Journal Article

Complexity of Driving Scenarios Based on Traffic Accident Data

Zhang, Daowen, Dong, Xinchi, Zhang, Tianshu, Mu, Yaoyao, Tang, Kaiwen

2024

Overview

To solve the problems of difficult quantification of complex driving scenes and unclear classification, a method of complex measurement and scene classification 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 quantified 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 verified by experiments. This scenario complex metric method can provide a reference for studying the complex metrics and scene classification of smart vehicle test scenarios.

• Publisher: Springer Nature B.V
• Subject: Accident data / Back propagation networks / Bayesian analysis / Classification / Cluster analysis / Clustering / Complexity / Conditional probability / Entropy / Entropy (Information theory) / Intelligent vehicles / Neural networks / Railroad accidents & safety / Traffic accidents / Vector quantization