Developing Algorithms for ADAS Systems with MATLAB and Simulink
From the series: Improving Your Racecar Development
Car safety systems are essential for the future of autonomous driving, and are already an important part of the cars we are driving today. Given that it is dangerous to test these systems in a real world environment, simulations for developing, testing, and verifying active safety system’s functionality are indispensable.
In this episode, we’ll discover how to realize lane tracking using Computer Vision Toolbox™ and adaptive cruise control in MATLAB® and Simulink®. But sometimes it is not sufficient to have only one sensor, because each type of sensor is restricted and can only provide a limited amount of information. Thus sensor fusion is necessary to generate a reliable data basis for the control system.
Marco demonstrates how to handle this multi-domain problem in the MATLAB and Simulink environment. In fact, the sensor data (radar signals or camera images) needs to be processed and a control algorithms need to be developed.
This subject not only applies to automotive OEMs. Formula Student teams have been working on that for years. For this episode, Team Starkstrom from UAS Augsburg and in person Robert Dollinger has provided us with a short video of their autonomous driving racing car which has been developed using MATLAB and Simulink.
Links to the examples of this episode can be found in the MATLAB Documentation. Please check the following links for more information.
- Lane Departure Warning System
- Automotive Adaptive Cruise Control Using FMCW Technology
- Modeling RF Front End in Radar System Simulation
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