本文關(guān)鍵詞： 車輛主動安全 預(yù)測模型 越線時間 自適應(yīng)巡航控制系統(tǒng) 目標(biāo)辨識 狀態(tài)預(yù)測 Petri網(wǎng)　出處：《長安大學(xué)》2014年博士論文　論文類型：學(xué)位論文

【摘要】：現(xiàn)代汽車安全技術(shù)的主流趨勢已經(jīng)由被動安全系統(tǒng)轉(zhuǎn)為主動安全系統(tǒng)。車輛主動安全系統(tǒng)能夠在交通沖突發(fā)生的早期對駕駛員進行提示或者介入車輛的操控，從而避免交通沖突的進一步惡化而引發(fā)事故。從保證安全的角度而言，當(dāng)存在交通沖突風(fēng)險時，車輛主動安全系統(tǒng)應(yīng)該盡早的作出辨識。從時間序列而言，如果能夠使用車輛的現(xiàn)有行駛狀態(tài)表征參數(shù)對車輛下一步的運動狀態(tài)進行預(yù)測，則可以對“即將到來”的交通沖突進行預(yù)先準(zhǔn)備，從而進一步提高車輛主動安全系統(tǒng)的生效時間。車輛的行駛狀態(tài)表征參數(shù)縱多，同時車輛行駛交通環(huán)境類型也不一樣，因此，如何利用現(xiàn)有參數(shù)對車輛行駛狀態(tài)和交通環(huán)境進行預(yù)測是車輛主動安全系統(tǒng)在算法設(shè)計時需要重點考慮的問題。 針對車輛主動安全系統(tǒng)對于參數(shù)預(yù)測的關(guān)鍵技術(shù)需求，本文利用多類型傳感器搭建了車輛行駛過程中的表征參數(shù)同步采集試驗平臺，實現(xiàn)了車載環(huán)境下多車輛行駛車速、交通環(huán)境參數(shù)的同步采集。利用上述試驗平臺對10名被試在不同道路環(huán)境下開展了真實駕駛試驗，獲取了大量的車輛行駛狀態(tài)真實參數(shù)�？紤]車輛主動安全系統(tǒng)在線運行的真實特點，在對國內(nèi)外現(xiàn)有技術(shù)進行分類總結(jié)的情況下，主要完成了以下的研究內(nèi)容： 1、提出了基于幾何分析方法的車輛換道過程中越線時間預(yù)測模型。通過使用車輛與車道線距離數(shù)據(jù)，分析了車輛換道過程中的幾何特性。并結(jié)合車-路幾何模型，提出了車輛換道過程中的車輛偏航角估計理論。針對直道路段和彎道路段，并考慮車輛換道方向與道路彎道方向，分別提出了車輛在直道路段和彎道路段換道過程中的越線時間預(yù)測模型。采用真實數(shù)據(jù)對預(yù)測模型的精度進行驗證，結(jié)果表明，模型整體預(yù)測誤差較小，且絕大部分的誤差分布于零點附件。所進行的檢驗結(jié)果中，直道路段預(yù)測誤差絕對值小于等于0.1s的比例達到了78.3%，彎道路段相應(yīng)的比例達到了80.8%，且兩種模型的預(yù)測誤差均符合正態(tài)分布規(guī)律。 2、通過建立車-路之間的幾何關(guān)系模型，，并采用車速與橫擺角速度對道路曲率進行估計，提出并建立了ACC系統(tǒng)對有效目標(biāo)、潛在有效目標(biāo)和無效目標(biāo)的辨識理論與模型。對辨識模型分別進行了單目標(biāo)追蹤、多目標(biāo)追蹤以及多目標(biāo)狀態(tài)切換追蹤的檢驗，結(jié)果表明，本文所建立的模型能夠有效的區(qū)分三類目標(biāo)。在此基礎(chǔ)上，利用模糊加權(quán)評價方法，采用目標(biāo)車的速度、目標(biāo)車跟車時距、目標(biāo)車橫向運動狀態(tài)等參數(shù)建立了前方車輛狀態(tài)切換的預(yù)測模型。采用真實試驗數(shù)據(jù)對預(yù)測模型進行檢驗，結(jié)果表明，該模型對目標(biāo)車不同狀態(tài)的切換預(yù)測準(zhǔn)確率均超過了90%。 3、針對車輛運行過程中對于自車運動狀態(tài)參數(shù)的預(yù)測需求，以線性二自由度車輛模型為研究對象，采用模糊Petri理論建立了車輛運行軌跡模型，將車身橫向、縱向加速度、俯仰以及側(cè)傾角速度作為輸入變量建立了車輛運行狀態(tài)預(yù)測模型，分別實現(xiàn)了對自車運行速度、橫擺角速度、運行軌跡等參數(shù)的預(yù)測。針對單純BP神經(jīng)網(wǎng)絡(luò)模型在對車輛運動狀態(tài)預(yù)測過程中存在的不足，本文提出采用貝葉斯濾波器對BP神經(jīng)網(wǎng)絡(luò)模型的結(jié)果進行優(yōu)化，檢驗結(jié)果表明，該方法將預(yù)測準(zhǔn)確率由83.6%提高到了92.4%。 本研究得到了國家自然科學(xué)基金項目（51178053和61374196）和教育部長江學(xué)者和創(chuàng)新團隊發(fā)展計劃項目（IRT1286）的資助。
[Abstract]:The main trend of modern automotive safety technology has been from the passive safety system to active safety system. The vehicle active safety system can prompt intervention or manipulation of the vehicle driver in the early stage of traffic conflict, so as to avoid further deterioration of the traffic conflict caused the accident. From the security point of view, when there is traffic conflict risk the vehicle active safety system, should as soon as possible to make identification. From the time sequence, if the motion state of the existing driving state parameters can be used for vehicle vehicle next prediction, can the traffic conflict coming "were prepared in advance, so as to further improve vehicle active safety system. The effect of time parameter the running state of the vehicle longitudinal, and vehicle traffic environment types are not the same, therefore, how to use the existing parameters of The vehicle driving state and traffic environment prediction are the key problems to be considered when the vehicle active safety system is designed in the algorithm design.
