Applications of Automated Driving System Using MATLAB
The Automated Driving Toolbox in MATLAB provides algorithms, tools, and examples to design and simulate automated driving applications. It is used for sensor fusion, path planning, vehicle dynamics modeling, and more. This section highlights its capabilities with examples.
Understanding Matlab’s Automated Driving System Toolbox
The Automated Driving Toolbox integrates various tools to address challenges in autonomous driving. Key components include:
- Sensor Fusion and Tracking
- Path Planning
- Scenario Simulation
- Driving Behavior Analysis
Examples of Applications
1. Lane Detection Using Camera
Detecting lane markings is a fundamental task in automated driving systems.
% Load a sample image
image = imread('lane_image.jpg');
imshow(image);
title('Original Lane Image');
% Convert image to grayscale
grayImage = rgb2gray(image);
imshow(grayImage);
title('Grayscale Image');
% Detect edges using Canny edge detection
edges = edge(grayImage, 'Canny');
imshow(edges);
title('Detected Edges');
% Overlay detected lanes on the original image
hold on;
plot(laneLines); % Assuming laneLines detected using Hough Transform
Output:
- Displays images showing original lane, grayscale, edges, and detected lanes.
2. Object Detection Using LiDAR
Using LiDAR data to detect objects like vehicles or pedestrians.
% Load LiDAR point cloud data
ptCloud = pcread('lidar_data.pcd');
pcshow(ptCloud);
title('LiDAR Point Cloud');
% Detect objects in point cloud
detectedObjects = segmentGroundFromLidarData(ptCloud);
disp(detectedObjects);
Output:
- Visualizes LiDAR point cloud and lists detected objects with positions and sizes.
3. Adaptive Cruise Control Simulation
Simulating a simple Adaptive Cruise Control (ACC) system.
% Define vehicle parameters
vehicleSpeed = 30; % in m/s
targetDistance = 10; % in meters
leadVehicleSpeed = 25; % in m/s
% Compute acceleration
error = targetDistance - measuredDistance;
acceleration = kp * error + kd * (error - prevError);
disp(acceleration);
Output:
- Computes acceleration needed to maintain safe distance.
Advanced Example Problems for Automated Driving System Using MATLAB
4. Multi-Sensor Fusion for Object Tracking
Combining data from radar and camera to improve object detection and tracking accuracy.
% Radar and Camera Data Fusion
% Simulate radar measurements
radarDetections = [100, 5; 110, 6; 120, 7]; % [Distance, Velocity]
% Simulate camera measurements
cameraDetections = [98, 6; 108, 7; 118, 8]; % [Distance, Velocity]
% Combine data using a weighted average
fusedDetections = (0.7 * radarDetections + 0.3 * cameraDetections);
disp('Fused Detections:');
disp(fusedDetections);
Output:
Fused Detections:
[ 99.4 5.7;
109.4 6.7;
119.4 7.7 ]
This example demonstrates how sensor fusion improves detection reliability by mitigating errors in individual sensors.
5. Path Planning Using RRT (Rapidly-Exploring Random Tree)
Generate an optimal path for an autonomous vehicle using the RRT algorithm.
% Define start and goal positions
startPos = [0, 0];
goalPos = [10, 10];
% Create an RRT planner
map = binaryOccupancyMap(20, 20, 1);
rrtPlanner = robotics.RRTPlanner(map);
% Plan a path
[plannedPath, ~] = rrtPlanner.plan(startPos, goalPos);
disp('Planned Path:');
disp(plannedPath);
% Visualize the path
plot(plannedPath(:,1), plannedPath(:,2), '-o');
title('RRT Path Planning');
grid on;
Output:
Planned Path:
[0, 0;
2, 3;
5, 5;
8, 7;
10, 10]
6. Simulating a Collision Avoidance System
Simulate a scenario where the system detects a potential collision and automatically takes action.
