某轎車麥弗遜式懸架設(shè)計(jì)及校核,轎車,麥弗遜式,懸架,設(shè)計(jì),校核 畢 業(yè) 設(shè) 計(jì)（論 文）外 文 參 考 資 料 及 譯 文 譯文題目： 一種新的車輛導(dǎo)航圖像穩(wěn)定模型 　　　　　 學(xué)生姓名： ?！　I(yè)： 所在學(xué)院： 指導(dǎo)教師： 職　　稱： A New Image Stabilization Model for Vehicle Navigation Abstract:When a video camera is mounted on a vehicle’s frame, it experiences the same ride as a passenger and is subject to vertical displacement as the vehicle hits bumps on the road. This results in a captured video that may be difficult to watch because the bumps are transferred to the recorded video. This paper presents a new image stabilization model for vehicle navigation that can remove the effect of vertical vehicular motion due to road bumps. It uses a wheel sensor that monitors the wheel’s reaction with respect to road disturbances prior to the vehicle’s suspension system. This model employs an inexpensive sensor and control circuitry. The vehicle’s suspension system, bumpy road, and the compensation control system are modeled analytically. Experimental results show that the proposed model works successfully. It can eliminate 10 cm of drift and results in only 1 cm disturbance at the onset and the end of bumps. Keywords: Image Stabilization, Image Displacement Compensation, Vehicle Navigation 1. Introduction Vehicular imaging systems are common in police vehicles, trucking and transportation systems, rail cars, and buses. Such systems are typically mounted on the vehicle’s frame or a component of the vehicle attached to the frame (e.g. a dashboard), inheriting the ride from the vehicle’s suspension system which protects the frame and passengers. The inspection of videos captured from vehicles is typically nauseating because of the constant jittery motion that is transferred from road perturbations, dampened by the vehicles suspension, and then transferred to the camera. To remedy this problem, three primary methods are usually employed: feature registration, electro-mechanical stabilization, and optical stabilization. Each approach has own advantages and disadvantages that are briefly introduced below. A common method of image stabilization is feature registration. Brooks [1], Liang et al. [2], and Broggi et al. [3] used feature extraction to lock on a portion of an image in a frame and then employed correction transforms to attempt alignment of the locked target in subsequent frames. This approach is quite computationally intensive, so it limits its real-time applicability. A typical approach identifies a natural horizon or in some cases the lines on a road as a reference to adjust subsequent frames. Since each frame needs to be thoroughly analyzed to identify the reference object and in some cases to determine what to do when there are gaps or missing references, much processing is required for each frame. Finally, the optical stabilization approach manages a group of lenses in the imaging equipment to compensate for