Automated Driving with MATLAB
This two-day course provides hands-on experience with developing and verifying automated driving perception algorithms. Examples and exercises demonstrate the use of appropriate MATLAB® and Automated Driving Toolbox™ functionality.
- Labeling of ground truth data
- Visualizing sensor data
- Detecting lanes and vehicles
- Processing lidar point clouds
- Tracking and sensor fusion
- Generating driving scenarios and modeling sensors
Day 1 of 2
Labeling of Ground Truth Data
Objective: Label ground truth data in a video or sequence of images interactively. Automate the labeling with detection and tracking algorithms.
- Overview of the Ground Truth Labeler app
- Label regions of interest (ROIs) and scenes
- Automate labeling
- View and export ground truth results
Visualizing Sensor Data
Objective: Visualize camera frames, radar, and lidar detections. Use appropriate coordinate systems to transform image coordinates to vehicle coordinates and vice versa.
- Create a bird’s-eye plot
- Plot sensor coverage areas
- Visualize detections and lanes
- Convert from vehicle to image coordinates
- Annotate video with detections and lane boundaries
Detecting Lanes and Vehicles
Objective: Segment and model parabolic lane boundaries. Use pretrained object detectors to detect vehicles.
- Perform a bird’s-eye view transform
- Detect lane features
- Compute lane model
- Validate lane detection with ground truth
- Detect vehicles with pretrained object detectors
Processing Lidar Point Clouds
Objective: Work with lidar data stored as 3-D point clouds. Import, visualize, and process point clouds by segmenting them into clusters. Register point clouds to align and build an accumulated point cloud map.
- Import and visualize point clouds
- Preprocess point clouds
- Segment objects from lidar sensor data
- Build a map from lidar sensor data
Day 2 of 2
Fusing Sensor Detections and Tracking
Objective: Create a multi-object tracker to fuse information from multiple sensors such as camera, radar and lidar.
- Track multiple objects
- Preprocess detections
- Utilize Kalman filters
- Manage multiple tracks
- Track with multi-object tracker
Tracking Extended Objects
Objective: Create a probability hypothesis density tracker to track extended objects and estimate their spatial extent.
- Define sensor configurations
- Track extended objects
- Estimate spatial extent
Generating Driving Scenarios and Modeling Sensors
Objective: Create driving scenarios and synthetic radar and camera sensor detections interactively to test automated driving perception algorithms.
- Overview of the Driving Scenario Designer app
- Create scenarios with roads, actors, and sensors
- Simulate and visualize scenarios
- Generate detections and export scenarios
- Test algorithms with scenarios