現在介紹一個專案，這個專案跟優達學城的課程有點像，可以學習一下.。

  不管是含有ADAS功能的車還是依賴於多種感測器設計的自動駕駛汽車，它們需要的感測器都包括超聲波、雷達、鐳射雷達和攝像頭。接下來的專案闡述的是用單目相機實現自動駕駛過程中一部分內容，實現的內容如下：

1.車道線檢測

2.檢測機動車，行人以及另一些物體。

3.判斷自己車與物體的距離。

1.定義相機配置

focalLength    = [309.4362, 344.2161]; % [fx, fy] in pixel units
principalPoint = [318.9034, 257.5352]; % [cx, cy] optical center in pixel coordinates
imageSize      = [480, 640];           % [nrows, mcols]

camIntrinsics = cameraIntrinsics(focalLength, principalPoint, imageSize);

接下來，定義相對於車輛底盤的攝像機位置，

注意，單目像機設定一個非常具體的車體座標。其中X軸指向車輛前方，Y軸指向車輛左側，Z軸從地面上指向天空。

座標系的遠點在地面上，直接在的攝像機中心的正下方。

這裡給出相機相對座標引數：

height = 2.1798;    % mounting height in meters from the ground
pitch  = 14;        % pitch of the camera in degrees

sensor = monoCamera(camIntrinsics, height, 'Pitch', pitch);

2.匯入視訊

videoName = 'caltech_cordova1.avi';
videoReader = VideoReader(videoName);

讀感興趣的部分，其中包含車道標誌和車輛。

timeStamp = 0.06667;                   % time from the beginning of the video
videoReader.CurrentTime = timeStamp;   % point to the chosen frame

frame = readFrame(videoReader); % read frame at timeStamp seconds
imshow(frame) % display frame

3.建立鳥瞰圖（程式後面都有註釋）

% Using vehicle coordinates, define area to transform
distAheadOfSensor = 30; % in meters, as previously specified in monoCamera height input
spaceToOneSide    = 6;  % all other distance quantities are also in meters
bottomOffset      = 3;

outView   = [bottomOffset, distAheadOfSensor, -spaceToOneSide, spaceToOneSide]; % [xmin, xmax, ymin, ymax]
imageSize = [NaN, 250]; % output image width in pixels; height is chosen automatically to preserve units per pixel ratio

birdsEyeConfig = birdsEyeView(sensor, outView, imageSize);

生成鳥瞰圖

birdsEyeImage = transformImage(birdsEyeConfig, frame);
figure
imshow(birdsEyeImage)

車輛座標中的車道標誌

% Convert to grayscale
birdsEyeImage = rgb2gray(birdsEyeImage);

% Lane marker segmentation ROI in world units
vehicleROI = outView - [-1, 2, -3, 3]; % look 3 meters to left and right, and 4 meters ahead of the sensor
approxLaneMarkerWidthVehicle = 0.25; % 25 centimeters

% Detect lane features
laneSensitivity = 0.25;
birdsEyeViewBW = segmentLaneMarkerRidge(birdsEyeImage, birdsEyeConfig, approxLaneMarkerWidthVehicle,...
    'ROI', vehicleROI, 'Sensitivity', laneSensitivity);

figure
imshow(birdsEyeViewBW)

% Obtain lane candidate points in vehicle coordinates
[imageX, imageY] = find(birdsEyeViewBW);
xyBoundaryPoints = imageToVehicle(birdsEyeConfig, [imageY, imageX]);

maxLanes      = 2; % look for maximum of two lane markers
boundaryWidth = 3*approxLaneMarkerWidthVehicle; % expand boundary width

[boundaries, boundaryPoints] = findParabolicLaneBoundaries(xyBoundaryPoints,boundaryWidth, ...
    'MaxNumBoundaries', maxLanes, 'validateBoundaryFcn', @validateBoundaryFcn);

決定車道邊界

在上面程式中，我們找到的曲線有可能是無效的，例如，當曲線擬合到人行橫道標誌時，用另一些方法去刪除這樣的曲線：

% Establish criteria for rejecting boundaries based on their length
maxPossibleXLength = diff(vehicleROI(1:2));
minXLength         = maxPossibleXLength * 0.60; % establish a threshold

