Course Schedule

Prerequisites

MATLAB Fundamentals or equivalent experience using MATLAB. Image Processing with MATLAB, Computer Vision with MATLAB and basic knowledge of image processing and computer vision concepts. Deep Learning with MATLAB is recommended.

Automated Driving with MATLAB

This two-day course provides hands-on experience with developing and verifying automated driving perception algorithms. Examples and exercises demonstrate the use of appropriate MATLAB^® and Automated Driving Toolbox^™ functionality. Topics include:

Labeling of ground truth data
Visualizing sensor data
Detecting lanes and vehicles
Processing lidar point clouds
Tracking and sensor fusion
Generating driving scenarios and modeling sensors

See detailed course outline

Prerequisites

Day 1 of 2
Labeling of Ground Truth Data	Objective: Label ground truth data in a video or sequence of images interactively. Automate the labeling with detection and tracking algorithms. Overview of the Ground Truth Labeler app Label regions of interest (ROIs) and scenes Automate labeling View and export ground truth results
Visualizing Sensor Data	Objective: Visualize camera frames, radar, and lidar detections. Use appropriate coordinate systems to transform image coordinates to vehicle coordinates and vice versa. Create a bird’s-eye plot Plot sensor coverage areas Visualize detections and lanes Convert from vehicle to image coordinates Annotate video with detections and lane boundaries
Detecting Lanes and Vehicles	Objective: Segment and model parabolic lane boundaries. Use pretrained object detectors to detect vehicles. Perform a bird’s-eye view transform Detect lane features Compute lane model Validate lane detection with ground truth Detect vehicles with pretrained object detectors
Processing Lidar Point Clouds	Objective: Work with lidar data stored as 3-D point clouds. Import, visualize, and process point clouds by segmenting them into clusters. Register point clouds to align and build an accumulated point cloud map. Import and visualize point clouds Preprocess point clouds Segment objects from lidar sensor data Build a map from lidar sensor data

Day 2 of 2
Fusing Sensor Detections and Tracking	Objective: Create a multi-object tracker to fuse information from multiple sensors such as camera, radar and lidar. Track multiple objects Preprocess detections Utilize Kalman filters Manage multiple tracks Track with multi-object tracker
Tracking Extended Objects	Objective: Create a probability hypothesis density tracker to track extended objects and estimate their spatial extent. Define sensor configurations Track extended objects Estimate spatial extent
Generating Driving Scenarios and Modeling Sensors	Objective: Create driving scenarios and synthetic radar and camera sensor detections interactively to test automated driving perception algorithms. Overview of the Driving Scenario Designer app Create scenarios with roads, actors, and sensors Simulate and visualize scenarios Generate detections and export scenarios Test algorithms with scenarios

Guaranteed to Run

When you register for one of these courses, you can rely on the fact that it won't be canceled or rescheduled for any reason.

Course Schedule

Results 1 - 6 of 6

Dates	Location	Language	Price
09 Jun 2020- 10 Jun 2020	China, 北京	Chinese	CNY 6,500
07 Jul 2020- 08 Jul 2020	China, 上海	Chinese	CNY 6,500
15 Sep 2020- 16 Sep 2020	China, 中国，广州	Chinese	CNY 6,500
28 Oct 2020- 29 Oct 2020	China, 中国，深圳	Chinese	CNY 6,500
24 Nov 2020- 25 Nov 2020	China, 北京	Chinese	CNY 6,500
22 Dec 2020- 23 Dec 2020	China, 上海	Chinese	CNY 6,500

Results 1 - 6 of 6

The pricing applies for purchase and use in United States, For pricing in other regions Contact Sales. The product price does not include sales, use, excise, value-added, or other taxes. Any applicable taxes, duties, levies, assessments and governmental charges payable in connection with this purchase will be assessed on the order. Refer to Training Policies for more information