A forward collision avoidance algorithm based on driver braking behavior. (August 2019)

• Record Type: Journal Article
• Authors: Xiong, Xiaoxia; Wang, Meng; Cai, Yingfeng; Chen, Long; Farah, Haneen; Hagenzieker, Marjan
• Is Part Of: Accident analysis and prevention. Volume 129(2019)
• Page Start: 30
• Page End: 43
• Publication Date: 2019-08
• Subjects: Collision avoidance -- Deceleration curve -- Cluster analysis -- Fuzzy logic -- Driver braking behavior profile -- Dynamic time warping; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2019.05.004
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: A novel combination of offline deceleration profile segmentation and online risk level classification. Deceleration curves from critical evasive braking were clustered into different risk levels using spectrum clustering. Risk classification rules at critical braking onset were extracted according to deceleration curve clusters. Promising in balancing the objectives of avoiding collision and reducing interference with driver's normal driving. Abstract: 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.