% Reject short boundaries
isOfMinLength = arrayfun(@(b)diff(b.XExtent) > minXLength, boundaries);
boundaries    = boundaries(isOfMinLength);

% To compute the maximum strength, assume all image pixels within the ROI
% are lane candidate points
birdsImageROI = vehicleToImageROI(birdsEyeConfig, vehicleROI);
[laneImageX,laneImageY] = meshgrid(birdsImageROI(1):birdsImageROI(2),birdsImageROI(3):birdsImageROI(4));

% Convert the image points to vehicle points
vehiclePoints = imageToVehicle(birdsEyeConfig,[laneImageX(:),laneImageY(:)]);

% Find the maximum number of unique x-axis locations possible for any lane
% boundary
maxPointsInOneLane = numel(unique(vehiclePoints(:,1)));

% Set the maximum length of a lane boundary to the ROI length
maxLaneLength = diff(vehicleROI(1:2));

% Compute the maximum possible lane strength for this image size/ROI size
% specification
maxStrength   = maxPointsInOneLane/maxLaneLength;

% Reject weak boundaries
isStrong      = [boundaries.Strength] > 0.4*maxStrength;
boundaries    = boundaries(isStrong);

boundaries = classifyLaneTypes(boundaries, boundaryPoints);

% Locate two ego lanes if they are present
xOffset    = 0;   %  0 meters from the sensor
distanceToBoundaries  = boundaries.computeBoundaryModel(xOffset);

% Find candidate ego boundaries
leftEgoBoundaryIndex  = [];
rightEgoBoundaryIndex = [];
minLDistance = min(distanceToBoundaries(distanceToBoundaries>0));
minRDistance = max(distanceToBoundaries(distanceToBoundaries<=0));
if ~isempty(minLDistance)
    leftEgoBoundaryIndex  = distanceToBoundaries == minLDistance;
end
if ~isempty(minRDistance)
    rightEgoBoundaryIndex = distanceToBoundaries == minRDistance;
end
leftEgoBoundary       = boundaries(leftEgoBoundaryIndex);
rightEgoBoundary      = boundaries(rightEgoBoundaryIndex);

在鳥瞰檢視影象中顯示檢測到的車道標誌，並在常規視角中顯示：

xVehiclePoints = bottomOffset:distAheadOfSensor;
birdsEyeWithEgoLane = insertLaneBoundary(birdsEyeImage, leftEgoBoundary , birdsEyeConfig, xVehiclePoints, 'Color','Red');
birdsEyeWithEgoLane = insertLaneBoundary(birdsEyeWithEgoLane, rightEgoBoundary, birdsEyeConfig, xVehiclePoints, 'Color','Green');

frameWithEgoLane = insertLaneBoundary(frame, leftEgoBoundary, sensor, xVehiclePoints, 'Color','Red');
frameWithEgoLane = insertLaneBoundary(frameWithEgoLane, rightEgoBoundary, sensor, xVehiclePoints, 'Color','Green');

figure
subplot('Position', [0, 0, 0.5, 1.0]) % [left, bottom, width, height] in normalized units
imshow(birdsEyeWithEgoLane)
subplot('Position', [0.5, 0, 0.5, 1.0])
imshow(frameWithEgoLane)

定位車輛座標

檢測與跟蹤汽車對前碰撞報警系統（FCW）和自動緊急剎車系統（ABE）是至關重要的。

detector = vehicleDetectorACF();

% Width of a common vehicle is between 1.5 to 2.5 meters
vehicleWidth = [1.5, 2.5];

monoDetector = configureDetectorMonoCamera(detector, sensor, vehicleWidth);

[bboxes, scores] = detect(monoDetector, frame);

locations = computeVehicleLocations(bboxes, sensor);

% Overlay the detections on the video frame
imgOut = insertVehicleDetections(frame, locations, bboxes);
figure;
imshow(imgOut);

問題：

1.你知道怎麼把圖片轉化為鳥瞰圖嗎？

2.你知道怎麼擬合車道邊界嗎？

3.怎麼決定車道標記，如人行道干擾，還有車道有的是實線，有的是虛線？

4.怎麼定位車輛座標？