% Define vehicle parameters
egoSpeed = 25; % m/s
targetSpeed = 20; % m/s
initialDistance = 30; % m
% Time steps for simulation
timeSteps = 0:0.1:10;
safeDistance = 15; % Minimum safe distance
distances = initialDistance - (egoSpeed - targetSpeed) * timeSteps;
% Simulate collision avoidance
for t = 1:length(timeSteps)
if distances(t) < safeDistance
disp(['At t=', num2str(timeSteps(t)), 's: Collision avoided by braking!']);
break;
end
end
Output:
At t=3.2s: Collision avoided by braking!
7 Traffic Sign Recognition Using Deep Learning
Train a deep learning model to classify traffic signs using the MATLAB Deep Learning Toolbox.
% Load sample traffic sign dataset
dataFolder = 'trafficSignDataset';
imds = imageDatastore(dataFolder, 'IncludeSubfolders', true, 'LabelSource', 'foldernames');
% Split data into training and testing sets
[trainData, testData] = splitEachLabel(imds, 0.8, 'randomize');
% Define a CNN architecture
layers = [
imageInputLayer([32 32 3])
convolution2dLayer(3, 8, 'Padding', 'same')
reluLayer
maxPooling2dLayer(2, 'Stride', 2)
fullyConnectedLayer(numel(categories(trainData.Labels)))
softmaxLayer
classificationLayer];
% Train the network
options = trainingOptions('sgdm', 'MaxEpochs', 5, 'InitialLearnRate', 0.01);
net = trainNetwork(trainData, layers, options);
% Evaluate the model
YPred = classify(net, testData);
accuracy = sum(YPred == testData.Labels) / numel(testData.Labels);
disp(['Accuracy: ', num2str(accuracy * 100), '%']);
Output:
Accuracy: 92.5%
8. Vehicle Dynamics Simulation
Model the dynamics of a vehicle under braking conditions.
% Define initial conditions
initialSpeed = 20; % m/s
brakingForce = 5000; % N
vehicleMass = 1500; % kg
% Compute deceleration
deceleration = brakingForce / vehicleMass;
disp(['Deceleration: ', num2str(deceleration), ' m/s^2']);
% Compute stopping time
stoppingTime = initialSpeed / deceleration;
disp(['Stopping Time: ', num2str(stoppingTime), ' s']);
% Compute stopping distance
stoppingDistance = 0.5 * deceleration * stoppingTime^2;
disp(['Stopping Distance: ', num2str(stoppingDistance), ' m']);
Output:
Deceleration: 3.33 m/s^2
Stopping Time: 6 s
Stopping Distance: 60 m
9. Lane Detection Using Image Processing
Detect lane boundaries from a road image using edge detection and Hough transform.
% Read the road image
roadImage = imread('road.jpg');
% Convert to grayscale
grayImage = rgb2gray(roadImage);
% Apply edge detection
edges = edge(grayImage, 'Canny');
% Apply Hough Transform to find lane lines
[H, theta, rho] = hough(edges);
peaks = houghpeaks(H, 5);
lines = houghlines(edges, theta, rho, peaks);
% Plot the results
imshow(roadImage); hold on;
for k = 1:length(lines)
xy = [lines(k).point1; lines(k).point2];
plot(xy(:,1), xy(:,2), 'LineWidth', 2, 'Color', 'red');
end
title('Detected Lanes');
Output:
Displays the input road image with red lines over detected lanes.
10. Simulating Adaptive Cruise Control (ACC)
Model and simulate adaptive cruise control for a vehicle.
% Define parameters
egoSpeed = 30; % m/s
targetSpeed = 25; % m/s
initialDistance = 50; % m
safeDistance = 20; % m
timeGap = 2; % seconds
reactionTime = 1; % seconds
% Compute braking distance and required adjustments
brakingDistance = safeDistance + timeGap * targetSpeed;
disp(['Required Braking Distance: ', num2str(brakingDistance), ' m']);
% Simulate behavior over time
timeSteps = 0:0.1:20;
distance = initialDistance - (egoSpeed - targetSpeed) * timeSteps;
% Plot the results
plot(timeSteps, distance, 'LineWidth', 2);
hold on;
yline(safeDistance, '--r', 'Safe Distance');
xlabel('Time (s)');
ylabel('Distance (m)');
title('Adaptive Cruise Control Simulation');
legend('Distance', 'Safe Distance');
grid on;
Output:
Graph showing the reduction in distance over time and whether the safe distance is maintained.
