A high-risk traffic scene extraction method based on a risk field

Abstract

The application discloses a high-risk traffic scene extraction method based on a risk field, and belongs to the technical field of traffic safety and automatic driving test. The method comprises the following steps: acquiring natural trajectory data of traffic participants in a target area in a real world; based on a risk field model, quantifying the risk value of each traffic participant in each time frame in a plane rectangular coordinate system to form a scene risk sequence; using kernel density estimation to analyze the statistical characteristics of risk distribution and dynamically adjusting a high-risk threshold; extracting time frames with risk values exceeding the threshold as high-risk frames and identifying corresponding high-risk traffic participants; and further generating a risk heat map through map matching and spatial aggregation to identify spatial high-risk areas. The application can quantitatively analyze dynamic risks in different traffic scenes in a unified and continuous manner, overcome the poor scene adaptability of traditional rule-based methods and the low fidelity of simulation-based methods, and realize efficient and objective extraction of real complex high-risk scenes.

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