Vehicle following safety automatic assessment method based on machine learning

Abstract

The invention discloses a vehicle following safety automatic assessment method based on machine learning. The vehicle following safety automatic assessment method comprises the steps that data are acquired; data cleaning is performed on the acquired data, the data meeting the requirements are reserved, and the data are standardized and normalized in the same data set D; extraction and modeling of the required feature fields are performed on the cleaned data; modeling data M used for machine learning are extracted from the cleaned and normalized data according to accident records and relevant monitoring data; the M set is randomly extracted and divided into two subsets MT and ME according to the given proportion, MT is used for model training, and ME is used for model performance verification testing; supervised classification and machine learning algorithms are adopted, modeling learning is performed by utilizing training data MT, the obtained model performance is verified by ME data and relevant confusion matrix and model classification accuracy is calculated; the results of each time are recorded and compared, and an optimal model is selected; and all the records in the data set D are automatically assessed by using the optimal model, and the results are appended to the data set D and the results are outputted.

Description

technical field [0001] The invention belongs to the field of information processing of expressway operation management, and in particular relates to a machine learning-based automatic vehicle following safety evaluation method. Background technique [0002] In recent years, the role of informatization in expressway operation and management has been increasing day by day. With the improvement of informatization (a large amount of data accumulation, the improvement of machine performance, the development of data mining and other disciplines), the precise management and service based on vehicles are in the smart It came into being in the construction of expressways. Providing precise management services for individual vehicles relies on in-depth cognition of various behaviors of vehicles. However, due to the large number of management objects and complex cognition content, manual evaluation cannot be processed one by one on a vehicle-by-vehicle basis due to the huge workload. ...

AI Technical Summary

Problems solved by technology

[0003] In the traditional sense, tracking and judging the driving behavior of individual vehicles is difficult and requires a lot of work, and there is no precedent for using traditional methods

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