Collision Prevention While Driving in Real Traffic Flow Using Emotional Learning Fuzzy Inference Systems

Event
SAE 2013 World Congress & Exhibition
Authors Abstract
Content
This paper proposes a methodology for collision prevention in car following scenarios. For this purpose, Emotional Learning Fuzzy Inference System (ELFIS) approach is used to simulate and predict the behavior of a driver-vehicle-unit in a short time horizon ahead in the future. Velocity of the follower vehicle and relative distance between the follower and the lead vehicles are predicted in a parallel structure. Performance of the proposed algorithm is assessed using real traffic data and superior accuracy of this method is demonstrated through comparisons with another available technique (ANFIS). The predicted future driving states are then used to judge about safety of the current driving pattern. The algorithm is used to generate a warning message while a safe-distance keeping measure is violated in order to prevent a collision. Satisfactory performance of the proposed method is demonstrated through simulations using real traffic data. The proposed method can be applied, in real time, for a variety of applications including driver assistant and collision prevention systems as well as other intelligent transportation applications.
Meta TagsDetails
DOI
https://doi.org/10.4271/2013-01-0623
Pages
6
Citation
Zarringhalam, R., and Ghaffari, A., "Collision Prevention While Driving in Real Traffic Flow Using Emotional Learning Fuzzy Inference Systems," Transportation Safety 1(1):58-63, 2013, https://doi.org/10.4271/2013-01-0623.
Additional Details
Publisher
Published
Apr 8, 2013
Product Code
2013-01-0623
Content Type
Journal Article
Language
English