MATLAB & Simulink

Automotive Applications

Automated Driving with MATLAB

Course Highlights

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.


Topics include:

  • 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

Who Should Attend

Partners 

Upcoming Program

Techsource Systems is
Mathworks Sole and Authorised Distributor and Training Partner

Prerequisite

Course Benefits

Course Outlline

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/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 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 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