Assessing the safety performance of automated driving is essential for the market introduction of this technology. Different regulatory bodies have explicitly or implicitly asked for proofing the safety effects of the technology, respectively to demonstrate that the technology is at least as good as human drivers. Due to the complexity of automated driving, the answer hardly can be found by test track tests. Instead, virtual assessment tools, such as the simulation, are required to assess the safety performance of automated driving. Within the simulation, the baseline, which is defined by the human drivers, needs to be represented as well. This is typically done by a driver behavior model. For this purpose, BMW and its partners have developed the stochastic cognitive model (SCM). SCM is presented in this paper. The aim of this paper is to show the performance of SCM. Therefore, it is applied in the critical situation of a passive cut-in maneuver. For this scenario, a Monte-Carlo simulation experiment is conducted. The results of this experiment are compared to real world passive cut-in maneuvers of the HighD and GIDAS PCM datasets.


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    Title :

    Driver Behavior Model for the Safety Assessment of Automated Driving


    Contributors:


    Publication date :

    2022-06-05


    Size :

    719075 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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