Development of a Driving Behavior-Based Collision Warning System Using a Neural Network
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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 wordsAdvanced 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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- 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
- 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
- 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
- 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
- Korean Road Traffic Authority (2016). Statistical Analysis of Traffic Accidents in the Local Government in 2015. Traffic Accident Analysis Center.Google Scholar
- 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
- National Highway Traffic Safety Administration (2015). Traffic Safety Facts 2014.Google Scholar
- Vector (2010). CANoe. FlexRay 7.2 Product Catalog.Google Scholar