本文選題：駕駛員模型 + 轉(zhuǎn)向行為��； 參考：《南京航空航天大學》2016年碩士論文

【摘要】：隨著人們生活水平的不斷提高,汽車的保有量不斷增加,隨之而來的交通安全問題越來越引人注目,人們對汽車安全性能的要求也越來越高。因此,如何對汽車安全性能進行客觀、公正的評價,成為了研究人員急需解決的問題。然而由于許多實車試驗都是在極限工況下進行,對駕駛員的生命安全問題構(gòu)成了嚴重威脅,為此本文對駕駛員的轉(zhuǎn)向操縱行為進行研究,以期建立一個與真實的駕駛員轉(zhuǎn)向操縱行為具有良好一致性的駕駛員轉(zhuǎn)向模型取代真實的駕駛員,對汽車安全性能進行客觀、公正的評價。另外駕駛員轉(zhuǎn)向模型的研究對智能車轉(zhuǎn)向系統(tǒng)的開發(fā)也具有一定的參考價值。本文以駕駛員轉(zhuǎn)向模型為研究對象,結(jié)合車輛模型、道路模型建立人-車-路閉環(huán)控制系統(tǒng)模型,其主要的工作內(nèi)容如下:首先,建立車-路模型。第一步對車輛的橫向運動進行受力分析,建立線性二自由度車輛動力學模型;第二步將離散的道路數(shù)據(jù)點進行曲線擬合,建立S形曲線道路模型;第三步基于車輛模型和道路模型建立車、路相對位置模型,即車-路模型。隨后,建立駕駛員子系統(tǒng)模型。第一步對駕駛員轉(zhuǎn)向操縱行為進行整體分析,探明在車輛轉(zhuǎn)向行駛過程中,有哪些駕駛員子系統(tǒng)參與其中;第二步借鑒人類科學相關(guān)研究成果對參與轉(zhuǎn)向操縱行為的駕駛員子系統(tǒng)進行分析與數(shù)學建模,最終建立了基于兩點預瞄視覺機制的視覺子模型、前庭橫擺加速度感知子模型、基于大腦情感學習回路(BEL)的轉(zhuǎn)向決策子模型、手臂神經(jīng)肌肉子模型。最后,建立人-車-路閉環(huán)控制系統(tǒng)模型,并進行相關(guān)仿真分析。本部分要解決的重點問題是將前兩部分建立的車-路模型以及駕駛員子模型進行有機結(jié)合,形成人-車-路閉環(huán)控制系統(tǒng)模型。第一步建立基于BEL模型的單點預瞄駕駛員轉(zhuǎn)向模型,并將其與單點預瞄最優(yōu)曲率駕駛員轉(zhuǎn)向模型進行對比分析,驗證了BEL駕駛員決策模型的有效性以及該模型與真實駕駛員轉(zhuǎn)向決策行為的一致性;第二步在基于BEL模型的單點預瞄駕駛員轉(zhuǎn)向模型的基礎(chǔ)之上建立兩點預瞄駕駛員轉(zhuǎn)向模型,并將其與基于BEL模型的單點預瞄駕駛員轉(zhuǎn)向模型進行對比分析,驗證了兩點預瞄視覺機制優(yōu)于單點預瞄視覺機制;第三步對基于BEL模型的兩點預瞄駕駛員轉(zhuǎn)向模型與基于LQR的多點預瞄駕駛員轉(zhuǎn)向模型進行對比分析,分析結(jié)果表明在跟蹤特性上多點預瞄駕駛員轉(zhuǎn)向模型的性能優(yōu)于兩點預瞄駕駛員轉(zhuǎn)向模型,但兩點預瞄駕駛員轉(zhuǎn)向模型也能夠很好的完成目標道路軌跡的精確跟蹤,并且各項性能指標優(yōu)越,即兩者之間在跟蹤性能上差距不大。綜上所述,由于本文所建立的駕駛員轉(zhuǎn)向模型是基于對真實駕駛員轉(zhuǎn)向行為分析基礎(chǔ)之上所建立的,因此其物理意義清晰,能夠很好地再現(xiàn)真實駕駛員的轉(zhuǎn)向行為,與現(xiàn)實中駕駛員的轉(zhuǎn)向行為具有良好一致性。另外該模型能夠很好的實現(xiàn)目標道路軌跡的精確跟蹤,對智能車轉(zhuǎn)向系統(tǒng)的開發(fā)具有一定的參考價值。
[Abstract]:With the continuous improvement of people's living standards, the number of cars kept increasing, the attendant traffic safety problems are becoming more and more noticeable, and the demand for car safety performance is becoming more and more high. Therefore, how to conduct objective and fair evaluation of automobile safety performance has become an urgent problem for the researchers to solve. The test of multi real car is carried out under the extreme condition, which poses a serious threat to the driver's life safety problem. Therefore, this paper studies the steering control behavior of the driver in order to establish a driver steering model which is in good agreement with the real driver's steering control behavior to replace the real driver and to the automobile safety. In addition, the research of driver steering model also has certain reference value for the development of intelligent vehicle steering system. This paper takes the driver steering model as the research object, combines the vehicle model and the road model to establish a man car road closed loop control system model. The main work contents are as follows: first, build The first step is to analyze the lateral motion of the vehicle and establish a linear two degree of freedom vehicle dynamic model. The second step is to fit the discrete road data points and establish the S shape curve road model; the third step is based on the vehicle model and road model, the road relative position model, the car road model. Then, the vehicle model and the road model are established. The first step is to establish the driver's subsystem model. The first step is to analyze the driver's steering control behavior as a whole, and to find out what driver subsystem is involved in the steering of the vehicle, and the second step is to analyze and model the driver subsystem of the steering control by using the related research results of human science, and finally establish the model of the driver's subsystem. The visual submodel based on the two-point preview visual mechanism, the vestibule yaw acceleration perception sub model, the steering decision submodel of the brain affective learning loop (BEL) and the arm neuromuscular model. Finally, the model of the human vehicle road closed loop control system is established and the related simulation analysis is carried out. The key problem to be solved in this part is the first two. In the first step, a single point preview driver steering model based on BEL model is established, and the model is compared with the single point preview driver model of the optimal curvature driver, which validates the effectiveness of the BEL driver decision model. As well as the consistency of the model and the real driver's turning to the decision-making behavior, the second step is based on the single point preview driver steering model based on the BEL model, and establishes a two point preview driver steering model, and compares it with the single point preview driver steering model based on the BEL model, and verifies the two preview vision machine. The system is superior to the single point preview visual mechanism; the third step based on the BEL model is compared with the LQR based multi point preview driver steering model. The analysis results show that the performance of the multi point preview driver steering model is better than the two point preview driver steering model on the tracking characteristics, but the two points preview is preaimed. The driver's steering model can also accurately track the track track of the target, and the performance index is superior, that is, there is little difference in the tracking performance between the two. In summary, the driver steering model is based on the analysis of the real driver's steering behavior. The meaning is clear and the steering behavior of the real driver can be reproduced well. It is in good agreement with the driver's steering behavior in reality. In addition, the model can accurately track the path of the target Road, and has certain reference value for the development of the intelligent vehicle steering system.

【學位授予單位】：南京航空航天大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：U463.4

【參考文獻】

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1 譚運生;沈\，

本文編號：1786235