Target vehicle speed prediction method based on dynamic planning

Abstract

The invention discloses a target vehicle speed prediction method based on dynamic planning. A vehicle speed data prediction model in a control and analysis module builds a target vehicle speed model based on oil consumption of a vehicle, vehicle speed following, a control variable and a relative distance between vehicles; a data analysis module in the control and analysis module adopts a quick model prediction method to acquire the optimal target vehicle speed sequence for the target vehicle speed model; and an instruction issuing module in the control and analysis module feeds back instruction information for the optimal target vehicle speed sequence acquired by the data analysis module to a driver of each vehicle in a wireless transmission mode. Compared with the prior art, the target vehicle speed prediction method based on dynamic planning has the following advantages that the oil consumption of the vehicle can be effectively reduced; and with the total oil consumption of multiple vehicles in a traffic system to be minimal as a target, from a macro point of view, an idea is provided for solving the problem of total minimal energy consumption of multiple vehicles.

Description

technical field [0001] The invention relates to a target vehicle speed prediction system, in particular to a target vehicle speed prediction method based on dynamic programming. Background technique [0002] The rule-based prediction system has been applied industrially, while the optimization-based control method mainly stays in the theoretical research stage. In order to get the theoretical optimal solution, some scholars combine model prediction (MPC) and dynamic programming (DP). Considering the time cost of calculation, some scholars have studied the Equivalent Fuel Consumption Minimum Principle (ECMS) which can obtain an approximate optimal solution. However, the combined algorithm of MPC and DP, ECMS, PMP or improved methods of these algorithms, as well as some other optimization-based control algorithms, have always been unable to achieve real-time control. Moreover, in the process of using these methods to solve the problem, the model of the controlled object is g...

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Problems solved by technology

However, the combined algorithm of MPC and DP, ECMS, PMP or improved methods of these algorithms, as well as some other optimization-based control algorithms, have always been unable to achieve real-time control

Moreover, in the process of using these methods to solve the problem, the model of the controlled object is generally simplified to varying degrees, and it is easy to ignore the driver's driving intention, actual road conditions, the influence of signal lights, and the mutual influence between vehicles, etc. The optimal solution obtained is not the optimal solution in the true sense

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