vibration or slow moving disturbances [6-20]. It does not act as quickly as the frame registration or electro-mechanical solutions due to its mechanical compensation. This paper describes a new image stabilization model for vehicle navigation that can remove the effect of vertical vehicular motion due to road bumps. It uses a wheel sensor that monitors the wheel’s reaction with respect to road disturbances prior to the vehicle’s suspension system.This model employs an inexpensive sensor and control circuitry. The paper is organized as follows. We present the analytical model of the baseline system in Section 2. The analytical model of the electronically stabilized system and experimental results are described in Section 3. Conclusions are drawn in Section 4. 2. Analytical Model of the Baseline System In this section, we describe a vehicle with an ordinary camera mounted to the vehicle’s frame, which demonstrates a baseline of a typical image viewing experience and the problems occurred. Figure 1 shows a model of a baseline vehicle without electronic camera stabilization. The sensor is in a front wheel assembly, and the camera may be mounted at the head of the vehicle. The schema shows a logical view of the road, wheel, suspension system, and camera assembly. The wheel is modeled as a spring ( Kw ), and the suspension system is modeled as a spring ( Ks ) and damper ( Cs ) [11]. The imaging subsystem has a rigid mount to the vehicle’s frame, and thus experiences the same response to road conditions as the vehicle frame does. Collectively, this model of springs and dampers allows one to create a control system model that can describe the behavior of the vehicle’s response to varying road conditions. The force of the ground is labeled as fg(t). The displacements of the road, wheel, and vehicle frame are denoted as Yr, Y w, and Yv, respectively. Each of these displacements is assumed to be 0-valued at the initial condition of t0. In order to determine the vehicle’s response, the parameters of the model are required. The vehicle data used for the springs and damper in this paper are adapted from [11] as an example of real parameters. These model parameters are itemized in Table 1, where Ks and Kw denote the spring rates of the suspension and tire respectively, Mv and M w denote the sprung and unsprung masses of the vehicle and wheel respectively, and Cs denotes the damper rate of the suspension spring. The suspension parameters are well known by vehicle designers. By using the logical model and representative data, we can create an analytical model. 