Master Class: Driving into the Future: AI-Enabled Autonomous Systems
Artificial intelligence (AI) is increasingly being used in autonomous systems to enhance their performance, efficiency, and safety. AI algorithms such as deep learning, reinforcement learning, and machine learning are commonly used to develop AI-enabled autonomous systems. These algorithms allow the system to learn from data and improve its performance over time. High resolution sensors such as camera, lidar, and radar provide a wealth of data that feed perception algorithms and enable new ways of understanding the surrounding environment. Perception and navigation subsystems leverage deep learning and machine learning for a variety of applications such as object detection and target classification, estimating occupancy map for navigation, acceleration motion planning, and more. Control leverage reinforcement learning develops controllers for vehicle actuation in a complex environment.
In this master class, learn how MATLAB® and Simulink® enable engineers and scientists working on autonomous systems (UAVs, UGVs) to build and leverage artificial intelligence in their workflows while working on perception, navigation, and controls.
Highlights:
- Handling data (scarcity, annotation, large data, preprocessing)
- Developing and hyperparameter tuning of AI models
- Formally verifying the robustness properties of AI models
- Acceleration of motion planning using deep learning
- Development of a hybrid controller for an automated parking valet
Use cases:
Published: 17 Aug 2023
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