A forward collision avoidance algorithm based on driver braking behavior

Xiaoxia Xiong, Meng Wang, Yingfeng Cai, Long Cheng, Haneen Farah, Marjan Hagenzieker

Research output: Contribution to journalArticleScientificpeer-review

12 Citations (Scopus)
38 Downloads (Pure)


Measuring risk is critical for collision avoidance. The paper aims to develop an online risk level classification algorithm for forward collision avoidance systems. Assuming risk levels are reflected by braking profiles, deceleration curves from critical evasive braking events from the Virginia “100-car” database were first extracted. The curves are then clustered into different risk levels based on spectrum clustering, using curve distance and curve changing rate as dissimilarity metrics among deceleration curves. Fuzzy logic rules of safety indicators at critical braking onset for risk classification were then extracted according to the clustered risk levels. The safety indicators include time to collision, time headway, and final relative distance under emergency braking, which characterizes three kinds of uncertain critical conditions respectively. Finally, the obtained fuzzy risk level classification algorithm was tested and compared with other Automatic Emergency Braking (AEB) algorithms under Euro-NCAP testing scenarios in simulation. Results show the proposed algorithm is promising in balancing the objectives of avoiding collision and reducing interference with driver's normal driving compared with other algorithms.

Original languageEnglish
Pages (from-to)30-43
Number of pages14
JournalAccident Analysis and Prevention
Publication statusPublished - 2019


  • Cluster analysis
  • Collision avoidance
  • Deceleration curve
  • Driver braking behavior profile
  • Dynamic time warping
  • Fuzzy logic


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