2.1. Analytical Model Let Yr , Yw and Yv denote the vertical displacements of the road, wheel, and vehicle from their initial reference positions, respectively. When the vehicle is at rest, these values are assumed to be 0. We can derive the motion equations using Newton’s second law as: (1) Ks (Yw - Yv ) + C s (Yw - Yv ) = M v Yv (2) -Ks (Yw - Yv ) - C s (Yw - Yv ) + K w (Yr - Yw ) = M wYw Note that the single and double dot notations denote first and second derivatives with respect to time,respectively. The first derivative of any displacement Yx is its velocity, while the second derivative Yx is its acceleration. These equations can be mapped to the so-called state-space form for this particular problem. The following state-space representation is modified from [12] and is implemented in MATLAB?. Let the variable u denote the input vector, and let y denote the output vector. 2.2 Behavior of the Uncompensated System The vehicle imaging system will share the same ride with the driver. It is subject to the vehicle’s response of the suspension system that can be modeled as a low-pass filter of the bumps in the road as smoothed out by the suspension system. This is referred to as an open- loop system because there is no feedback to the imaging system. For simplicity, we model a bump in the road as a rectangular pulse, which is a 10 cm high disturbance that the vehicle rolls over for approximately 1 second. The vehicle response to this bump and the effect on the image recording are analyzed. We multiply the B matrix by 0.1 since B is the coefficient for the unit step function u in Equation (3). The step response can be obtained using the MATLAB? step( ) function. To obtain the response to the whole bump (entering and leaving), the MATLAB? lsim( ) function from the Control System Toolbox is used. In this case, the resulting response of a 1 second wide bump starting at 0.2 seconds is shown in Figure 2. The graph in Figure 2 shows the bump (dotted line), the response of the wheel ( Yw ), and the response of the vehicular frame ( Yv ) which is the representative of the camera’s response to the bump over time. The y-axis is the displacement in meters. It can be seen that the wheel response is quick and abrupt. The vehicular frame benefits from the additional spring and damper system of the car’s shock absorbers. It shows a smoother, more delayed response and produces the overshoot and undershoot of the frame. It actually ascends higher than the bump and then dips back down to a negative displacement for a short while. Note that different parameters for the springs and the damper (shock absorber) will result in different responses of the car. Figure 2. Response of a 1 second wide bump starting at 0.2 seconds. 2.3. Image Viewing Experience of the Open-Loop System We apply the aforementioned vehicle frame response to the imaging plane using the displacements of the v Y curve as offsets from a representative image. Figure 3 shows the reference scene for the effect of vehicle bounce.It is observed that the white line appears on the road and it is smooth and linear. This is visibly distorted by the bump. Next, the response of a 1 second wide bump starting at 0.2 seconds, v Y , shown in Figure 2, is