| The GLOSA algorithm provides a suggested speed based on the timing and phase information about the traffic lights the vehicle will pass through.The algorithm aims to reduce the number of red lights encountered by vehicles on the road in order to reduce unnecessary stops,thus saving fuel consumption and reducing carbon dioxide emissions.It also can provide technical support for energy conservation and emission reduction of transportation industry.Simulation is an important part of GLOSA algorithm research,and plays an important role in verifying and evaluating algorithms before they are deployed in the field.However,GLOSA algorithms are often designed to simulate the performance of a single vehicle or a small number of vehicles over a small area,and the results of such simulations are of low value of field deployment.Therefore,designing simulation scenes and GLOSA algorithms based on real complex traffic scenarios will help improve the efficiency of the GLOSA algorithm in real traffic and accelerating the promotion of vehicles equipped with GLOSA systems.In this research,a GLOSA algorithm aimed at reducing vehicle fuel consumption was proposed by analyzing the real world vehicle movement state and traffic situation.This algorithm can be used as a solution for Intelligent and Connected Vehicle Applications in multi-modal traffic environment.The contents of this research include the following several parts:Firstly,we deeply analyze the motion state of the car and study the fuel consumption of the car in different stages.According to the relationship between the vehicle state transition and fuel consumption,the priority of the vehicle during the GLOSA algorithm state transition is determined.Secondly,we analyze the real traffic environment and extract the real traffic environment into various scenes of multi-modal traffic.We fully consider the physical,behavioral and logical differences between different vehicles,and establish a vehicle behavior model under multi-modal traffic,including the vehicle following model and the vehicle lane change logic.According to the relationship between vehicle state transition and fuel consumption and considering the specific environment of complex traffic environment,this paper designed GLOSA algorithm under multi-modal traffic.Finally,a multi-modal traffics simulation environment is built based on VEINS architecture.Based on the architecture simulation platform,GLOSA algorithm under multi-modal traffic is simulated and analyzed in this paper.The effects of traffic density and switching GLOSA algorithm on traffic efficiency are analyzed through validation experiments and output results of the developed algorithm.The work of this part has important reference function for the improvement of algorithm and the future real vehicle tests debugging.Compared with other GLOSA algorithm,this paper proposed GLOSA algorithm is more pay attention to the adaptability of complex driving scenes of reality,enhances the algorithm feasibility in actual traffic.Although the design of this paper did not consider reducing the waiting time of vehicles as a goal,the GLOSA algorithm proposed effectively reduced the waiting time of vehicles in the simulation experiments.The final conclusion were that GLOSA vehicles reduce fuel consumption and carbon dioxide emissions by 11.28%,and reduce waiting times by 9.27%. |
