Simulink开发项1000例实战专栏--实例188：基于Simulink的自动驾驶车辆路径平滑处理仿真

 % 创建新的Simulink模型

    
 modelName = 'AutonomousVehiclePathSmoothing';
    
 new_system(modelName);
    
 open_system(modelName);

 % 添加自行车模型模块

    
 add_block('vehicleDynamics/Bicycle Model', [modelName '/Vehicle']);
    
 set_param([modelName '/Vehicle'], 'Mass', '1500'); % 设置车辆质量为1500kg
    
 set_param([modelName '/Vehicle'], 'Inertia', '[1000, 0, 0; 0, 1500, 0; 0, 0, 1000]'); % 设置惯性矩矩阵
    
 set_param([modelName '/Vehicle'], 'Wheelbase', '2.8'); % 设置轮距为2.8米

 % 添加纵向控制模块

    
 add_block('automatedDriving/Longitudinal Driver', [modelName '/Longitudinal_Driver']);
    
 set_param([modelName '/Longitudinal_Driver'], 'MaxAcceleration', '3'); % 设置最大加速度为3m/s^2
    
  
    
 % 添加横向控制模块
    
 add_block('automatedDriving/Lateral Driver', [modelName '/Lateral_Driver']);
    
 set_param([modelName '/Lateral_Driver'], 'MaxSteeringRate', '0.5'); % 设置最大转向角速率为0.5rad/s

 % 连接纵向控制模块到车辆底盘模块

    
 add_line(modelName, [modelName '/Longitudinal_Driver'], [modelName '/Vehicle'], 'autorouting', 'on');
    
  
    
 % 连接横向控制模块到车辆底盘模块
    
 add_line(modelName, [modelName '/Lateral_Driver'], [modelName '/Vehicle'], 'autorouting', 'on');

 % 添加路径规划模块

    
 add_block('automatedDriving/Path Planner', [modelName '/Path_Planner']);
    
 set_param([modelName '/Path_Planner'], 'StartPoint', '[0, 0]'); % 设置起始点为(0, 0)
    
 set_param([modelName '/Path_Planner'], 'GoalPoint', '[100, 100]'); % 设置目标点为(100, 100)
    
 set_param([modelName '/Path_Planner'], 'Obstacles', '[20, 20; 40, 40; 60, 60]'); % 设置障碍物位置

 function bezierPath = generateBezierPath(controlPoints, numPoints)

    
     % 使用贝塞尔曲线生成平滑路径
    
     t = linspace(0, 1, numPoints);
    
     bezierPath = zeros(numPoints, size(controlPoints, 2));
    
     
    
     for i = 1:numPoints
    
     u = t(i);
    
     bezierPath(i, :) = bernsteinPolynomial(controlPoints, u);
    
     end
    
 end
    
  
    
 function bp = bernsteinPolynomial(controlPoints, u)
    
     n = size(controlPoints, 1) - 1;
    
     bp = zeros(1, size(controlPoints, 2));
    
     
    
     for i = 0:n
    
     bp = bp + nchoosek(n, i) * (u^i) * ((1-u)^(n-i)) * controlPoints(i+1, :);
    
     end
    
 end

 function splinePath = generateSplinePath(x, y, numPoints)

    
     % 使用样条插值生成平滑路径
    
     xs = linspace(min(x), max(x), numPoints);
    
     splinePath = interp1(x, y, xs, 'spline');
    
     splinePath = [xs', splinePath];
    
 end

 % 添加低通滤波器模块

    
 add_block('signalProcessingToolbox/Lowpass Filter', [modelName '/Lowpass_Filter']);
    
 set_param([modelName '/Lowpass_Filter'], 'FilterOrder', '6'); % 设置滤波器阶数为6
    
 set_param([modelName '/Lowpass_Filter'], 'CutoffFrequency', '0.1'); % 设置截止频率为0.1Hz

 % 示例：在MATLAB Function模块中实现贝塞尔曲线

    
 add_block('simulink/User-Defined Functions/MATLAB Function', [modelName '/Bezier_Smoothing']);
    
 set_param([modelName '/Bezier_Smoothing'], 'FunctionName', 'generateBezierPath');
    
 set_param([modelName '/Bezier_Smoothing'], 'PortsDataType', 'Inherit: auto');

 % 连接路径规划模块到路径平滑处理模块

    
 add_line(modelName, [modelName '/Path_Planner'], [modelName '/Bezier_Smoothing'], 'autorouting', 'on');

 % 添加雷达传感器模块

    
 add_block('automatedDriving/Radar Sensor', [modelName '/Radar_Sensor']);
    
 set_param([modelName '/Radar_Sensor'], 'RangeResolution', '0.1'); % 设置距离分辨率为0.1米
    
  
    
 % 添加激光雷达传感器模块
    
 add_block('automatedDriving/Lidar Sensor', [modelName '/Lidar_Sensor']);
    
 set_param([modelName '/Lidar_Sensor'], 'AngularResolution', '0.05'); % 设置角度分辨率为0.05度
    
  
    
