Design, Simulate, and Test Automated Driving Systems with MATLAB and Simulink

Automotive engineers use MATLAB® and Simulink® to design automated driving system functionality.

Access these videos, articles, and other resources to learn how MATLAB and Simulink can help you answer these questions:

  • How can we analyze and synthesize data?
  • How can we design and deploy algorithms?
  • How can we integrate and test?

Analyze and Synthesize Scenarios

Visualize Sensor Coverage, Detections, and Tracks Using a Bird's Eye Plot
Cuboid Driving Scenario Simulation Using the Driving Scenario Designer App (5:09)
3D Simulation for Automated Driving Using Unreal Engine
Scenario Generation from Recorded Vehicle Data
Label Videos and Images Using Ground Truth Labeler (6:04)

Perception

Perception with Computer Vision and Lidar
Radar Signal Simulation and Processing for Automated Driving
Generate and Deploy CUDA Code for Object Detection on NVIDIA Jetson (2:25)
Sensor Fusion and Tracking Examples
Understanding Sensor Fusion and Tracking, Part 1: What Is Sensor Fusion? (12:34)

Planning and Control

Design Simulate and Deploy Path Planning Algorithms Using Navigation Toolbox (1:50)
Automated Valet Parking Example
Understanding Model Predictive Control, Part 1: Why Use MPC? (4:50)
Adaptive Cruise Control with Sensor Fusion Using Model Predictive Control (6:00)

Integration and Testing

Integration with Robotics Operating System (ROS)
Simulating Longitudinal and Lateral Vehicle Dynamics (9:13)
Automate Testing for Highway Lane Following
Master Class: Design and Test Decision Making, Path Planning, and Control Modules in Traffic Scenarios (44:32)

Customer Stories and Examples

Ricardo: Simulating Passenger Comfort and Motion Sickness in Autonomous Vehicles (23:54)
Caterpillar: Big Data, Data Analytics, and Machine/Deep Learning Infrastructure (18:47)
Autoliv: Automated LiDAR Point-Cloud Annotation for Sensor Verification (14:05)
Tata Motors European Technical Centre: Accelerating Development of Autonomous Vehicle Control Algorithms with Model-Based Design
Voyage Auto: Developing Longitudinal Controls for a Self-Driving Taxi
Hitachi: Developing Model Predictive Control for Adaptive Cruise Control
Technical University Munich: A Real-Time Simulation Environment for Autonomous Vehicles in Highly Dynamic Driving Scenarios (23:47)
Examples: Automated Driving Applications Using Model Predictive Control