"Shifting from Code Analysis to Engineering Wisdom."

Dig into Apollo was originally designed to help developers navigate the Apollo autopilot system. As AI evolves to handle basic code explanation, this project is pivoting to focus on Design Patterns, Engineering Trade-offs, and Real-world Troubleshooting—the "human" experience that AI cannot replicate.

I am currently building WheelOS—an autonomous driving system driven by user input. While startup life is demanding, it provides a unique vantage point on what truly matters in production.

I am rededicating my efforts here to share high-value insights: moving beyond "how the code reads" to "how the system is designed and why it fails." Expect future updates to focus on problem-solving frameworks and architectural evolution.

The future of this project lies in these high-level engineering domains:

• Engineering Philosophy (PEPs): Establishing "Apollo Enhancement Proposals"—design standards and best practices for AD.
• Problem Diagnosis: Post-mortems of system failures and strategies for millisecond-level latency optimization.
• Architectural Trade-offs: Analyzing why specific algorithms were chosen over others in real-world constraints.

The following sections contain detailed, line-by-line code analysis. While valuable for understanding the foundation, please refer to the "New Horizon" sections above for modern engineering insights.

• What's apollo
• How to build
• Code learning
  • cyber
  • docker
  • modules
    • audio
    • bridge
    • canbus
    • data
    • drivers
    • dreamview
    • map
    • localization
    • perception
    • prediction
    • routing
    • planning
    • control
    • transform
    • tools
    • v2x
• performance
• simulation
• library
• papers
• questions

1. Macro Understanding: Grasp module functions first. It’s hard to understand the code if you don’t understand the intent. See this Beginner Tutorial.
2. Learn by Modules: Follow the specific documentation within this project to see how theories are implemented in code.
3. The "Pain Zone": Learning Apollo is difficult. Stay persistent; it usually takes 1-2 months of consistent study to feel comfortable.
4. Practice & Improve: No system is perfect. Try to implement latest https://www.google.com/search?q=papers, modify configurations, and "make your hands dirty" in the simulator.

• C++ Foundations: I recommend "C++ Primer" or courses by Hou Jie.
• Simulation: Use LGSVL.
• Theoretical Depth: Hongyi Li's Deep Learning and 3Blue1Brown's Math.

Contributions that focus on design patterns, bug-fixing experiences, or architectural improvements are highly welcome.

• Apollo Official
• Awesome Self-Driving Car