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Complexity of Driving Scenarios Based on Traffic Accident Data
Dong Xinchi,Zhang Daowen,Mu Yaoyao,Zhang Tianshu,Tang Kaiwen 한국자동차공학회 2024 International journal of automotive technology Vol.25 No.1
To solve the problems of diffi cult quantifi cation of complex driving scenes and unclear classifi cation, a method of complex measurement and scene classifi cation was proposed. Based on the Bayesian network, the posterior probability distribution was obtained, the variable weights were determined by information entropy theory and BP neural network, and the gravitational model was improved so that the complex metric model of the driving scene was established, the static and dynamic complexity of the scene was quantifi ed respectively, and a weighted fusion of the two was conducted. The K-means clustering method was used to divide the driving scenario into three categories, i.e., simple scenario, medium complex scenario, and complex scenario, and the rationality of the method was verifi ed by experiments. This scenario complex metric method can provide a reference for studying the complex metrics and scene classifi cation of smart vehicle test scenarios.
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