基于Simulink的智能驾驶辅助系统性能评估与优化

 % 创建新的Simulink模型

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

 % 添加车辆动力学模块

    
 add_block('vehicleDynamics/Longitudinal Driver', [modelName '/Longitudinal_Driver']);
    
 add_block('vehicleDynamics/Brake System', [modelName '/Brake_System']);
    
 add_block('vehicleDynamics/Rigid Body', [modelName '/Rigid_Body']);

 % 添加雷达传感器模块

    
 add_block('sensorFusion/Radar Detection Generator', [modelName '/Radar_Sensor']);
    
 set_param([modelName '/Radar_Sensor'], 'MaxRange', '150'); % 设置最大检测范围为150米
    
 set_param([modelName '/Radar_Sensor'], 'MinRange', '5'); % 设置最小检测范围为5米
    
 set_param([modelName '/Radar_Sensor'], 'UpdateRate', '10'); % 设置更新频率为10Hz

 % 添加PID控制器模块

    
 add_block('simulink/Continuous/PID Controller', [modelName '/PID_Controller']);
    
 set_param([modelName '/PID_Controller'], 'P', '1'); % 设置比例系数Kp为1
    
 set_param([modelName '/PID_Controller'], 'I', '0.1'); % 设置积分系数Ki为0.1
    
 set_param([modelName '/PID_Controller'], 'D', '0.01'); % 设置微分系数Kd为0.01

 % 计算相对距离

    
 relativeDistance = calculateRelativeDistance(radarData); // 自定义函数计算相对距离
    
  
    
 % 添加制动控制器
    
 add_block('simulink/User-Defined Functions/MATLAB Function', [modelName '/Braking_Controller']);
    
  
    
 % 编写MATLAB Function代码
    
 function brakingForce = fcn(relativeDistance)
    
     brakingForce = pid(relativeDistance); % 使用PID控制器计算制动力
    
 end

 % 连接各模块

    
 add_line(modelName, [modelName '/Radar_Sensor'], [modelName '/Braking_Controller'], 'autorouting', 'on');
    
 add_line(modelName, [modelName '/Braking_Controller'], [modelName '/Brake_System'], 'autorouting', 'on');

 % 设置仿真参数

    
 set_param(modelName, 'StopTime', '60'); % 模拟运行时间为60秒
    
 set_param(modelName, 'Solver', 'ode45');

 % 获取制动力数据

    
 brakingForce = getBrakingForce();
    
  
    
 % 计算上升时间和调节时间
    
 riseTime = calculateRiseTime(brakingForce); % 自定义函数计算上升时间
    
 settlingTime = calculateSettlingTime(brakingForce); % 自定义函数计算调节时间
    
  
    
 % 显示结果
    
 disp(['Rise Time: ', num2str(riseTime), ' seconds']);
    
 disp(['Settling Time: ', num2str(settlingTime), ' seconds']);

 % 计算超调量

    
 overshoot = calculateOvershoot(brakingForce); // 自定义函数计算超调量
    
  
    
 // 显示结果
    
 disp(['Overshoot: ', num2str(overshoot), '%']);

 % 获取车辆速度数据

    
 vehicleSpeed = getVehicleSpeed();
    
  
    
 % 检查是否发生碰撞
    
 if any(isCollision(vehicleSpeed, radarData)) % 自定义函数检查是否发生碰撞
    
     disp('Warning: Collision detected.');
    
 else
    
     disp('Vehicle successfully avoided collision.');
    
 end

 % 调整PID控制器参数

    
 set_param([modelName '/PID_Controller'], 'P', '1.2'); % 增加比例系数Kp
    
 set_param([modelName '/PID_Controller'], 'I', '0.15'); % 增加积分系数Ki
    
 set_param([modelName '/PID_Controller'], 'D', '0.02'); % 增加微分系数Kd
    
  
    
 % 重新运行仿真
    
 sim(modelName);

 % 添加前馈控制模块

    
 add_block('simulink/User-Defined Functions/MATLAB Function', [modelName '/Feedforward_Controller']);
    
  
    
 % 编写MATLAB Function代码
    
 function feedforwardSignal = fcn(predictedRelativeDistance)
    
     feedforwardSignal = calculateFeedforward(predictedRelativeDistance); % 自定义函数计算前馈控制信号
    
 end

 % 使用模型预测控制（MPC）

    
 add_block('simulink/Control System Toolbox/Model Predictive Control Toolbox/MPC Controller', [modelName '/MPC_Controller']);

 % 创建测试用例

    
 testCase = sltest.testmanager.TestFile('AEB_TestCases');
    
 testCase.addTestSuite('AEB_TestSuite');
    
 testCase.addTestCase('Scenario_1', 'Initial_Speed_50kmh');
    
  
    
 % 运行测试
    
 sltest.testmanager.run(testCase);