This course offers introductory exploration into the cutting-edge field of artificial intelligence with environment interactions, focusing on the development and deployment of autonomous AI agents. Students will use computing tools and learning methods to develop autonomous AI systems for agent decision-making and interaction.

• AI agent learning paradigm;
• Deep reinforcement learning (DRL);
• Imitation learning (IL);
• Environmental interaction for AI agents;
• Datasets and simulators.

This course will introduce the fundamental technologies for autonomous vehicle sensors, perception, and machine learning, from electromagnetic spectrum characteristics and signal acquisition, vehicle extrospective sensor data analysis, perspective geometry models, image and point cloud processing, to machine/deep learning approaches. We will also have hands-on programming experience in vehicle perception problems through homework and class projects.

• Electromagnetic spectrum characteristics and 1D Radar signal processing;
• Visual perception using 2D image processing and machine learning recognition;
• 3D LiDAR and point cloud data representation and processing;
• Visual/LiDAR/IMU for vehicle simultaneous localization and mapping (SLAM);
• Deep learning for vehicle perceptual sensor data processing.

This course is designed to provide knowledge in the design and implementation of real-time parallel and high-performance computing (HPC), GPU computing, AI and edge-AI computing, simulation technologies for autonomous robots and vehicle software systems. The students will achieve these learning objectives through extensive examples, homework, case and paper studies, and project design.

• Programming and real-time computing;
• Parallel/HPC and GPU computing;
• AI computing for robots;
• Autonomous robot/vehicle simulation.