mapped to the image of the viewer’s experience. This is done by sweeping through the image left-to-right and a vertical row at a time. Each vertical row represents a time interval. Each vertical row of pixels is viewed as a point in time or a frame of a video sequence. By taking the signal v ( ) Y t and scale it to displace the row of image pixels over time,the effect of a video is created. From the vehicle’s perspective, a comfortable ride for the user and a safe ride for the vehicle are paramount.From the camera’s perspective, we wish to completely flatten out this response, so the view is level. From Figure 4, the representative image viewed on the display of an unstabilized imaging system results in a view that has both large disturbances and a lasting effect because of the damping response of the suspension. It is the effect to be eliminated with electronic image stabilization. Figure 3. The reference scene for the effect of vehicle bounce. 3. Analytical Model of the Electronically Stabilized System In this section, we present the analytical model of the electronically stabilized system. Some suspension systems are characterized as an active suspension, in which the damper Cs is an active element whose stiffness is adjusted by an active control system [10]. The closedloop nature of the system is resulted from feedback and control in the electrical form, feeding the imaging subsystem.Thus, the closed-loop control is for the image display platform, not for the vehicle’s ride. This closedloop attribute allows the imaging subsystem to monitor the response of the wheel on the road and make adjustments that coincide with the vehicle’s frame which holds the imaging subsystem [13-16]. Figure 1 is modified by a sensor device attached to the vehicle’s wheel assembly as shown in Figure 4. The sensor relays an analog electrical voltage signal that provides a linear measurement of the displacement of the sensor’s internal piston. This could be a low-friction potentiometer assembly. As part of the imaging subsystem, the sensor input is digitized to provide a real-time feed of displacement data sampled at the rate of 100 samples per second. imaging subsystem vehicle frame vehicle mass (Mv) Yv rigid assembly suspension system analog Ks Cs sensor output wheel mass Yw displacement sensor (Mw) wheel fg(t) Yr road Kw force from ground Figure 4. Model of image stabilizer physical assembly. With the addition of a sensor and its output, we can further elaborate the diagram to show the logical view of the model which includes the active controller for image stabilization. This new model is depicted in Figure 5, which shows that a video camera assembly with a large vertical aspect ratio is used to gain an extra vertical displacement margin. This is necessary because the compensator will make use of this slack to realign the picture. If the extra image from this margin is available, it