Double-layer collaborative optimization method for ramp merging of networked vehicles

Abstract

The invention belongs to the technical field of intelligent control of networked vehicles under an Internet of Vehicles system, and particularly relates to a double-layer collaborative optimization method for ramp merging of networked vehicles. According to the method, the balance between the calculation load and the optimality is considered, and the optimality is improved to a greater extent andthe calculation amount is reduced to the maximum extent through the form of an upper-layer optimization model and a lower-layer analytical solution; an upper-layer scheduling optimization model and alower-layer scheduling optimization model of intelligent networked vehicle ramp merging cooperative control are adopted. The upper layer determines and allocates the time for each intelligent networked vehicle to enter a merging area, and the lower layer determines the specific motion trail of each intelligent networked vehicle, the improvement of the traffic efficiency and the energy utilizationrate of ramp merging is facilitated, and through lower-layer calculation, the intelligent networked vehicles which cannot meet the minimum time interval return to the upper-layer scheduling again forscheduling calculation, so that the system has robustness.

Description

technical field [0001] The invention belongs to the technical field of intelligent control of connected vehicles under the Internet of Vehicles system, and specifically relates to a double-layer collaborative optimization method for merging ramps of connected vehicles. Background technique [0002] The increasing frequency of transport activities and traffic congestion has a major impact on socio-economic issues. As one of the bottlenecks in the transportation system, highway ramp mergers not only lead to huge economic and traffic costs, but also have side effects on increasing air pollutant emissions and collision risks. The development of car networking and automation technology provides opportunities to solve the above problems. Utilizing the real-time information transmission capabilities between vehicle-to-vehicle and vehicle-to-infrastructure, more advanced and efficient traffic management systems can be developed to reduce traffic congestion and air pollutant emissio...

AI Technical Summary

Problems solved by technology

The rule-based approach is mainly a vehicle coordination algorithm at unsignalized ramps based on the "first in, first out" rule, however, it does not consider microscopic performance (such as vehicle trajectories) and optimize the merging order of vehicles

There are two optimization-based methods, one only optimizes the trajectory of the vehicle, and assumes

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