【摘要】：駕駛員車輛控制模型不但可以揭示駕駛員的駕駛機理，而且可以計算、仿真和預(yù)測駕駛員的車輛控制行為，幫助改進車輛和輔助駕駛系統(tǒng)的設(shè)計過程。此外，它們可以為智能輔助駕駛系統(tǒng)和無人駕駛車輛控制技術(shù)的研究提供新的思路。因此，駕駛員車輛控制模型的研究有重要的科學(xué)意義和實用價值。 現(xiàn)有的研究或從控制（包括駕駛員神經(jīng)肌肉系統(tǒng)方面）或從認知角度發(fā)展駕駛員車輛控制模型。因此，這些模型不能全面的揭示駕駛員的控制機理和仿真駕駛員的車輛控制行為，這也限制了它們在幫助發(fā)展輔助駕駛系統(tǒng)等方面的價值。為了解決上述問題，本文建立了融合神經(jīng)肌肉動力學(xué)和QN認知體系的駕駛員車輛側(cè)向控制模型，，提出了運動輔助任務(wù)條件下的駕駛員車輛側(cè)向控制的雙任務(wù)建模方法。該模型以QN認知體系為框架可以反映駕駛員認知能力和局限，與控制理論的結(jié)合可以實現(xiàn)車輛控制的數(shù)學(xué)描述，融入神經(jīng)肌肉動力學(xué)可以體現(xiàn)駕駛員的神經(jīng)肌肉系統(tǒng)和車輛轉(zhuǎn)向系統(tǒng)之間的動態(tài)交互。 本論文取得的主要創(chuàng)新性成果： 1）改進了基于QN認知體系的駕駛員模型，提出了表征駕駛轉(zhuǎn)向控制的神經(jīng)肌肉動力學(xué)模型，在此基礎(chǔ)上，通過融合神經(jīng)肌肉動力學(xué)和QN認知體系模型，建立了一種新穎的駕駛員車輛側(cè)向控制模型。 2）利用建立的駕駛員模型，揭示了神經(jīng)肌肉動力學(xué)參數(shù)（包括肌肉協(xié)同收縮剛度和反射控制增益）對駕駛員側(cè)向控制性能的影響規(guī)律。 3）在建立的駕駛員模型的基礎(chǔ)上，通過應(yīng)用多任務(wù)調(diào)度方法，提出了一種運動輔助任務(wù)條件下的駕駛員車輛側(cè)向控制的雙任務(wù)建模方法。 這些創(chuàng)新成果的取得不僅可以幫助更好地揭示駕駛員的車輛控制機理，解釋運動輔助任務(wù)對駕駛主任務(wù)的影響，也可以為智能車輛和智能輔助駕駛系統(tǒng)的研究和開發(fā)提供支持。
[Abstract]:The driver's vehicle control model can not only reveal the driver's driving mechanism, but also can calculate, simulate and predict the driver's vehicle control behavior, and help to improve the design process of vehicle and auxiliary driving system. In addition, they can provide new ideas for the research of intelligent auxiliary driving system and driverless vehicle control technology. Therefore, the study of driver's vehicle control model has important scientific significance and practical value. Existing studies have developed driver's vehicle control models either in terms of control (including driver's neuromuscular systems) or from a cognitive perspective. Therefore, these models can not fully reveal the driver's control mechanism and the simulation of the driver's vehicle control behavior, which limits their value in helping to develop the auxiliary driving system. In order to solve the above problems, a driver's lateral control model based on neuromuscular dynamics and QN cognitive system is established, and a two-task modeling method for driver's lateral control under the condition of motion assistant task is proposed. The model based on QN cognitive system can reflect the cognitive ability and limitation of drivers, and the mathematical description of vehicle control can be realized by combining with control theory. The integration of neuromuscular dynamics can reflect the dynamic interaction between the driver's neuromuscular system and the vehicle steering system. The main innovative achievements in this thesis are as follows: 1) the driver model based on QN cognitive system is improved, and the neuromuscular dynamics model which represents the steering control is proposed. By combining neuromuscular dynamics with QN cognitive system model, a novel driver lateral control model was established. 2) the influence of neuromuscular dynamic parameters (including muscle cocontraction stiffness and reflex control gain) on driver's lateral control performance is revealed by using the driver's model. 3) based on the established driver model, a two-task modeling method for driver lateral control under the condition of motion assistant task is proposed by applying the multi-task scheduling method. The achievement of these innovations can not only help to better reveal the mechanism of driver's vehicle control, explain the influence of motion assistant task on driving main task, but also provide support for the research and development of intelligent vehicle and intelligent auxiliary driving system.
【學(xué)位授予單位】：北京理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：U491.25

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