MATLAB & Simulink for Cyber Physical Systems

MATLAB & Simulink for Cyber Physical Systems Sumit Tandon Senior Customer Success Manager, MathWorks 2017 The MathWorks, Inc. 1

Agenda Intro to MATLAB and Simulink What, where, who, how, quick demos Intro to Automated Driving System Toolbox What, where, why, quick demos Resources Q & A 2

What is MATLAB? High-level language Interactive development environment Used for: Numerical computation Data analysis and visualization Algorithm development and programming Application development and deployment 3

Data Analysis Workflow Access Files Explore & Discover Data Analysis & Modeling Share Reporting and Documentation Software Algorithm Development Outputs for Design Code & Applications Hardware Application Development Deployment Automate 4

Demo: Fuel Economy Analysis Goal: Study the relationships between fuel economy, horsepower, and type of vehicle Approach: Import data from spreadsheet Interactively visualize and explore trends Create a model Document results 5

Simulink The leading environment for modeling, simulating, and implementing dynamic and embedded systems Block-diagram environment Model, simulate and analyze multidomain systems Accurately design, implement, and test: Control systems Signal processing systems Communications systems And other dynamic systems Platform for Model-Based Design 6

System Design Process 7

Demo: Wing Landing Gear System 8

What people do in industry: Mars Rovers NASA Jet Propulsion Lab MATLAB is used for: Entry, Descent, and Landing System Design, Navigation, and Data Analysis Artist's rendition of Mars rover. Graphics courtesy of NASA/JPL/Cornell 9

What people do in industry: Motion-Stereo Parking BMW MATLAB is use for: Motion-Stereo Systems, Object Detection, Computer Vision, Real-Time Control Vehicle equipped with side-view camera. As the vehicle moves, the side-view camera acquires images that are used to measure depth. 10

What people do in industry: Prosthetic Arm Applied Physics Lab MATLAB is use for: Simulation in Virtual Environment, Machine Learning, Real-Time Controller Design, and Clinical Application 11

Automated Driving System Toolbox 12

Some common questions from automated driving engineers 1011010101010100101001 0101010100100001010101 0010101001010100101010 0101010100101010101001 0101010010100010101010 0010100010100101010101 0100101010101010010101 0100101010010110000101 0100101010101010010010 1010011101010100101010 vehicle 1 F 2 How can I visualize vehicle data? How can I detect objects in images? How can I fuse multiple detections? 13

Some common questions from automated driving engineers 1011010101010100101001 0101010100100001010101 0010101001010100101010 0101010100101010101001 0101010010100010101010 0010100010100101010101 0100101010101010010101 0100101010010110000101 0100101010101010010010 1010011101010100101010 vehicle 1 F 2 How can I visualize vehicle data? How can I detect objects in images? How can I fuse multiple detections? 14

Visualize sensor data 15

Visualize differences in sensor detections 16

Learn more about visualizing vehicle data by exploring examples in the Automated Driving System Toolbox Plot object detectors in vehicle coordinates Vision & radar detector Lane detectors Detector coverage areas Transform between vehicle and image coordinates Plot lidar point cloud 17

Some common questions from automated driving engineers 1011010101010100101001 0101010100100001010101 0010101001010100101010 0101010100101010101001 0101010010100010101010 0010100010100101010101 0100101010101010010101 0100101010010110000101 0100101010101010010010 1010011101010100101010 vehicle 1 F 2 How can I visualize vehicle data? How can I detect objects in images? How can I fuse multiple detections? 18

How can I detect objects in images? Object detector Classification Left Classification Bottom Left Width Bottom Height Width Height 19

Train object detectors based on ground truth Images Ground Truth Train detector Object detector Classification Left Classification Bottom Left Width Bottom Height Width Height 20

Train object detectors based on ground truth Images Ground Truth Train detector Object detector Design object detectors with the Computer Vision System Toolbox Machine Learning Deep Learning Aggregate Channel Feature Cascade R-CNN (Regions with Convolutional Neural Networks) Fast R-CNN Faster R-CNN trainacfobjectdetector traincascadeobjectdetector trainrcnnobjectdetector trainfastrcnnobjectdetector trainfasterrcnnobjectdetector 21

Specify ground truth to train detector Images How can I create ground truth? Ground Truth Train detector Object detector 22

Specify ground truth to train detector Video Ground Truth Labeler App Ground Truth Train detector Object detector 23

Specify ground truth to train detectors Video Set 1 Ground Truth Labeler App Ground Truth Train detector Object detector Specify ground truth to evaluate detectors Video Set 2 Object detector Detections Evaluate detections Ground Truth Labeler App Ground truth 24

Learn more about detecting objects in images by exploring examples in the Automated Driving System Toolbox Label detections with Ground Truth Labeler App Add automation algorithm for lane tracking Extend connectivity of Ground Truth Labeler App 25

Learn more about detecting objects in images by exploring examples in the Automated Driving System Toolbox Train object detector using deep learning and machine learning techniques Explore pre-trained pedestrian detector Explore lane detector using coordinate transforms for monocamera sensor model 26

Some common questions from automated driving engineers 1011010101010100101001 0101010100100001010101 0010101001010100101010 0101010100101010101001 0101010010100010101010 0010100010100101010101 0100101010101010010101 0100101010010110000101 0100101010101010010010 1010011101010100101010 vehicle 1 F 2 How can I visualize vehicle data? How can I detect objects in images? How can I fuse multiple detections? 27

Learn more about sensor fusion by exploring examples in the Automated Driving System Toolbox Design multi-object tracker based on logged vehicle data Generate C/C++ code from algorithm which includes a multi-object tracker Synthesize driving scenario to test multi-object tracker 28

Upcoming Webinar: Introduction to Automated Driving System Toolbox Date Time 25 Jan 2018 1:30 AM PST 25 Jan 2018 6:00 AM PST 25 Jan 2018 11:00 AM PST www.mathworks.com > Events > Upcoming Webinars https://www.mathworks.com/company/events/webinars/upcoming/introduction-to-automated-driving-system-toolbox- 2355969.html 29

Resources 30

Q & A 31