For the vehicle active safety system for the demand of key technical parameters prediction, this paper by multi-sensor synchronous acquisition test platform parameters built during the running of the vehicle, the vehicle speed vehicle under multi environment, synchronous acquisition traffic environment parameters. On 10 subjects of the real driving test carried out in different road environment the use of the test platform, to obtain a large number of real vehicle state parameters. Considering the real characteristics of online vehicle active safety system, summarized the situation of existing technology at home and abroad, the main research contents of the following:
1, put forward the geometric analysis method of lane changing process and time prediction model based on vehicle and lane. By using distance data, analysis of the geometric characteristics of road vehicles to change process. Combined with the vehicle road geometry model is proposed for vehicle road vehicle yaw angle estimation process in straight theory. And curved sections, lane changing direction and the curve of the road direction and consider, respectively put forward more time line vehicles in straight and curved sections lane change process model. To verify the accuracy of the real data of the prediction model. The results show that the model of overall prediction error is small, and most of the error distribution in the zero attachment. The test results, the straight section of the prediction error absolute value is less than or equal to 0.1s ratio reached 78.3%, the proportion of the corresponding curve sections reached 80.8%, and the two kinds of model pre The measurement error is in accordance with the normal distribution.
2, through the establishment of vehicle geometry model between the road, and the speed and yaw rate of the road curvature estimation, put forward and established a ACC system for effective target, identification theory and model of potential targets and effective target. The invalid identification model were investigated by single target tracking, multi-target test tracking and multi-target tracking state switching. The results show that the model can effectively distinguish the three kinds of targets. On this basis, using weighted fuzzy evaluation method, the target vehicle speed, the car with the car away from the target, the target vehicle lateral motion parameter prediction model is set up in front of the vehicle state switch. By testing, the prediction model was the real test data. The results show that the handoff prediction model on the target vehicle in different states accurate rate of over 90%.
3, according to the running process of the vehicle for demand forecasting vehicle motion state parameters, the linear two degrees of freedom vehicle model as the research object, using fuzzy Petri theory to establish the vehicle trajectory model, the transverse, longitudinal acceleration, pitch and roll rate as input variables to establish the prediction model of vehicle running state, respectively. The realization of vehicle speed, yaw rate, prediction trajectory and other parameters. For the simple BP neural network model in the vehicle motion state prediction process problems, this paper adopts Bei Juliu filter to the BP neural network model optimization results, test results show that this method will predict accuracy increased from 83.6% to 92.4%.
The study was funded by the National Natural Science Foundation (51178053 and 61374196) and the Ministry of education, the Yangtze River scholar and the innovative team development program (IRT1286).

【學(xué)位授予單位】：長安大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：U461.91

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