基于駕駛員博弈仿真的車聯(lián)網(wǎng)下節(jié)能策略評價
發(fā)布時間：2018-07-25 10:59
【摘要】：車聯(lián)網(wǎng)技術的不斷發(fā)展,為智能交通系統(tǒng)的實現(xiàn)提供了可能,在車聯(lián)網(wǎng)逐漸普及的過程中,如何利用車聯(lián)網(wǎng)提供的信息來幫助駕駛員提高行車效率、減少燃油消耗成為汽車領域的研究熱點。從車輛方面出發(fā)進行節(jié)能駕駛的研究已經(jīng)相對成熟了,這類研究主要是根據(jù)發(fā)動機的動力性和經(jīng)濟性去調(diào)整車輛在行駛中的速度和加速度,該方法在實際使用中常常因受到周圍車輛的干擾等原因,很難達到預想的行駛效率及節(jié)能效果。從駕駛員行為及交通條件出發(fā)進行節(jié)能駕駛的研究,可以避免因為沒有考慮周圍其他車輛造成與實際狀況不符的情況,但提出的節(jié)能駕駛策略是否有效需要進行實驗驗證。然而,基于實車的實際道路或者試驗場實驗通常存在實驗成本高、周期長、場景復雜并且重復性差等問題,而傳統(tǒng)的車輛仿真技術和交通系統(tǒng)仿真技術雖然具有成本低重復性高的優(yōu)勢,但也存在無法實現(xiàn)車聯(lián)網(wǎng)下車輛對周圍環(huán)境感知及車輛間信息分享等實驗環(huán)境的不足。因此,本文對車聯(lián)網(wǎng)下節(jié)能策略評價方法進行了研究,建立了面向車聯(lián)網(wǎng)下車輛運行仿真的駕駛員博弈仿真平臺,并用于評價駕駛員的節(jié)能駕駛策略。本文的主要工作和成果總結如下:1.對駕駛員博弈仿真系統(tǒng)進行分析,設計了駕駛員博弈仿真平臺。平臺主要包括道路生成與信號燈模塊、駕駛員博弈行為仿真模塊、人機交互模塊和數(shù)據(jù)分析模塊。道路模型和信號燈配時方案參考實際道路情況選擇,駕駛員博弈行為仿真模塊包含了GM跟馳模型及改進的Gipps換道模型,其中駕駛員行為參數(shù)根據(jù)實際道路的調(diào)查結果選取,人機交互模塊可以動態(tài)顯示仿真過程中各個車輛位置和信號燈實時狀態(tài),數(shù)據(jù)分析模塊可以根據(jù)仿真結果統(tǒng)計出每個車輛對應的各項行駛指標并保存輸出。2.對駕駛員博弈仿真平臺的性能進行了評價。對駕駛員博弈仿真平臺評價指標進行了研究,確定以平均行程速度和駕駛員分布均勻性為主要評價指標,并參考速度分布、怠速比和速度時間序列幾項指標。設計仿真實驗,根據(jù)確定的幾項指標分別對平臺進行驗證,得到仿真平臺性能滿足設計要求的結果。3.對典型交通狀況下的仿真參數(shù)選擇進行了研究。設計在不同車輛數(shù)下的駕駛員博弈仿真實驗,確定表征自由流、平峰流、高峰流交通狀態(tài)的車輛數(shù)分別為100輛,300輛和800輛。設計在不同仿真時間下的駕駛員博弈仿真實驗,確定仿真時長達到5400s時,仿真參數(shù)和統(tǒng)計數(shù)據(jù)總體收斂的結果。4.對評價駕駛節(jié)能策略效果的指標和參考值進行了研究。對用于評價駕駛節(jié)能策略的指標進行了分析,確定以時間收益和油耗收益為主要指標,并使用價值方法將這兩個指標統(tǒng)一成行駛總收益這一個指標作為評價駕駛節(jié)能策略的指標。5.對固定型駕駛策略的行駛收益進行了博弈分析,揭示了各種駕駛策略在多策略環(huán)境下的不同收益情況。設計在不同的車輛數(shù)下,對五種固定型駕駛策略在不同比例分布及單類型駕駛員時的仿真實驗,分析在各種條件下各類型駕駛員的收益結果,并將每種交通密度及駕駛員分布下所有駕駛員的平均收益作為評價駕駛節(jié)能策略的參考值。根據(jù)純策略博弈和混合策略博弈分析理論上各類型駕駛員的行駛收益,對比理論值和仿真的真實值,推斷出:在實際交通環(huán)境中,采用各類型駕駛策略的駕駛員收益差比理論值小,整體收益呈現(xiàn)一種更均衡的狀態(tài)。6.對車聯(lián)網(wǎng)下跟隨策略、平均策略和組合策略的三種駕駛節(jié)能策略實際效果進行了分析,揭示了復雜交通環(huán)境下實現(xiàn)節(jié)能策略的難度,也說明了在多策略駕駛條件下進行策略有效性評價的必要性。設計在不同駕駛員類型比例分布及不同車輛數(shù)下的仿真實驗,對分別使用跟隨策略、平均策略和組合策略的實際收益進行統(tǒng)計,并根據(jù)統(tǒng)計結果評價各類型節(jié)能策略的實際效果和適用范圍。根據(jù)分析結果得出,各類型駕駛員都無法在任何交通環(huán)境和交通密度下持續(xù)占優(yōu),只有根據(jù)道路的交通環(huán)境和交通密度實時調(diào)整駕駛策略才能達到節(jié)能效果的結果。
[Abstract]:The continuous development of vehicle networking technology provides the possibility for the realization of the intelligent transportation system. In the process of the gradual popularization of the vehicle network, how to use the information provided by the vehicle network to help drivers improve the driving efficiency and reduce fuel consumption has become a hot spot in the field of automobile research. It is mature. This kind of research mainly adjusts the speed and acceleration of the vehicle in driving according to the power and economy of the engine. This method is often difficult to achieve the expected driving efficiency and energy saving effect because of the interference of the surrounding vehicles in actual use. It can be avoided because the other vehicles around the world do not agree with the actual situation, but the effectiveness of the proposed energy-saving driving strategy needs to be verified experimentally. However, the actual road or test field experiments based on real cars usually have problems such as high experimental cost, long period, complex scene and poor repeatability, etc. The traditional vehicle simulation technology and the traffic system simulation technology have the advantages of low cost and low repetition, but there are also the inability to realize the experimental environment of vehicle environment perception and information sharing between vehicles. Therefore, this paper studies the evaluation method of energy saving strategy under the vehicle network, and establishes a car facing couplet. The driver game simulation platform of the vehicle operation simulation under the network is used to evaluate the driver's energy-saving driving strategy. The main work and results of this paper are summarized as follows: 1. the driver game simulation system is analyzed and the driver game simulation platform is designed. The platform mainly includes the Lu Shengcheng and the signal lamp