This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English version of the page.

Note: This page has been translated by MathWorks. Click here to see
To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

Automated Driving System Toolbox

Design, simulate, and test ADAS and autonomous driving systems

Automated Driving System Toolbox™ provides algorithms and tools for designing and testing ADAS and autonomous driving systems. You can automate ground-truth labeling, generate synthetic sensor data for driving scenarios, perform multisensor fusion, and design and simulate vision systems.

For open-loop testing, the system toolbox provides a customizable workflow app and evaluation tools that let you automate labeling of ground truth and test your algorithms against ground truth. For HIL and desktop simulation of sensor fusion and control logic, you can generate driving scenarios and simulate object lists from radar and camera sensors.

Automated Driving System Toolbox supports multisensor fusion development with Kalman filters, assignment algorithms, motion models, and a multiobject tracking framework. Algorithms for vision system design include lane marker detection, vehicle detection with machine learning, including deep learning, and image-to-vehicle coordinate transforms.

Getting Started

Learn the basics of Automated Driving System Toolbox

Sensor Configuration and Coordinate System Transforms

Camera sensor configuration, image-to-vehicle coordinate system transform, bird’s-eye-view image transform

Ground Truth Labeling

Interactive ground truth labeling for object detection, semantic segmentation, and image classification

Perception with Computer Vision and Lidar

Object and lane boundary detections using machine learning and deep learning, lidar processing

Tracking and Sensor Fusion

Object tracking and multisensor fusion, bird’s-eye plot of detections and object tracks

Driving Scenario Generation and Sensor Models

Test driving algorithms using generated scenarios and synthetic detections from radar and camera sensor models

Planning, Navigation, and Control

Path planning, costmaps, geographic map display, vehicle controllers