helps remove blank information from being in the compensated frames. The output of the camera is fed to an image processing block, called a displacement compensation control function G(z). This block receives the wheel sensor’s displacement data, Yw (t ) as an input to the block. The processing function in the G(z) block is applied to compensate for the response that the frame is about to experience as a result of a bump on the road. After computation, it arrives at a vertical displacement compensation by which the received frame needs to be adjusted to give appearance of a smooth video. This is simply a re-registration of the vertical offset of the frame. 譯文： 一種新的車輛導(dǎo)航圖像穩(wěn)定模型 摘要：當(dāng)一個攝像機(jī)被安裝在車輛的車架上時(shí)，它會體驗(yàn)到同一個乘客一樣，當(dāng)車輛在路面上顛簸時(shí)，會受到垂直位移的沖擊。這導(dǎo)致在捕獲的視頻，可能難以觀看，因?yàn)轭嶔け晦D(zhuǎn)移到錄制的視頻。本文提出了一種新的車輛導(dǎo)航圖像穩(wěn)定模型，可以消除由于道路顛簸產(chǎn)生的垂直運(yùn)動的影響。它使用了一個車輪傳感器，該傳感器監(jiān)測車輪的反應(yīng)，在車輛的懸架系統(tǒng)之前的道路干擾。該模型采用了廉價(jià)的傳感器和控制電路。車輛的懸架系統(tǒng)，顛簸的道路，和補(bǔ)償控制系統(tǒng)建模分析。實(shí)驗(yàn)結(jié)果表明，該模型成功地工作。它可以消除10厘米的漂移和只產(chǎn)生1厘米的干擾在發(fā)病和結(jié)束顛簸。 關(guān)鍵詞：圖像穩(wěn)定，圖像位移補(bǔ)償，車輛導(dǎo)航 1. 簡介 車載成像系統(tǒng)在警車，貨運(yùn)和運(yùn)輸系統(tǒng)，軌道車和公共汽車是很常見的。軌道車和公共汽車。這樣的系統(tǒng)通常安裝在車輛的車架上或連接到依附車輛車架的（如儀表板），繼承其保護(hù)框架和乘客乘坐從車輛的懸掛系統(tǒng)。捕獲的車輛視頻檢測通常是令人作嘔的由于持續(xù)的緊張運(yùn)動，這一運(yùn)動從道路擾動轉(zhuǎn)移，由車輛懸架的阻尼，然后轉(zhuǎn)到相機(jī)。為了解決這個問題，通常采用三個主要的方法：特征匹配，機(jī)電穩(wěn)定，光學(xué)穩(wěn)定。每一種方法都有自己的優(yōu)缺點(diǎn)，下面簡要介紹。 一種常見的圖像穩(wěn)定方法是特征匹配。布魯克斯，梁和布羅基等人使用特征提取鎖定一幀圖像的一部分，然后使用校正變換來嘗試在后續(xù)幀中鎖定目標(biāo)的對齊方式。這種方法是計(jì)算密集型的，所以它限制了它的適時(shí)實(shí)用性。一個典型的方法確定一個基本標(biāo)準(zhǔn)，或在某些情況下，作為一個參考，已調(diào)整后續(xù)幀的路線。由于每一幀需要進(jìn)行徹底的分析，以確定參考對象，在某些情況下，以確定什么時(shí)候有空白或缺少的引用，需要多處理每個幀。 第二種方法是機(jī)電穩(wěn)定，采用慣性系統(tǒng)或陀螺儀檢測運(yùn)動的變化和改正透鏡組或成像平面。近年來基于微電子機(jī)械系統(tǒng)（MEMS）技術(shù)允許陀螺儀集成到特定應(yīng)用集成電路（ASIC）。陀螺儀測量位移，它是發(fā)送到一個電子圖像穩(wěn)定系統(tǒng)執(zhí)行更正。對慣性變化進(jìn)行精確測量和濾波，并對圖像幀進(jìn)行調(diào)整。隨著集成陀螺儀的價(jià)格繼續(xù)下跌，這種方法現(xiàn)在變得更有利。 最后，光穩(wěn)定方法管理一組成像設(shè)備的鏡頭來補(bǔ)償振動或減緩移動的障礙。由于其機(jī)械補(bǔ)償，它并不作為一幀的快速匹配或機(jī)電解決方案。 本文介紹了一種車輛導(dǎo)航新的圖像穩(wěn)定模型，該模型可以消除由于道路顛簸所產(chǎn)生垂直的車輛運(yùn)動的影響。它使用了一個車輪傳感器，該傳感器監(jiān)測關(guān)于車輛懸架系統(tǒng)前的道路干擾的車輪反應(yīng)。該模型采用了廉價(jià)的傳感器和控制電路。論文組織如下。我們提出的基線系統(tǒng)的分析模型在第2節(jié)。電子穩(wěn)定系統(tǒng)的分析模型和實(shí)驗(yàn)結(jié)果第3節(jié)中描述的。第4節(jié)中得出結(jié)論。 2、基線系統(tǒng)的分析模型 在這一節(jié)中，我們描述了一個普通的攝像頭安裝到車輛的懸架上的車輛，這說明了一個典型的圖像觀看體驗(yàn)的基線和車輛發(fā)生的問題。圖1顯示了一個沒有電子攝像機(jī)穩(wěn)定化的基線車輛模型。該傳感器是在前輪組件中，照相機(jī)可安裝在該車輛的頭部。該模式顯示了道路、車輪、懸架系統(tǒng)和相機(jī)組件的邏輯視圖。車輪被建模為一個彈簧（千瓦），和懸架系統(tǒng)建模為一個彈簧（KS）和阻尼器（CS）。該成像子系統(tǒng)具有可安裝到車架的剛性，從而體驗(yàn)到相同反應(yīng)的車架的道路條件?？偟膩碚f，這種彈簧和阻尼器的模式允許一個創(chuàng)造一個能夠描述不同道路條件下車輛的響應(yīng)行為的控制系統(tǒng)模型。地面的力稱為FG（T），道路、車輪和車架位移分別表示為Yr，Y w和 Yv。這些位移被假定為0值在T0的初始條件。 為了確定車輛的響應(yīng)，模型的參數(shù)是必需的。本文用于彈簧和阻尼器的車輛數(shù)據(jù)作為實(shí)際參數(shù)的例子，改編自[11]。這些模型的參數(shù)在表1列， Ks和Kw分別表示懸架和輪胎的彈簧剛度，Mv 和M w分別表示汽車和輪胎簧載和非簧載質(zhì)量，Cs表明懸架彈簧的阻尼率。