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A forward collision avoidance algorithm based on driver braking behavior.

Xiaoxia Xiong1, Meng Wang2, Yingfeng Cai1

  • 1School of Automotive and Traffic Engineering, Jiangsu University, 301 Xuefu Road, 212013, Zhenjiang, Jiangsu, China.

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|May 20, 2019
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Summary
This summary is machine-generated.

This study introduces an online risk classification algorithm for forward collision avoidance systems. It effectively balances collision avoidance with reduced driver interference, showing promising results in simulations.

Keywords:
Cluster analysisCollision avoidanceDeceleration curveDriver braking behavior profileDynamic time warpingFuzzy logic

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Area of Science:

  • Automotive Safety Engineering
  • Intelligent Transportation Systems
  • Machine Learning for Control Systems

Background:

  • Collision avoidance systems are crucial for road safety.
  • Accurate real-time risk assessment is needed for effective forward collision avoidance.
  • Existing systems may struggle to balance safety with driver comfort.

Purpose of the Study:

  • To develop an online risk level classification algorithm for forward collision avoidance systems.
  • To utilize braking profiles to infer risk levels.
  • To create a system that minimizes unnecessary interventions during normal driving.

Main Methods:

  • Extracted deceleration curves from critical braking events in the Virginia "100-car" database.
  • Applied spectrum clustering using curve distance and changing rate as dissimilarity metrics.
  • Developed fuzzy logic rules based on safety indicators like time to collision and time headway.
  • Tested the algorithm against other Automatic Emergency Braking (AEB) algorithms in Euro-NCAP scenarios.

Main Results:

  • Successfully clustered deceleration curves into distinct risk levels.
  • Extracted fuzzy logic rules for risk classification based on safety indicators.
  • The proposed algorithm demonstrated a promising balance between collision avoidance and reduced driver interference.
  • Outperformed other AEB algorithms in simulated Euro-NCAP testing scenarios.

Conclusions:

  • The developed fuzzy risk classification algorithm is effective for forward collision avoidance.
  • The algorithm shows potential for enhancing vehicle safety without compromising driving experience.
  • Further validation in real-world scenarios is warranted.