 % 添加摄像头传感器模块
    
 add_block('automatedDriving/Camera Sensor', [modelName '/Camera_Sensor']);
    
 set_param([modelName '/Camera_Sensor'], 'FieldOfView', '90'); % 设置视场角为90度

 % 添加传感器数据聚合模块

    
 add_block('automatedDriving/Sensor Data Aggregator', [modelName '/Sensor_Data_Aggregator']);
    
 set_param([modelName '/Sensor_Data_Aggregator'], 'SensorTypes', 'Radar, Lidar, Camera'); % 设置传感器类型为雷达、激光雷达、摄像头
    
 set_param([modelName '/Sensor_Data_Aggregator'], 'SamplingFrequency', '10'); % 设置采样频率为10Hz

 % 连接雷达传感器模块到传感器数据聚合模块

    
 add_line(modelName, [modelName '/Radar_Sensor'], [modelName '/Sensor_Data_Aggregator'], 'autorouting', 'on');
    
  
    
 % 连接激光雷达传感器模块到传感器数据聚合模块
    
 add_line(modelName, [modelName '/Lidar_Sensor'], [modelName '/Sensor_Data_Aggregator'], 'autorouting', 'on');
    
  
    
 % 连接摄像头传感器模块到传感器数据聚合模块
    
 add_line(modelName, [modelName '/Camera_Sensor'], [modelName '/Sensor_Data_Aggregator'], 'autorouting', 'on');

 % 设置仿真参数

    
 set_param(modelName, 'StopTime', '600'); % 模拟运行时间为600秒
    
 set_param(modelName, 'Solver', 'ode45');

 function smoothness = calculateSmoothness(path)

    
     % 示例：计算路径平滑度
    
     curvature = diff(diff(path(:, 2))) ./ (1 + diff(path(:, 1)).^2).^(3/2); % 计算曲率
    
     smoothness = mean(abs(curvature)); % 计算平均曲率变化率
    
 end
    
  
    
 % 获取路径数据
    
 path = getPathData();
    
  
    
 % 计算路径平滑度
    
 smoothness = calculateSmoothness(path);
    
 disp(['Path Smoothness: ', num2str(smoothness)]);

 function comfortLevel = calculateComfortLevel(acceleration)

    
     % 示例：计算行驶舒适性
    
     jerk = diff(acceleration); % 计算加速度变化率（Jerk）
    
     comfortLevel = mean(abs(jerk)); % 计算平均Jerk
    
 end
    
  
    
 % 获取加速度数据
    
 acceleration = getAccelerationData();
    
  
    
 % 计算行驶舒适性
    
 comfortLevel = calculateComfortLevel(acceleration);
    
 disp(['Comfort Level: ', num2str(comfortLevel)]);

 function trackingError = calculateTrackingError(actualPath, plannedPath)

    
     % 示例：计算路径跟踪误差
    
     distance = sqrt(sum((actualPath - plannedPath).^2, 2)); % 计算两点间距离
    
     trackingError = mean(distance); % 计算平均跟踪误差
    
 end
    
  
    
 % 获取实际路径和规划路径数据
    
 actualPath = getActualPathData();
    
 plannedPath = getPlannedPathData();
    
  
    
 % 计算路径跟踪误差
    
 trackingError = calculateTrackingError(actualPath, plannedPath);
    
 disp(['Tracking Error: ', num2str(trackingError), ' meters']);

 % 修改贝塞尔曲线控制点数量

    
 set_param([modelName '/Bezier_Smoothing'], 'NumControlPoints', '10'); % 更改为10个控制点
    
  
    
 % 修改样条插值节点密度
    
 set_param([modelName '/Spline_Smoothing'], 'NumNodes', '50'); % 更改为50个节点
    
  
    
 % 修改低通滤波器的截止频率
    
 set_param([modelName '/Lowpass_Filter'], 'CutoffFrequency', '0.05'); % 更改为0.05Hz

 % 使用多传感器数据融合算法

    
 add_block('automatedDriving/Multi-Sensor Fusion', [modelName '/Multi_Sensor_Fusion']);
    
 set_param([modelName '/Multi_Sensor_Fusion'], 'FusionAlgorithm', 'Kalman Filter'); % 设置融合算法为卡尔曼滤波
    
  
    
 % 连接传感器数据聚合模块到多传感器融合模块
    
 add_line(modelName, [modelName '/Sensor_Data_Aggregator'], [modelName '/Multi_Sensor_Fusion'], 'autorouting', 'on');
    
  
    
 % 连接多传感器融合模块到路径平滑处理模块
    
 add_line(modelName, [modelName '/Multi_Sensor_Fusion'], [modelName '/Bezier_Smoothing'], 'autorouting', 'on');

 % 使用基于图搜索的路径规划算法

    
 add_block('automatedDriving/Graph-Based Path Planner', [modelName '/Path_Planner']);
    
 set_param([modelName '/Path_Planner'], 'Algorithm', 'A*'); % 设置算法为A*
    
  
    
 % 连接传感器数据聚合模块到路径规划模块
    
 add_line(modelName, [modelName '/Sensor_Data_Aggregator'], [modelName '/Path_Planner'], 'autorouting', 'on');
    
  
    
 % 连接路径规划模块到路径平滑处理模块
    
 add_line(modelName, [modelName '/Path_Planner'], [modelName '/Bezier_Smoothing'], 'autorouting', 'on');

 % 创建测试用例

    
 testCase = sltest.testmanager.TestFile('PathSmoothing_TestCases');
    
 testCase.addTestSuite('PathSmoothing_TestSuite');
    
 testCase.addTestCase('Scenario_1', 'Standard_WLTC_Cycle');
    
  
    
 % 运行测试
    
 sltest.testmanager.run(testCase);