Get Started with Automated Driving Toolbox
Automated Driving Toolbox™ provides algorithms and tools for designing, simulating, and testing ADAS and automated driving features. These ADAS features include forward collision warning, autonomous emergency braking, adaptive cruise control, lane keeping assist, and parking valet.
The toolbox integrates scenarios, sensors, and vehicle dynamics for validating ADAS algorithms for model-in-the-loop (MIL), software-in-the-loop (SIL), and hardware-in-the-loop (HIL) simulations. You can programmatically author and simulate scenarios in Cuboid and RoadRunner environments. It offers visualization tools, such as bird's-eye-view plots, video, lidar, and map displays, and connects with Unreal Engine®.
The Test Suite for Euro NCAP® Protocols add-on supports standards-based testing by providing scenarios, metrics, and reports. The Scenario Builder add-on enables you to recreate real-world driving conditions from recorded sensor data, including camera, lidar, Global Positioning Systems (GPS), and Inertial Measurement Units (IMU).
Tutorials
- Create Driving Scenario Programmatically
Programmatically create ground truth driving scenarios for synthetic sensor data and tracking algorithms. - Create Driving Scenario Interactively and Generate Synthetic Sensor Data
Use the Driving Scenario Designer app to create a driving scenario and generate sensor detections and point cloud data from the scenario. - Simulate Simple Driving Scenario and Sensor in Unreal Engine Environment
Learn the basics of configuring and simulating scenes, vehicles, and sensors in a virtual environment rendered using the Unreal Engine from Epic Games®. - Overview of Simulating RoadRunner Scenarios with MATLAB and Simulink
This topic describes workflows to create actor behaviors in MATLAB® or Simulink®, associate the behaviors with graphical actors in RoadRunner Scenario, start the scenario simulation in RoadRunner, and log simulation results for further analysis. - Visual Perception Using Monocular Camera
Construct a monocular camera sensor simulation capable of lane boundary and vehicle detections. - Train a Deep Learning Vehicle Detector
Train a vision-based vehicle detector using deep learning. - Multiple Object Tracking Tutorial
Perform automatic detection and motion-based tracking of moving objects in a video by using a multi-object tracker. - Design Lidar SLAM Algorithm Using Unreal Engine Simulation Environment
Develop a simultaneous localization and mapping algorithm using synthetic lidar sensor data recorded from the Unreal Engine simulation environment. - Develop Visual SLAM Algorithm Using Unreal Engine Simulation
Develop a visual simultaneous localization and mapping (SLAM) algorithm using image data from the Unreal Engine simulation environment. - Get Started with Ground Truth Labeling
Interactively label multiple lidar and video signals simultaneously.
Driving Scenario Design
Detection and Tracking
Localization and Mapping
Ground Truth Labeling
About Automated Driving
- Coordinate Systems in Automated Driving Toolbox
Understand coordinate systems for automated driving.
Videos
Sensor Simulation and Virtual Scene Design with the Driving Scenario Designer
App, Part 1
Create virtual driving scenarios and import scenarios into the app.
Sensor Simulation and Virtual Scene Design with the Driving Scenario Designer
App, Part 2
Generate synthetic sensor detections and export them to MATLAB.
Design Lidar-Based SLAM Using Unreal Engine Simulation Environment
Build a Map from Lidar Data Using SLAM.
How to Simulate Automated Driving Systems: Adaptive Cruise Control
Simulate and test adaptive cruise control application for automated
driving.