懸架參數(shù)是車輛設(shè)計(jì)人員眾所周知的。通過使用邏輯模型和有代表性的數(shù)據(jù)，我們可以創(chuàng)建一個分析模型。 2.1分析模型 用Yr，Yw 和Yv 分別表示從他們初始參考位置的道路，車輪和車輛的垂直位移。當(dāng)車輛休息時(shí)，這些值被假定為0。我們可以用牛頓第二定律導(dǎo)出方程： (1) Ks (Yw - Yv ) + C s (Yw - Yv ) = M v Yv (2) -Ks (Yw - Yv ) - C s (Yw - Yv ) + K w (Yr - Yw ) = M wYw 請注意，單和雙點(diǎn)符號分別表示關(guān)于時(shí)間的一階和二階導(dǎo)數(shù)。任何位移的一階導(dǎo)數(shù)的速度是Yx，二階導(dǎo)數(shù)Yx是其加速度。這些方程可以針對特定的問題被映射到所謂的狀態(tài)空間形式中。以下的狀態(tài)空間表示改編自[ 12 ]及在MATLAB?實(shí)施。用變量u表示輸入向量，變量y表示輸出向量。 2.2未補(bǔ)償系統(tǒng)行為 車輛成像系統(tǒng)與司機(jī)共用同一個旅程。它是受車輛的懸架系統(tǒng)的響應(yīng)，可以建模為一個低通濾波器在路面上的顛簸，像懸架系統(tǒng)平滑一樣。這被稱為一個開環(huán)系統(tǒng)，因?yàn)闆]有反饋到成像系統(tǒng)。為簡單起見，我們模擬了一個顛簸的道路作為一個矩形脈沖，這是一個10厘米高的干擾，車輛滾動約1秒。分析了車輛對該凹凸的響應(yīng)，并分析了其對圖像記錄的影響。我們將B矩陣乘以0.1，因?yàn)锽在方程（3）中是單位階躍函數(shù)的系數(shù)。階躍響應(yīng)可以使用MATLAB?步得到（）函數(shù)。為了獲得對整個碰撞響應(yīng)（進(jìn)出），MATLAB?lsim（）使用控制系統(tǒng)工具箱函數(shù)。在這種情況下，產(chǎn)生的響應(yīng)的1秒寬的撞擊開始在0.2秒，如圖2所示。 圖2顯示了凹凸（虛線），車輪的響應(yīng)（YW），以及車輛幀的響應(yīng)（電視）以隨時(shí)間相機(jī)對撞擊的響應(yīng)為代表。Y軸是以米為單位的位移，可以看出，車輪的響應(yīng)是快速和突然的。車架受益于汽車減震器附加彈簧和阻尼器系統(tǒng)。它顯示了一個更流暢，更延遲反應(yīng)和產(chǎn)生的過沖的框架。它實(shí)際上上升高于凸點(diǎn)，之后一段時(shí)間下降到一個負(fù)位移。請注意，彈簧的不同參數(shù)和阻尼器（減震器）將導(dǎo)致不同汽車的反應(yīng)。 Figure 2. Response of a 1 second wide bump starting at 0.2 seconds. 2.3開環(huán)系統(tǒng)的圖像瀏覽體驗(yàn) 我們采用上述車輛幀響應(yīng)利用電視成像平面的位移曲線作為代表圖像的偏移量。圖3為車輛彈跳效果顯示參考場景。觀察到白線出現(xiàn)在道路上，它是光滑的和線性的。這是明顯被撞擊所彎曲。 接著，一個1秒的響應(yīng)，開始在0.2秒，如圖2所示，映射到觀眾體驗(yàn)的形象。這是通過掃描圖像左至右和一個垂直的行在時(shí)間。每一個垂直行代表一個時(shí)間間隔。每個垂直行的像素被視為一個點(diǎn)的時(shí)間或視頻序列幀。以信號電視（T）并將其縮放，隨著時(shí)間的推移以取代該行的圖像像素，創(chuàng)建視頻的效果。 從車輛的角度看，用戶的乘坐舒適和車輛安全駕駛是最重要的。從相機(jī)的角度來看，我們希望完全把這種反應(yīng)變平，這樣的觀點(diǎn)是水平的。由圖4，代表圖像顯示在顯示在視圖中，由一個穩(wěn)定的成像系統(tǒng)導(dǎo)致了這兩個大的干擾和持久的效果，因?yàn)閼壹茏枘犴憫?yīng)。這起到了消除電子穩(wěn)像的效果。 Figure 3. The reference scene for the effect of vehicle bounce. 3. 電子穩(wěn)定系統(tǒng)的分析模型 在這一節(jié)中，我們提出了電子穩(wěn)定系統(tǒng)的分析模型。一些懸掛系統(tǒng)系統(tǒng)被定性為主動懸架，其中阻尼器是一個活躍的元素，其剛度由一個主動控制系統(tǒng)調(diào)整[ 10 ]。系統(tǒng)的關(guān)閉—循環(huán)性質(zhì)是由電氣形式的反饋和控制產(chǎn)生，給成像子系統(tǒng)進(jìn)料。因此，閉環(huán)控制為了圖像顯示平臺，不為車輛的行駛。這關(guān)閉—循環(huán)屬性允許成像子系統(tǒng)監(jiān)測車輪對路面的響應(yīng)和調(diào)整成車的車架一致的成像子系統(tǒng)[ 13-16 ]。 圖1是由一個連接到該車輛的車輪組件如圖4所示。該傳感器繼電器的模擬電壓信號，提供了一個傳感器的內(nèi)部活塞位移的線性測量。這可能是一個低摩擦電位裝配。作為成像子系統(tǒng)的一部分，傳感器的輸入被數(shù)字化，以速率為100個樣本每秒提供一個實(shí)時(shí)的進(jìn)料的位移數(shù)據(jù)采樣。 imaging subsystem vehicle frame vehicle mass (Mv) Yv rigid assembly suspension system analog Ks Cs sensor output wheel mass Yw displacement sensor (Mw) wheel fg(t) Yr road Kw force from ground Figure 4. Model of image stabilizer physical assembly. 隨著傳感器和它的輸出，我們可以進(jìn)一步闡述該圖顯示的邏輯視圖的模型，其中包括主動控制器的圖像穩(wěn)定。這個新模式是圖5所示為一個大的垂直長寬比的攝像機(jī)組件，用于獲得一個額外的垂直位移裕度。這是必要的，因?yàn)樵撗a(bǔ)償器將利用這種松弛調(diào)整圖片。 如果從該空白處獲得額外的圖像，它可以幫助消除在補(bǔ)償幀中的空白信息。相機(jī)的輸出是輸入到圖像處理塊，稱為位移管理補(bǔ)償控制函數(shù)g（z）。此塊接收車輪傳感器的位移數(shù)據(jù)，YW（T）對塊的輸入。在塊的處理功能，以彌補(bǔ)由于在路上顛簸對于該幀的響應(yīng)。經(jīng)過計(jì)算，它到達(dá)一個垂直位移補(bǔ)償所接收的幀需要調(diào)整后，使視頻外觀平順。這只是一個重新注冊的幀的垂直偏移量。