Intelligent networked vehicle speed prediction method based on road traffic environment

Abstract

The invention discloses an intelligent networked vehicle speed prediction method based on a road traffic environment. The invention relates to the field of intelligent networked automobiles, and provides a long-time vehicle speed prediction method for quantifying road traffic environment characteristics; the predicted vehicle speed obtained by the method can be used for a driving energy and driving time demand prediction scene, so that the robustness and generalization ability of an existing vehicle speed prediction model are greatly improved, and data support and basis are provided for realizing intelligent management and optimization of vehicle-mounted energy and reasonable planning of a path. The prediction method comprises the following steps: S1, data acquisition; S2, data extraction;S3, establishing a model; S4, consumption prediction. According to the invention, the robustness and generalization ability of an existing vehicle speed prediction model are greatly improved, and data support and basis are provided for intelligent management and optimization of vehicle-mounted energy and reasonable planning of a path.

Description

technical field [0001] The invention relates to the field of intelligent networked vehicles, in particular to a speed prediction method for intelligent networked vehicles based on quantification of road traffic environment characteristics. Background technique [0002] Long-term vehicle speed prediction refers to predicting the future driving speed on the entire planned route according to the road traffic information on the entire planned route, combined with the driver's behavior habits and vehicle conditions before the vehicle is driving. Effective and accurate long-term vehicle speed prediction is an inevitable requirement for the development of Intelligent Connected Vehicles (ICV) and Intelligent Transportation Systems (ITS). It has important research value and application prospects, and can be widely used to improve traffic efficiency, improve safe driving control, Intelligent optimal control of on-board energy, improvement of fuel economy and estimation of remaining dr...

AI Technical Summary

Problems solved by technology

However, most of the long-term vehicle speed prediction methods are based on time period-location historical vehicle speed data. Essentially, historical vehicle speeds are used to predict future vehicle speeds, which cannot achieve more accurate vehicle speed predictions for new driving sections that lack historical data.

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