module, and the driver's game behavior is imitated. True module, man-machine interaction module and data analysis module. The road model and signal timing scheme refer to the actual road condition selection. The driver game behavior simulation module includes the GM following model and the improved Gipps lane change model. The driver behavior parameters are selected according to the actual road investigation results, and the human-computer interaction module can be moved. The position of each vehicle and the real-time state of the signal lamp are displayed in the simulation process. The data analysis module can count the driving indexes corresponding to each vehicle according to the simulation results and evaluate the performance of the driver game simulation platform. The simulation platform evaluation index of the driver game is studied and the.2. is determined. The average travel speed and the uniformity of the driver distribution are the main evaluation indexes, and refer to the speed distribution, the idle speed ratio and the speed time series. The simulation experiments are designed to verify the platform according to the certain indexes. The simulation platform performance meets the design requirements and the simulation parameters of the typical traffic conditions are obtained by.3.. The driver game simulation experiment under different vehicle number is designed to determine the number of vehicles representing free flow, flat peak flow and peak flow traffic state, respectively, 100 vehicles, 300 vehicles and 800 vehicles. The driver game simulation experiment under different simulation time is designed to determine the simulation parameters and statistical data when the simulation is up to 5400s. The result of overall convergence.4. studies the index and reference value of the effect of driving energy saving strategy evaluation. The indexes used to evaluate the driving energy saving strategy are analyzed, and the main indexes of the time income and oil consumption income are determined, and the value method is used to unify the two indexes as the driving total income as the evaluation driving. The index.5. of driving energy strategy has a game analysis on the driving income of the fixed driving strategy, and reveals the different benefits of various driving strategies under the multi strategy environment. The simulation experiments of five fixed driving strategies under different proportion distribution and single type drivers are designed under different number of vehicles. The income of each type of driver is given, and the average income of each driver is considered as the reference value of the driving energy saving strategy. The driving income of all types of drivers in the theory of the pure strategy game and the mixed strategy game is analyzed, and the theoretical value and the real value of the simulation are compared. In the actual traffic environment, the driver income difference of each type of driving strategy is smaller than the theoretical value, and the overall income presents a more balanced state.6.. The actual effects of three driving energy saving strategies are analyzed, which reveals the difficulty of realizing the energy saving strategy in complex traffic environment. It also illustrates the necessity of evaluating the effectiveness of the strategy under the conditions of Multi Strategy driving. The simulation experiments of different driver type ratio distribution and different vehicle number are designed, and the actual income of the following strategy, the average strategy and the combination strategy are used respectively, and the actual results are used to evaluate the practice of each type of energy saving strategy. According to the results of the analysis, it is concluded that all types of drivers can not continue to dominate in any traffic environment and traffic density. Only according to the traffic environment and traffic density can adjust the driving strategy in real time to achieve the result of energy saving.
