve less under these circumstances.

Methodology:

The researcher utilized the InSight website to query the 2nd Strategic Highway Research Program (SHRP-2) Naturalistic Driving Study Dataset, which contains data from over 3,000 participants in the United States. The analysis focused specifically on crashes and near-crashes involving conflicts with lead vehicles, oncoming traffic, pedestrians, or obstacles. From an initial query of 73 events, 59 were selected that clearly showed a driver swerving in response to an emergency. Independent variables included driver age, reaction time, TTC, and distractions (categorized as no distraction, visual-only, and visual-manual). Dependent measures included the time to reach the lane edge (TTL), time to cross the lane line (TOL), and time to complete a lane change (L_CHG), alongside lateral acceleration measurements. Statistical significance was determined using a T-distribution with a probability threshold of p < 0.05.

Results:

The study revealed that swerving in response to a slower-moving lead vehicle is the most common emergency maneuver, accounting for 76% of analyzed events. Key findings include:

  • Driver Age and Experience: Drivers younger than 25 were significantly more likely to swerve in risky conditions, such as at night (average age 22.1 vs. 36.8 for daytime) or in the rain. In bad weather, experienced drivers (>25) steered only an average of 2 feet, whereas younger drivers swerved nearly the same distance (6 feet) as they did in clear conditions, indicating a failure to anticipate hazardous road surfaces.
  • The Impact of Distraction: Drivers engaged in visual-manual secondary tasks (e.g., texting, dialing) were significantly less likely to brake efficiently while swerving. This group arrived at the lane edge faster but completed lane changes more slowly, suggesting they steered harder both initially and during the counter-steer phase, which increases the risk of losing control.
  • Time-to-Contact (TTC) Paradox: Counter-intuitively, drivers with less time to react (shorter TTC) took longer to complete a full lane change. While their initial steer was sharper, the resulting hard counter-steer required to stabilize the vehicle extended the total maneuver time.
  • Vehicle Safety Technology: Older drivers (average age 40.5) were more likely to use ABS-equipped vehicles and generally swerved shorter distances (5.0 feet) compared to younger non-ABS users (5.6 feet), despite having greater vehicle capability.
  • Modeling Swerving Time: The research established that the average driver takes approximately 1.0 seconds to reach the lane edge (a 3-foot lateral movement), 2.1 seconds to cross the lane line (8 feet), and 2.5 seconds to complete a full 12-foot lane change.

