Multi-target intelligent network connection vehicle collaborative optimization control method

Abstract

The invention discloses a multi-target intelligent network connection vehicle collaborative optimization control method, and relates to the technical field of intelligent network connection vehicle optimization, and the method comprises the steps: building a car-following model for each entrance of a target intersection through each target vehicle of each entrance in a preset communication range in the target intersection, and employing the car-following model to achieve the cooperative optimization of the target vehicles. And obtaining the following sequence of each target vehicle passing through the target intersection. According to the technical scheme, vehicle delay can be reduced when vehicles in all directions pass through the conflict area, road traffic efficiency is improved, traffic congestion is relieved, energy consumption of the vehicles in the process of passing through the potential conflict area is reduced, and green traffic development is promoted.

Description

technical field [0001] The invention relates to the technical field of optimization of intelligent networked vehicles, in particular to a multi-objective intelligent networked vehicle collaborative optimization control method. Background technique [0002] The development of intelligent connected vehicle technology has brought about an upcoming revolution in traffic management. When considering the deployment of vehicles, the control of vehicles in potential conflict areas will have a complex impact on vehicle traffic management, such as on-ramps and intersections, so real-time and effective vehicle control strategies can reduce driving delays caused by vehicles in conflict areas and improve traffic flow. efficiency. [0003] In the existing technology, the research on the control of intelligent networked vehicles is mainly divided into two categories, one is the research on the rule-based strategy, which mainly uses heuristic rules to determine the passing order of vehicle...

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Problems solved by technology

[0003]In the existing technology, the research on the control of intelligent networked vehicles is mainly divided into two categories, one is the research on rule-based strategy, which mainly uses heuristic Formula rules are used to determine the passing order of vehicles, but the passing order based on the nature of the rules is a feasible solution, not an optimal solution. In addition, the rule-based strategy is limited by traditional signal control in some cases; The other is the strategy based on planning. The strategy based on planning is a method to find the optimal solution in a huge solution space. In the current research process, the main optimization goals considered are only time indicators such as passing time and total delay. There are few studies on the optimization of energy consumption, queue length and other objectives. At this stage, there is a lack of a general framework for scheduling intelligent networked vehicles that can take into account safety, traffic efficiency and energy consumption in real time.

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