本文關(guān)鍵詞：基于車輛行程數(shù)據(jù)的道路特征識別　出處：《華南理工大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 特征識別 分類問題 序列標(biāo)注 行程數(shù)據(jù) 駕駛操作

【摘要】：蜿蜒起伏的道路形狀是影響車輛燃油消耗的重要因素之一,主要體現(xiàn)在兩個方面:一是車輛在起伏的道路上行駛時動能與勢能之間的轉(zhuǎn)換需要消耗較多的能量;二是當(dāng)?shù)缆返男螤钐卣靼l(fā)生變化時不采取合適的駕駛操作會導(dǎo)致高油耗損失,最高能占據(jù)平均燃油消耗的30%�？梢娫诨诘缆沸螤顢�(shù)據(jù)的基礎(chǔ)上可以實現(xiàn)節(jié)能運(yùn)輸線路規(guī)劃、節(jié)能駕駛提醒等改善物流車輛油耗水平的應(yīng)用,然獲得道路的形狀數(shù)據(jù)是一個復(fù)雜的系統(tǒng)工程,本文研究如何智能化獲取道路形狀數(shù)據(jù)的問題。道路特征包括位置、拓?fù)浣Y(jié)構(gòu)及形狀特征,拓?fù)浣Y(jié)構(gòu)描述道路網(wǎng)絡(luò)的連通關(guān)系,形狀特征包括平面特征及縱面特征,在平面特征包括左彎路、直路、右彎路,縱面特征包括上坡路、平路、下坡路。目前道路特征識別研究集中在拓?fù)浣Y(jié)構(gòu)的獲取,主要有人工測量、影像識別及行程數(shù)據(jù)推斷三種,而對形狀特征的獲取研究極少,主要采用人工測量的方式,這種方式成本極高,且數(shù)據(jù)更新速度緩慢。因此本文提出采用行程數(shù)據(jù)對形狀特征進(jìn)行識別的方案:在正常的駕駛操作中,駕駛員根據(jù)實際的道路形狀而采取相應(yīng)的駕駛操作,可見車輛行程數(shù)據(jù)中隱含著道路形狀信息,因此可以通過從行程大數(shù)據(jù)中構(gòu)建合適的屬性集,然后根據(jù)屬性集對道路特征進(jìn)行反向推斷。本文的主要工作包括構(gòu)建合適的屬性集合及對道路特征識別問題進(jìn)行建模。在本文中通過兩種方式構(gòu)建屬性集,一是以經(jīng)過同一路段的多次行程的數(shù)據(jù)項的統(tǒng)計數(shù)據(jù)作為屬性集,稱為統(tǒng)計屬性;二是在行程數(shù)據(jù)的基礎(chǔ)上首先識別出駕駛操作,以駕駛操作分布數(shù)據(jù)作為屬性集,稱為操作屬性。道路形狀識別是在已知道路位置及拓?fù)鋽?shù)據(jù)的基礎(chǔ)上對形狀進(jìn)行分類或者標(biāo)注:把該問題建模為分類及序列標(biāo)注模型,然后采用加權(quán)K最近鄰、決策樹、樸素貝葉斯、隱馬爾科夫模型進(jìn)行求解,實驗結(jié)果表明平面特征的準(zhǔn)確識別率可高達(dá)99%以上,而縱面特征的準(zhǔn)確識別率可高達(dá)90%以上,此外還發(fā)現(xiàn)以操作分布作為后續(xù)的分類或標(biāo)注模型的輸入屬性要優(yōu)于直接對原始數(shù)據(jù)項的統(tǒng)計特征。
[Abstract]:The winding road shape is one of the important factors that affect vehicle fuel consumption, mainly reflected in two aspects: one is the vehicle on the up and down conversion between kinetic energy and potential energy consumes more energy; two is the shape feature when the road changes do not take appropriate driving operation leads to high fuel consumption the highest loss, can occupy the average fuel consumption of 30%. visible based on road shape data can be achieved on the energy transport line planning, energy saving driving reminder application of improved logistics vehicle fuel consumption level, so the road shape data obtained is a complex system engineering, this paper studies how to obtain intelligent road shape data. The road features including location, topology and shape features, describe the topology connectivity of road network, including plane feature and shape feature profile Features in the plane features include left bend road, straight, right detours, vertical features including uphill, downhill road, road recognition. Current research focused on the topology acquisition, mainly artificial measurement, image recognition and travel data from three, and the shape features of the acquisition of few studies, mainly by the manual measurement method, the high cost, and the data update speed is slow. Therefore this paper proposes a recognition method using shape features of the travel data: in the normal operation of the driver, the driver to take corresponding driving operation according to the actual road shape, visible vehicle travel data implies the road shape information, so you can by constructing appropriate attribute from the stroke data set, then according to the attribute set of reverse inference on the road features. The main work of this paper includes the construction of appropriate attribute set and Modeling of road feature recognition. Construct attribute set by two methods in this paper, one is the statistical data after several stroke of the same section of the data item as the attribute set, called statistical properties; two is based on data on the first trip to identify the driving operation, the driving operation data distribution as a set of attributes, called operation attributes. Road shape recognition is based on the known road location and topological data on the shape classification or mark: the problem of modeling for classification and sequence labeling model, and then use the weighted K nearest neighbor, decision tree, Naive Bayesian, solve the hidden Markov model, the experimental results show that accurate the recognition of planar feature rate can be as high as 99%, and the accurate identification of characteristics of vertical rate can reach more than 90%, in addition to the operation of distribution as the following classification or annotation model input Properties are superior to the statistical characteristics of the original data items directly.

【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TP391.41

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