Automated Driving

Access, Visualize, and Label Data

You can access live and recorded driving data using MATLAB interfaces for CAN and ROS. Using built-in tools, you can also visualize and label imported data. For example, the ground truth labeler app provides an interface to visualize and label multiple signals interactively, or you can automatically label these signals and export the labeled data to your workspace.

To access and visualize geographic map data, you can use HERE HD Live Maps and OpenStreetMap.

Documentation

• Forward Collision Warning with CAN FD and TCP/IP
• Work with Specialized ROS Messages
• Visualize Sensor Coverage, Detections, and Tracks
• Display Data on OpenStreetMap Basemap

Simulate Driving Scenarios

You can use cuboid and Unreal Engine simulation environments with MATLAB to develop and test algorithms in virtual scenarios.

The cuboid environment represents actors as simple graphics and uses probabilistic sensor models. You can use this environment for controls, sensor fusion, and motion planning.

Using the Unreal Engine environment, you can develop algorithms for perception and the cuboid-environment use-cases. RoadRunner lets you design scenes to use with simulators, including CARLA, Vires VTD, and NVIDIA Drive Sim, as well as MATLAB and Simulink. RoadRunner also supports export to industry standard file formats like FBX and OpenDRIVE.

Documentation

• Driving Scenario Designer
• Scenario Generation from Recorded Vehicle Data
• 3D Simulation for Automated Driving
• Visualize 3D Simulation Sensor Coverages and Detections
• Getting Started with RoadRunner

Design Planning and Control Algorithms

Using MATLAB and Simulink, you can develop path planning and control algorithms. You can design vehicle control systems using lateral and longitudinal controllers that enable autonomous vehicles to follow a planned trajectory.

You can also test your algorithms synthetically using sensor models and vehicle dynamics models, along with 2D and 3D simulation environments.

Documentation

• Traffic Light Negotiation
• Parallel Parking Using RRT Planner and MPC Tracking Controller
• Train DQN Agent for Lane Keeping Assist
• Autonomous Emergency Braking with Sensor Fusion
• Highway Lane Change
• Automated Parking Valet

Design Perception Algorithms

You can develop perception algorithms using data from camera, lidar, and radar. Perception algorithms include detection, tracking, and localization, which you can use for applications like automatic braking, steering, map building, and odometry. 

You can implement these algorithms as part of ADAS applications like emergency braking and steering.

Using MATLAB, you can develop algorithms for sensor fusion, simultaneous localization and mapping (SLAM), map building, and odometry.

Documentation

• Object Detection Using SSD Deep Learning
• Track-Level Fusion of Radar and Lidar Data
• Introduction to Tracking Metrics
• Estimate Orientation Through Inertial Sensor Fusion
• Build a Map from Lidar Data Using SLAM

Deploy Algorithms

You can deploy perception, planning, and control algorithms to hardware using code generation workflows. Supported code generation languages include C, C++, CUDA, Verilog, and VHDL^®.

You can also deploy algorithms to service-oriented architectures like ROS and AUTOSAR.

Using auto-generated code, you can connect sensors with other ECU components. Several deployment targets are supported, including hardware from NVIDIA, Intel, ARM, and more.

Documentation

• Code Generation for Object Detection by Using Single Shot Multibox Detector
• Code Generation for Semantic Segmentation Application on ARM Neon
• Create and Configure AUTOSAR Adaptive Software Component
• Automated Parking Valet with ROS in MATLAB

Integrate and Test

You can integrate and test your perception, planning, and control systems. Using Requirements Toolbox, you can capture and manage your requirements. You can also use Simulink Test to run and automate test cases in parallel.

Documentation

• Automate Testing for Highway Lane Following
• Continuous Integration for Verification of Simulink Models