References

  1. Kaber, D. B, Liang, Y., Zhang, Y., Rogers, M. L, Gangakhedkar, S. (2012). Driver performance effects of simultaneous visual and cognitive distraction and adaptation behavior. Transportation Research Part F: Traffic Psychology and Behaviour, 15(5), 491-501.
  2. Fisher, D. L, Knodler, M., & Muttart, J. (2009). Driver-Eye-Movement-Based Investigation for Improving Work-Zone Safety. Project NETC 04-2. The New England Transportation Consortium.
  3. Muttart, J. W. (2013). Identifying hazard mitigation behaviors that lead to differences in the crash risk between experienced and novice drivers. (Doctoral Dissertation). University of Massachusetts-Amherst.
  4. Muttart, J. W, Fisher, D. L, Pollatsek, A. P, Marquard, J. (2013). Hazard mitigation when approaching curves. 7th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design.
  5. Mayhew, D. R., Simpson, H. M. and Pak, A. (2003). Changes in collision rates among novice drivers during the first months of driving. Accident Analysis and Prevention, 3, 683-91.
  6. Braitman, K. A, Kirley, B. B, McCartt, A. T, and Chaudhary, N. K. (2008). Crashes of Novice Teenage Drivers: Characteristics and Contributing Factors. Journal of Safety Research, 39 (1), 47-54.
  7. Maeda, T., Irie, N., Hidaka, K., and Nishimura, H. (1977). Performance of Driver-Vehicle System in Emergency Avoidance. SAE Technical Paper 770130.
  8. Glennon, J. C, Neuman, T. R, Leisch, J. T. (1986). Safety and operational considerations for design of rural highway curves. Technical Paper DOT-FH-11-9575. Federal Highway Administration.
  9. McGehee, D., Mazzae, E., and Scott Baldwin, G. (1999). Examination of drivers’ collision avoidance behavior using conventional and antilock brake systems on the Iowa driving simulator. NHTSA Light Vehicle Antilock Brake Research Program, Task 5.
  10. Fatal Accident Reporting System. (2011). FARS.gov.
  11. Muttart, J. (2005). Factors that Influence Drivers’ Response Choice Decisions in Video Recorded Crashes. SAE Technical Paper 2005-01-0426.
  12. Araki, K. and Matsuura, Y. (1990). Driver’s Response and Behavior on Being Confronted with a Pedestrian or a Vehicle Suddenly Darting Across the Road. SAE Technical Paper 900144.
  13. Forkenbrock, G. J, Elsasser, D. (2005). An Assessment of Human Driver Steering Capability. Technical paper DOT HS 809 875.
  14. National Highway Transportation System Administration. (2014). onh00/bar8.htm.
  15. Tijerina L, Garrott, W. R, Stoltzfus, D., Parmer, E. (2005). Eye glance behavior of van and passenger car drivers during lane change decision phase. Transportation Research Record 1937, 37-43.
  16. Hetrick, S. (1997). Examination of driver lane change behavior and the potential effectiveness of warning onset rules for lane change or side crash avoidance systems. (Master’s Thesis). Virginia Polytechnic Institute & State University.
  17. Dippel, C., Andrews, M. C. (2005). Highway Lane Change Observations of Tractor-Trailer Vehicles. Texas Association of Accident Reconstruction Specialists.
  18. Reid, L. D, Solowka, E. N. Billing, A. M. (1981). A systematic study of steering behavior. Ergonomics, 24, 447-462.
  19. Chen, X., Wei, Z., Gao, L., Wang, X. (2011). The research on the driver steering behavior under emergency. Trans Tech. Publications.
  20. Rice, R. S, and Dell’Amico F. (1974). An Experimental Study of Automobile Driver Characteristics and Capabilities. Calspan Report ZS-5208-K-1.
  21. Koppa, R, and Hayes, G. (1976). Driver inputs during emergency or extreme vehicle maneuvers. Human Factors, 18(4), 361-370.
  22. Kim, J-H, Hayakawa, S., Suzuki, T., Hayashi, K., Okuma, S. Tsuchida, N., Shimizu, M., Kido, S. (2004). Modeling of driver collision avoidance behaviors based upon piecewise linear model. 43rd IEEE Conference on Decision and Control.
  23. Fitch, G. M, Lee, S. E, Klauer, S., Hankey, J., Sudweeks, J., Dingus, T. (2009). Analysis of lane change crashes and near crashes. NHTSA.
  24. Mazzae, E., Barickman, F., Scott Baldwin, G., and Forkenbrock, G. (1999). Driver Crash Avoidance Behavior with ABS in an Intersection Incursion Scenario on Dry Versus Wet Pavement. SAE Technical Paper 1999-01-1288.
  25. Li, L., Wang, F-Y. (2007). Advanced Motion Control and Sensing for Intelligent Vehicles. Springer-Verlag.
  26. Paul, A., Boyle, L. N, Tippin, J., Rizzo, M. (2005). Variability of driving performance during microsleeps. Proceedings of the Third International Symposium on Human Factors in Driver Assessment, Training and Vehicle Design.
  27. Krajewski, J., Sommer, D., Trutschel, U., Edwards, D., Golz, M. (2009). Steering wheel behavior based estimation of fatigue. Proceedings of the Fifth International Symposium on Human Factors in Driver Assessment, Training and Vehicle Design.
  28. Worrall, R. D, and Bullen, A. G. R. (1970). An Empirical Analysis of Lane Changing on Multilane Highways. Highway Research Board, 303, 30-43.
  29. Transportation Research Board of the National Academies of Science. (2013). The 2nd Strategic Highway Research Program Naturalistic Driving Study Dataset. SHRP 2 NDS InSight Data Dissemination.
  30. Muttart, J. (2003). Development and Evaluation of Driver Response Time Predictors Based upon Meta Analysis. SAE Technical Paper 2003-01-0885.
  31. Muttart, J., Messerschmidt, W., and Gillen, L. (2005). Relationship Between Relative Velocity Detection and Driver Response Times in Vehicle Following Situations. SAE Technical Paper 2005-01-0427.
  32. Fisher, D. L, Knodler, M., & Muttart, J. (2009). Driver-Eye-Movement-Based Investigation for Improving Work-Zone Safety. The New England Transportation Consortium. Project No. NETC 04-2.
  33. Bureau of Transportation Statistics. (2010). Fast Facts.
  34. McKnight, J.A, and McKnight, S.A. (2003). Young novice drivers: Careless or clueless. Accident Analysis and Prevention, 35, 921-925.
  35. Pradhan, A. K, Fisher, D. L, Pollatsek, A., Knodler, M., and Langone, M. (2006). Field Evaluation of a Risk Awareness and Perception Training Program for Younger Drivers. Proceedings of the Human Factors and Ergonomics Society 50th Annual Meeting, 2388-2391.
  36. Fisher, D. L, Pradhan, A. K, Pollatsek, A. and Knodler, M. A. Jr. (2007). Empirical evaluation of hazard anticipation behaviors in the field and on a driving simulator using an eye tracker. Proceedings of the 86th Transportation Research Board Annual Meeting.
  37. Xu, J., Yang, K., Shao, Y., Lu, G. (2014). An experimental study on lateral acceleration of cars in different environments in Sichuan, Southwest China. Discrete Dynamics in Nature and Society, 1-17.
  38. University of Massachusetts. (2015). HPL Software.

Download Study PDF