Automated Driving Toolbox

MAJOR UPDATE

 

Automated Driving Toolbox

Design, simulate, and test ADAS and autonomous driving systems

 

Automated Driving Toolbox™ provides algorithms and tools for designing, simulating, and testing ADAS and autonomous driving systems. You can design and test vision and lidar perception systems, as well as sensor fusion, path planning, and vehicle controllers. Visualization tools include a bird’s-eye-view plot and scope for sensor coverage, detections and tracks, and displays for video, lidar, and maps. The toolbox lets you import and work with HERE HD Live Map data and OpenDRIVE® road networks.

Using the Ground Truth Labeler app, you can automate the labeling of ground truth to train and evaluate perception algorithms. For hardware-in-the-loop (HIL) and desktop simulation of sensor fusion, path planning, and control logic, you can generate and simulate driving scenarios and radar and camera sensor outputs.  

Automated Driving Toolbox provides reference application examples for common ADAS and automated driving features, including FCW, AEB, ACC, LKA, and parking valet. The toolbox supports C/C++ code generation for rapid prototyping and HIL testing, with support for sensor fusion, tracking, path planning, and vehicle controller algorithms.

Reference Applications

Use reference applications as a basis for developing automated driving functionality. Automated Driving Toolbox includes reference applications for forward collision warning (FCW), lane keeping assist (LKA), and automated parking valet.

Detecting vehicles and lanes in the reference example about designing a visual perception system. 

Scenario Generation and Sensor Models

Test automated driving algorithms using authored scenarios and synthetic detections from radar and camera sensor models.

Author Driving Scenarios

Define road networks, actors, and sensors using the Driving Scenario Designer app. Import prebuilt Euro NCAP tests and OpenDRIVE road networks.

Modeling output of vision system including vehicle and lane detections. 

Test Algorithms Using Synthetic Data

Test and validate perception, sensor fusion, and control algorithms in open- and closed-loop settings using simulated data from driving scenarios and sensor models.

Test a lane keeping assist (LKA) system using simulated data.

Ground Truth Labeling

Automate labeling of ground truth data and compare output from an algorithm under test with ground truth data.

Ground Truth Labeling

Interactive and automated ground truth labeling for object detection, semantic segmentation, and scene classification.

Testing Perception Algorithms

Evaluate the performance of perception algorithms by comparing ground truth data against algorithm outputs.

Evaluate lane detection output against ground truth.

Perception with Computer Vision and Lidar

Develop and test vision and lidar processing algorithms for automated driving.

Vision System Design

Develop computer vision algorithms for vehicle and pedestrian detection, lane detection, and classification.

Monocular camera sensor simulation output.

Lidar Processing

Use lidar data to detect obstacles and segment ground planes.

Obstacle detection in lidar point clouds.

Sensor Fusion and Tracking

Perform multisensor fusion using multi-object tracking framework with Kalman filters.

Mapping

Access and visualize high-definition map data from the HERE HD Live Map service, and display vehicle and object locations on streaming map viewers.

Path Planning

Plan driving paths by using vehicle costmaps and motion-planning algorithms.

Vehicle Controllers

Use lateral and longitudinal controllers to follow a planned trajectory.

Latest Features

HERE HD Live Map Reader

Read and visualize data from high-definition maps designed for automated driving applications

Scenario Reader

Read driving scenarios into Simulink to test vehicle controllers and sensor fusion algorithms

Bird's-Eye Scope for Simulink

Analyze sensor coverages, detections, and tracks in your model

Prebuilt Driving Scenarios

Test driving algorithms using Euro NCAP and other prebuilt scenarios

Path Planning

Plan driving paths using an RRT* path planner and costmap

See release notes for details on any of these features and corresponding functions.

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