This repository is a curated C++-focused resource collection for autonomous driving, targeting engineers and job seekers. It provides a structured path from foundational concepts to practical implementation and interview preparation, emphasizing engineering and deployment.

How It Works

The project compiles extensive learning materials, tools, and open-source projects for C++ autonomous driving development. It covers core modules like perception, localization, planning, and control, alongside infrastructure such as middleware and simulation. The emphasis is on practical, engineering-focused C++ implementations and career preparation.

Quick Start & Requirements

This is a resource repository, not a runnable application. Setup involves integrating various listed tools. Prerequisites include C++, and depending on sub-modules, potentially Python, GPU/CUDA for deep learning, and middleware like ROS/ROS2. External links to courses, books, papers, datasets, and tools are provided.

Highlighted Details

• Core Modules: Comprehensive coverage of perception, localization, mapping, prediction, planning, and control, with specific algorithms and techniques listed.
• Infrastructure: Detailed sections on C++ deployment, simulation (CARLA), and middleware (ROS/ROS2, DDS).
• Learning Resources: A rich compilation of courses, books, academic papers, and datasets.
• Career Focus: Dedicated sections for algorithm practice and common interview questions for various autonomous driving roles.

Maintenance & Community

The repository encourages community contributions for expanding resources, fixing links, and improving content, aiming to be a central hub for C++ autonomous driving developers. Specific community channels or a detailed roadmap are not explicitly linked.

Licensing & Compatibility

The repository does not explicitly state a software license, which may pose compatibility concerns for commercial use or integration into proprietary projects.

Limitations & Caveats

As a curated list, it requires users to independently integrate and configure various tools. The sheer breadth of topics can be overwhelming. While C++ is the focus, many ML components may rely on Python. The absence of a clear license is a significant adoption blocker.