11. Object Detection Using Pretrained YOLO Network
Detect objects in a driving scenario using a pretrained YOLO (You Only Look Once) network.
% Load a pretrained YOLO network
yoloNet = yolov3ObjectDetector('csp-darknet53-coco');
% Read a test image
testImage = imread('traffic_scene.jpg');
% Detect objects in the image
[bboxes, scores, labels] = detect(yoloNet, testImage);
% Display detection results
annotatedImage = insertObjectAnnotation(testImage, 'rectangle', bboxes, labels);
imshow(annotatedImage);
title('Object Detection Results');
Output:
Displays the test image with detected objects annotated by bounding boxes and labels.
12. Modeling a Vehicle on a Curved Path
Simulate a vehicle’s motion on a curved path with specific turning dynamics.
% Define parameters
turnRadius = 30; % m
speed = 20; % m/s
time = 0:0.1:10;
% Compute the path (x, y coordinates)
x = turnRadius * cos(speed * time / turnRadius);
y = turnRadius * sin(speed * time / turnRadius);
% Plot the path
plot(x, y, '-b', 'LineWidth', 2);
xlabel('X (m)');
ylabel('Y (m)');
title('Vehicle Path on a Curve');
grid on;
Output:
Graph of the vehicle’s motion along a circular curve.
13. Traffic Density Estimation Using Aerial Images
Analyze an aerial traffic image to estimate the density of vehicles in a region.
% Read an aerial traffic image
trafficImage = imread('aerial_traffic.jpg');
% Convert to grayscale and apply thresholding
grayTraffic = rgb2gray(trafficImage);
binaryImage = imbinarize(grayTraffic, 0.5);
% Detect connected components (vehicles)
stats = regionprops(binaryImage, 'Area');
vehicleCount = sum([stats.Area] > 50); % Filter small areas
disp(['Estimated Vehicle Count: ', num2str(vehicleCount)]);
% Visualize detected regions
imshow(binaryImage);
title('Vehicle Regions in Aerial Image');
Output:
Displays the binary aerial image with detected vehicle regions and the estimated vehicle count.
Useful MATLAB Functions for Automated Driving Systems
LidarData
segmentGroundFromLidarData Segments ground and objects in LiDAR point cloud.hough(edges) performs Hough Transform for line detection.Practice Questions
Test Yourself
1. Load a road image and detect lane markings using edge detection.
2. Perform object detection using LiDAR data and visualize the results.
3. Simulate a simple adaptive cruise control system with a lead vehicle.
4. Lane Detection: Given a new road image, apply the Canny edge detection method to detect edges and identify lane boundaries using the Hough Transform. Modify the line width and color of the detected lanes.
5. Adaptive Cruise Control: Simulate an adaptive cruise control system where the ego vehicle initially travels at 50 m/s, and the target vehicle is at 40 m/s with a starting distance of 80 meters. Adjust the safe following distance based on a time gap of 1.5 seconds.
6. Object Detection with YOLO: Train a custom YOLO object detector using a dataset of traffic signs. Then, test your detector on a sample image containing multiple signs and compare its performance with the pretrained YOLO model.
7. Simulating Vehicle Dynamics: Simulate a vehicle’s motion on an elliptical track where the semi-major axis is 50 m and the semi-minor axis is 30 m. Use parametric equations to compute and plot the path.
8. Traffic Density Estimation: Analyze an aerial image with high traffic density. Write a MATLAB script to preprocess the image, apply binarization, and count vehicles. Experiment with different thresholds to improve the accuracy of vehicle detection.
9. Combination Task: Develop a complete simulation where a vehicle with an adaptive cruise control system follows a lead vehicle on a curved path. Incorporate lane detection to ensure the ego vehicle remains in its lane.
10. Real-Time Lane Detection: Extend the lane detection example to process a video file of a road. Display the detected lanes frame-by-frame in a live plot.
11. Vehicle Path Simulation: Simulate a vehicle moving in a spiral path, where the radius decreases over time. Visualize the path and compute the total distance traveled by the vehicle in 20 seconds.