In China Cut-in scenarios are quite common on both highway and urban road with heavy traffic. They have a potential risk of rear-end collision. When facing a cutting in vehicle, driver tends to brake in most case to reduce collision risk. The timing and dynamic characteristics of brake maneuver are indicators of driver subjective risk perception. Time to collision (TTC) and Time Headway (THW) demonstrate objective risk. This paper aims at building a model quantitatively revealing the relationship between drivers’ subjective risk perception and objective risk. A total of 66 valid critical Cut-in cases was extracted from China-FOT, which has a travel distance of about 130 thousand miles. It is found that under Cut-in scenarios, driver tended to brake when the cutting in vehicle right crossing line. This time point was defined as initial brake time. Brake strength and brake speed were taken to describe brake maneuver. Average brake pressure (ABP) and acceleration at initial brake time indicated brake strength. Brake pressure change rate (BPCR) and longitudinal jerk (derivative of acceleration) at initial brake time indicated brake speed. Analytic Hierarchy Process and Fuzzy Comprehensive Evaluation Method were adopted to obtain an integrated subjective risk perception indicator D. Critical cases were divided into 3 groups by distance of within 5 m, from 5 to 15 m and over 15 m. Within the distance of 5 m, D was linear with 1/THW. Within the distance of from 5 to 15 m, D was linear with 1/TTC. Within the distance of over 15 m, both 1/THW and 1/TTC have linear relationship with D.


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    Title :

    Driver Risk Perception Model under Critical Cut-In Scenarios


    Additional title:

    Sae Technical Papers


    Contributors:
    Zhu, Xichan (author) / Ma, Xuehan (author) / Feng, Zhiwei (author) / Ma, Zhixiong (author)

    Conference:

    Intelligent and Connected Vehicles Symposium ; 2018



    Publication date :

    2018-08-07




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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