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.
Ground-truth labeling workflow app to automate labeling, and tools to compare simulation output with ground truth
Sensor fusion and tracking algorithms, including Kalman filters, multiobject tracking framework, detection-track assignment, and motion models
Driving scenario generation, including road, actor, and vehicle definition and scenario visualizations
Sensor simulation for camera and radar, with object lists as output
Computer vision algorithms, including lane detection and classification, vehicle detection, and image-vehicle coordinate transforms
Visualizations, including bird's-eye-view plots of sensor coverage, detections, and tracks, and video overlays for lane markers and vehicle detection
C-code generation for sensor fusion and tracking algorithms (with MATLAB® Coder™)
Model and simulate automotive powertrain systems
Powertrain Blockset™ provides fully assembled reference application models of automotive powertrains, including gasoline, diesel, hybrid, and electric systems. It includes a component library for simulating engine subsystems, transmission assemblies, traction motors, battery packs, and controller models. Powertrain Blockset also includes a dynamometer model for virtual testing. MDF file support provides a standards-based interface to calibration tools for data import.
Powertrain Blockset provides a standard model architecture that can be reused throughout the development process. You can use it for design tradeoff analysis and component sizing, control parameter optimization, and hardware-in-the-loop testing. You can customize models by parameterizing components in a reference application with your own data or by replacing a subsystem with your own model.
Fully assembled models for gasoline, diesel, hybrid, and electric powertrains
Libraries of engine, transmission, traction motor, and battery components
Basic controllers for powertrain subsystems
Standard drive cycle data, including FTP75, NEDC, and JC08
Engine dynamometer model for virtual calibration and testing
MDF file support for calibration data import