【學位授予單位】：吉林大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U495
[Abstract]:The continuous development of vehicle networking technology provides the possibility for the realization of the intelligent transportation system. In the process of the gradual popularization of the vehicle network, how to use the information provided by the vehicle network to help drivers improve the driving efficiency and reduce fuel consumption has become a hot spot in the field of automobile research. It is mature. This kind of research mainly adjusts the speed and acceleration of the vehicle in driving according to the power and economy of the engine. This method is often difficult to achieve the expected driving efficiency and energy saving effect because of the interference of the surrounding vehicles in actual use. It can be avoided because the other vehicles around the world do not agree with the actual situation, but the effectiveness of the proposed energy-saving driving strategy needs to be verified experimentally. However, the actual road or test field experiments based on real cars usually have problems such as high experimental cost, long period, complex scene and poor repeatability, etc. The traditional vehicle simulation technology and the traffic system simulation technology have the advantages of low cost and low repetition, but there are also the inability to realize the experimental environment of vehicle environment perception and information sharing between vehicles. Therefore, this paper studies the evaluation method of energy saving strategy under the vehicle network, and establishes a car facing couplet. The driver game simulation platform of the vehicle operation simulation under the network is used to evaluate the driver's energy-saving driving strategy. The main work and results of this paper are summarized as follows: 1. the driver game simulation system is analyzed and the driver game simulation platform is designed. The platform mainly includes the Lu Shengcheng and the signal lamp module, and the driver's game behavior is imitated. True module, man-machine interaction module and data analysis module. The road model and signal timing scheme refer to the actual road condition selection. The driver game behavior simulation module includes the GM following model and the improved Gipps lane change model. The driver behavior parameters are selected according to the actual road investigation results, and the human-computer interaction module can be moved. The position of each vehicle and the real-time state of the signal lamp are displayed in the simulation process. The data analysis module can count the driving indexes corresponding to each vehicle according to the simulation results and evaluate the performance of the driver game simulation platform. The simulation platform evaluation index of the driver game is studied and the.2. is determined. The average travel speed and the uniformity of the driver distribution are the main evaluation indexes, and refer to the speed distribution, the idle speed ratio and the speed time series. The simulation experiments are designed to verify the platform according to the certain indexes. The simulation platform performance meets the design requirements and the simulation parameters of the typical traffic conditions are obtained by.3.. The driver game simulation experiment under different vehicle number is designed to determine the number of vehicles representing free flow, flat peak flow and peak flow traffic state, respectively, 100 vehicles, 300 vehicles and 800 vehicles. The driver game simulation experiment under different simulation time is designed to determine the simulation parameters and statistical data when the simulation is up to 5400s. The result of overall convergence.4. studies the index and reference value of the effect of driving energy saving strategy evaluation. The indexes used to evaluate the driving energy saving strategy are analyzed, and the main indexes of the time income and oil consumption income are determined, and the value method is used to unify the two indexes as the driving total income as the evaluation driving. The index.5. of driving energy strategy has a game analysis on the driving income of the fixed driving strategy, and reveals the different benefits of various driving strategies under the multi strategy environment. The simulation experiments of five fixed driving strategies under different proportion distribution and single type drivers are designed under different number of vehicles. The income of each type of driver is given, and the average income of each driver is considered as the reference value of the driving energy saving strategy. The driving income of all types of drivers in the theory of the pure strategy game and the mixed strategy game is analyzed, and the theoretical value and the real value of the simulation are compared. In the actual traffic environment, the driver income difference of each type of driving strategy is smaller than the theoretical value, and the overall income presents a more balanced state.6.. The actual effects of three driving energy saving strategies are analyzed, which reveals the difficulty of realizing the energy saving strategy in complex traffic environment. It also illustrates the necessity of evaluating the effectiveness of the strategy under the conditions of Multi Strategy driving. The simulation experiments of different driver type ratio distribution and different vehicle number are designed, and the actual income of the following strategy, the average strategy and the combination strategy are used respectively, and the actual results are used to evaluate the practice of each type of energy saving strategy. According to the results of the analysis, it is concluded that all types of drivers can not continue to dominate in any traffic environment and traffic density. Only according to the traffic environment and traffic density can adjust the driving strategy in real time to achieve the result of energy saving.
【學位授予單位】：吉林大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U495
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