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) testing and desktop simulation of perception, sensor fusion, path planning, and control logic, you can generate and simulate driving scenarios. You can simulate camera, radar, and lidar sensor output in a photorealistic 3D environment and sensor detections of objects and lane boundaries in a 2.5D simulation environment.
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.
Programmatically create ground truth driving scenarios for synthetic sensor data and tracking algorithms.
Use the Driving Scenario Designer app to create a driving scenario and generate sensor detections and point cloud data from the scenario.
Interactively label multiple lidar and video signals simultaneously.
Construct a monocular camera sensor simulation capable of lane boundary and vehicle detections.
Understand coordinate systems for automated driving.
Driving Scenario Designer
Create virtual driving scenarios and generate synthetic sensor data for testing perception algorithms.