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International Journal of Automotive Technology

, Volume 19, Issue 5, pp 837–844 | Cite as

Development of a Driving Behavior-Based Collision Warning System Using a Neural Network

  • Sang Hyeop Lee
  • Suk Lee
  • Man Ho Kim
Article
  • 19 Downloads

Abstract

An advanced driver assistance system (ADAS) uses radar, visual information, and laser sensors to calculate variables representing driving conditions, such as time-to-collision (TTC) and time headway (THW), and to determine collision risk using empirically set thresholds. However, the empirically set threshold can generate differences in performance that are detected by the driver. It is appropriate to quickly relay collision risk to drivers whose response speed to dangerous situations is relatively slow and who drive defensively. However, for drivers whose response speed is relatively fast and who drive actively, it may be better not to provide a warning if they are aware of the collision risk in advance, because giving collision warnings too frequently can lower the reliability of the warnings and cause dissatisfaction in the driver, or promote disregard. To solve this problem, this study proposes a collision warning system (CWS) based on an individual driver’s driving behavior. In particular, a driver behavior model was created using an artificial neural network learning algorithm so that the collision risk could be determined according to the driving characteristics of the driver. Finally, the driver behavior model was learned using actual vehicle driving data and the applicability of the proposed CWS was verified through simulation.

Key words

Advanced Driver Assistance System (ADAS) Autonomous Emergency Braking System (AEBS) Collision potential identifier Collision Warning System (CWS) Driver behavior model Neural network Time-To-Collision (TTC) 

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References

  1. Ali, G., Alreza, K. and Maysam, F. (2014). Optimal fuzzy control system design for car-following behaviour based on the driver–vehicle unit online delays in a real traffic flow. Proc. Institution of Mechanical Engineers, Part D: J. Automobile Engineering 228, 12, 1440–1451.Google Scholar
  2. Cao, M. S., Ding, Y. J., Ren, W. X., Wang, Q., Ragulskis, M. and Ding, Z. C. (2017). Hierarchical wavelet-aided neural intelligent identification of structural damage in noisy conditions. Applied Sciences 7, 4, 391–411.CrossRefGoogle Scholar
  3. Choi, J. W., Yi, K. S., Suh, J. Y. and Ko, B. C. (2014). Coordinated control of motor-driven power steering torque overlay and differential braking for emergency driving support. IEEE Trans. Vehicular Technology 63, 2, 566–579.CrossRefGoogle Scholar
  4. Fu, X., Li, S. and Jaithwa, I. (2015). Implement optimal vector control for LCL-filter-based grid-connected converters by using recurrent neural networks. IEEE Trans. Industrial Electronics 62, 7, 4443–4454.CrossRefGoogle Scholar
  5. Hummel, T., Kuhn, M., Bende, J. and Lang, A. (2015). Advanced driver assistance systems. German Insurance Association Insurers Accident Research 6, 1, 1–63.Google Scholar
  6. Kim, H. J. (2016). Design and Evaluation of Alert Threshold for Takeover Request in Partial Autonomous Vehicles considering Human Factors. M. S. Thesis. Kookmin University. Seoul, Korea.Google Scholar
  7. Kim, M. H. and Son, J. W. (2011). On-road assessment of in-vehicle driving workload for older drivers: Design guidelines for intelligent vehicle. Int. J. Automotive Technology 12, 2, 265–272.CrossRefGoogle Scholar
  8. Kim, M. H., Lee, S., Ha, K. N. and Lee, K. C. (2013). Implementation of a fuzzy-inference-based low-speed, close-range collision warning system for the urban area. Proc. Institution of Mechanical Engineers, Part D: J. Automobile Engineering 227, 2, 234–245.Google Scholar
  9. Korean Road Traffic Authority (2016). Statistical Analysis of Traffic Accidents in the Local Government in 2015. Traffic Accident Analysis Center.Google Scholar
  10. Lee, M. S. and Jeong, H. Y. (2014). Driver propensity characterization for different forward collision warning times. Int. J. Automotive Technology 15, 6, 927–936.CrossRefGoogle Scholar
  11. Lee, S. H., Lee, S., Lee, K. C. and Kim, M. H. (2017). Analytical hybrid redundancy system for the fault tolerance of advanced driver assistance systems. Proc. Institution of Mechanical Engineers, Part D: J. Automobile Engineering 231, 12, 1660–1665.Google Scholar
  12. Martínez-morales, J. D., Palacios-hernández, E. R. and Velázquez-carrillo, G. A. (2015). Modeling engine fuel consumption and Nox with RBF neural network and mopso algorithm. Int. J. Automotive Technology 16, 6, 1041–1049.CrossRefGoogle Scholar
  13. Min, S. K. and Lee, K. S. (2003). Driver adaptive control algorithm for intelligent vehicle. Trans. Korean Society of Mechanical Engineers A 27, 7, 1146–1151.CrossRefGoogle Scholar
  14. National Highway Traffic Safety Administration (2015). Traffic Safety Facts 2014.Google Scholar
  15. Vector (2010). CANoe. FlexRay 7.2 Product Catalog.Google Scholar
  16. Wang, J., Wang, Q. N., Zeng, X. H., Wang, P. Y. and Wang, J. N. (2015). Driving cycle recognition neural network algorithm based on the sliding time window for hybrid electric vehicles. Int. J. Automotive Technology 16, 4, 685–695.CrossRefGoogle Scholar
  17. Wang, J., Zhang, L., Zhang, D. and Li, K. (2013). An adaptive longitudinal driving assistance system based on driver characteristics. IEEE Trans. Intelligent Transportation Systems 14, 1, 1–12.CrossRefGoogle Scholar

Copyright information

© The Korean Society of Automotive Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mechanical EngineeringPusan National UniversityBusanKorea
  2. 2.Division of Automotive EngineeringDong-eui Institute of TechnologyBusanKorea

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