Simulating autonomous vehicle systems in MATLAB involves modeling vehicle dynamics, sensors, control algorithms, and environments

Simulating autonomous vehicle systems in MATLAB involves modeling vehicle dynamics, sensors, control algorithms, and environments. MATLAB’s Automated Driving Toolbox, Simulink, and Reinforcement Learning Toolbox are commonly used. Below is a step-by-step guide with code examples:

Step 1: Model the Vehicle and Environment

1.1 Create a Driving Scenario

Use the drivingScenario class to define roads, vehicles, and trajectories.

% Create a driving scenario
scenario = drivingScenario;
road(scenario, [0 0; 100 0]); % Straight road
egoVehicle = vehicle(scenario, 'ClassID', 1); % Add ego vehicle
trajectory(egoVehicle, [0 0 0; 50 0 0], 10); % Define path (50m in 10s)

1.2 Visualize the Scenario

% Plot the scenario
plot(scenario);
title('Autonomous Vehicle Simulation Scenario');
chasePlot(egoVehicle); % Chase camera view

Step 2: Simulate Sensors

2.1 Add Lidar/Camera/Radar Sensors

% Add a lidar sensor to the ego vehicle
lidarSensor = monostaticLidarSensor(1, 'MountingLocation', [0 0 2]);
lidarSensor.AzimuthResolution = 0.5; % Degrees
lidarSensor.ElevationResolution = 0.5;

% Add a front-facing camera
camera = visionDetectionGenerator('SensorIndex', 1, 'Height', 1.5);

2.2 Generate Sensor Data

while advance(scenario)
    % Simulate lidar point cloud
    [ptCloud, validTime] = lidarSensor(pose(egoVehicle), scenario);
    
    % Simulate camera detections
    [image, cameraDetections] = camera(pose(egoVehicle), scenario);
    
    % Visualize sensor data
    pcshow(ptCloud); % Lidar point cloud
    imshow(image); % Camera image
end

Step 3: Implement Control Algorithms

3.1 PID Controller for Path Tracking

Use Simulink or MATLAB’s Control System Toolbox to design a PID controller.

% Example PID controller for lateral control (steering)
Kp = 0.1; Ki = 0.01; Kd = 0.05;
pidController = pid(Kp, Ki, Kd);

% Simulate control input
steeringAngle = pidController(error); % error = desired vs. actual path

3.2 Reinforcement Learning for Decision-Making

Use the Reinforcement Learning Toolbox to train an agent (e.g., DQN or PPO).

% Define the environment
env = rlPredefinedEnv('AutonomousDriving-v0');

% Create a DQN agent
agent = rlDQNAgent(env);

% Train the agent
trainOpts = rlTrainingOptions('MaxEpisodes', 1000);
trainingStats = train(agent, env, trainOpts);

Step 4: Simulate Vehicle Dynamics

4.1 Use Simulink for Dynamics Modeling

Leverage Simulink’s Vehicle Dynamics Blockset to model vehicle physics:

Bicycle Model: Simulate lateral/longitudinal dynamics.
3D Vehicle Model: Include suspension and tire forces.

Simulink Vehicle Dynamics Model

Step 5: Integrate Perception, Planning, and Control

Combine sensor data, path planning (A* or RRT*), and control in a closed-loop simulation.

% Example workflow:
% 1. Perception: Detect obstacles using lidar/camera.
detectedObstacles = processLidarData(ptCloud);

% 2. Path Planning: Generate a collision-free path.
plannedPath = pathPlanner(detectedObstacles, egoPose);

% 3. Control: Adjust steering/throttle to follow the path.
steerCmd = pidController(plannedPath);

Step 6: Analyze Results

6.1 Visualize Trajectories and Sensor Data

% Plot ego vehicle trajectory
plot(egoVehicle.Trajectory.X, egoVehicle.Trajectory.Y, 'r--');
hold on;
plot(plannedPath.X, plannedPath.Y, 'b-');
legend('Actual Path', 'Planned Path');

6.2 Evaluate Safety Metrics

Calculate collision rates, time-to-collision (TTC), or tracking error.

collisionStatus = checkCollision(egoVehicle, scenario);
fprintf('Collision Detected: %d\n', collisionStatus);

Key Toolboxes

Automated Driving Toolbox: Sensor simulation, scenario generation.
Robotics System Toolbox: Path planning (A*, RRT*).
Computer Vision Toolbox: Object detection/tracking.
Reinforcement Learning Toolbox: AI-based decision-making.

Example Outputs

Sensor Data:
Control Performance:

Notes

Use Simulink 3D Animation for realistic visualization.
Leverage MATLAB’s ADAS Algorithm Examples for prebuilt models.
Test edge cases (e.g., sudden pedestrian crossings, sensor failures).

This workflow enables end-to-end simulation of autonomous vehicle systems in MATLAB. For advanced use cases, combine with ROS/ROS2 for hardware-in-the-loop (HIL) testing