本文選題：跟車模型 + 神經網絡��； 參考：《長安大學》2015年碩士論文

【摘要】：車輛的跟馳行為是車輛行駛中的常見駕駛行為之一,特定的駕駛人由于其跟車過程中心理感知等因素的差異,其行車車距和相對速度等安全范圍均不同。如果能夠對駕駛人的這種跟車特性進行模擬,建立起特定的跟車模型,就可以比較相似跟車狀態(tài)下的駕駛人的跟車行為是否存在異常。采取相應的措施對危險的跟車行為及時進行預警,就能夠有效降低事故的發(fā)生率。本文通過實際道路跟車試驗,使用視頻監(jiān)控系統(tǒng)和毫米波雷達等試驗設備對車輛跟車過程中的相關跟車數(shù)據進行采集。通過分析穩(wěn)定跟車過程中影響駕駛員進行加減速操作的相關數(shù)據,進而基于篩選出有效的試驗數(shù)據進行預測模型研究。本文的主要研究內容和結論:(1)通過對跟車模型的相關研究回顧,提取試驗數(shù)據中與駕駛員進行加減速操作相關的車輛運行參數(shù)和道路環(huán)境數(shù)據等。分析初步得出車輛相對速度、相對距離和本車速度可作為模型的特征輸入參數(shù)。(2)通過對初步提取的特征參數(shù)數(shù)據進行處理,建立起B(yǎng)P神經網絡跟車模型。通過預測分析可知BP模型很容易陷入局部極值點,而這種問題不能通過其自身結構的優(yōu)化解決,因此考慮使用遺傳算法來對其進行優(yōu)化。(3)遺傳算法對BP神經網絡模型進行結構優(yōu)化后,結果表明以車輛相對速度、相對距離和本車速度為組合的跟車行為預測模型準確度最高,但也僅為90.29%。通過反復試驗可知經過遺傳算法優(yōu)化后的雙隱含層的BP神經網絡能夠將預測準確率提高到94.17%。結果表明遺傳算法優(yōu)化后的雙隱含層BP神經網絡跟車模型,能夠對車輛的跟車狀態(tài)進行很好地預測。本研究得到了教育部長江學者與創(chuàng)新團隊支持計劃項目(IRT1286)和交通運輸部應用基礎研究項目(2013319812150)的資助。
[Abstract]:Car-following behavior is one of the common driving behaviors in vehicle driving. Because of the difference of psychological perception in the process of following the vehicle, the safety range of driving distance and relative speed are different. If we can simulate the characteristics of the driver and set up a specific model, we can compare whether the behavior of the driver in the similar state is abnormal or not. It can effectively reduce the incidence of accidents by taking corresponding measures to warn the dangerous following behavior in time. In this paper, we use video surveillance system and millimeter-wave radar to collect the data of vehicle following through the actual road following test. Through the analysis of the relevant data which affect the driver's acceleration and deceleration operation in the process of stable following the vehicle, the prediction model is studied based on the screening of effective test data. The main contents and conclusions of this paper are as follows: (1) based on the review of the related research on the following model, the vehicle operation parameters and the road environment data related to the driver's acceleration and deceleration operation are extracted from the test data. The relative speed, relative distance and vehicle speed of the vehicle can be regarded as the characteristic input parameters of the model. The BP neural network model is established by processing the data of the initial extracted characteristic parameters. The prediction analysis shows that BP model is easy to fall into local extremum, but this problem can not be solved by optimizing its own structure. Therefore, considering the use of genetic algorithm to optimize the structure of BP neural network model, the results show that the combination of vehicle relative speed, relative distance and vehicle speed is the most accurate model for prediction of car-following behavior. But only 90.29. Through repeated experiments, it can be seen that the BP neural network with double hidden layers optimized by genetic algorithm can improve the prediction accuracy to 94.17%. The results show that the double hidden layer BP neural network model, which is optimized by genetic algorithm, can well predict the vehicle following state. This study was supported by the Ministry of Education's Yangtze River Scholars and Innovation team support Project (IRT1286) and the Ministry of Transport's Applied basic Research Project (2013319812150).
【學位授予單位】：長安大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U491.255;TP18

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本文編號：1985090