Sensor Fusion and Tracking for Autonomous Systems

Autonomous systems require sensing, perception, planning, and execution. The stages of perception and planning pose the core of research and development efforts for autonomous systems.

The white paper demonstrates how you can use MATLAB® and Simulink® to:

Define scenarios and generate detections from sensors including radar, camera, lidar, and sonar

Develop algorithms for sensor fusion and localization

Compare state estimation filters, motion models, and multi-object trackers

Perform what-if analysis with different scenarios

Evaluate positional accuracy and track assignment performance versus ground truth

Generate C code for rapid prototyping

Read this white paper to learn how you can eliminate development effort spent starting over with each new autonomous system through examples focused on sensor fusion and multi-object tracking.

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Download the white paper to learn how sensor fusion and tracking algorithms can be designed for autonomous system perception using MATLAB and Simulink. Examples include multi-object tracking for camera, radar, and lidar sensors. 

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Using MATLAB and Simulink, you can focus your efforts on developing more advanced decisions and planning algorithm. Explore set of products for automated driving.

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