Autonomous Systems

Learn autonomous systems through practical guides on perception, planning, localization, and system integration.

This section is for engineers who want to understand how self-driving and autonomous systems are built as connected systems, not as isolated algorithms. The guides below are organized to help you move from perception basics into mapping, planning, and full-system thinking.

Who this section is for

• readers learning the architecture of self-driving systems
• engineers who want practical explanations of perception and planning
• students who need a clearer path through autonomous-system topics

Start here

• Camera perception in self-driving cars
• System integration for self-driving cars
• Occupancy grid maps in practice

Top guides

• Computer vision in practice
• Vision-based navigation in practice
• Region masking for lane detection
• Trajectory generation basics
• Yolo for real-time detection

Learning path

1. Start with perception and understand how cameras and sensors represent the world.
2. Move into mapping and localization to understand where the vehicle is.
3. Learn planning and trajectory generation to understand how the vehicle decides what to do next.
4. Finish with system integration so the full architecture makes sense as one system.

Perception path

• Camera perception in self-driving cars
• Computer vision in practice
• Yolo for real-time detection

Planning and system path

• Occupancy grid maps in practice
• Trajectory generation basics
• System integration for self-driving cars

Read next

The best first article here is Camera perception in self-driving cars. If you already know the perception basics, move to System integration for self-driving cars to see how the pieces fit together.