This repository serves as a curated collection of paper reading notes on Deep Learning and Machine Learning, primarily focusing on autonomous driving. It is intended for researchers and engineers seeking to stay abreast of the latest advancements in areas like Bird's-Eye View (BEV) perception, motion planning, and end-to-end driving systems. The project offers a structured overview of key research papers, providing summaries and insights into their methodologies and contributions.

How It Works

The repository is organized by topic and publication date, allowing users to navigate through a vast collection of research papers. Each entry typically includes a link to the paper, brief notes on its content, and sometimes links to related code or supplementary materials. The author has meticulously categorized papers, highlighting significant trends and foundational works in autonomous driving research.

Quick Start & Requirements

No installation or execution is required. This repository is a static collection of notes. Accessing the content involves browsing the GitHub repository.

Highlighted Details

• Extensive coverage of autonomous driving literature, with a strong emphasis on Bird's-Eye View (BEV) perception and transformer-based architectures.
• Detailed chronological organization of papers, facilitating tracking of research evolution.
• Inclusion of notes on foundational papers, practical implementation details, and emerging trends.
• Regular updates to reflect the rapidly evolving field of AI and autonomous systems.

Maintenance & Community

The repository is maintained by Patrick Langechuan Liu, with inspiration drawn from other prominent researchers in the field. Updates appear to be regular, reflecting ongoing engagement with new research.

Licensing & Compatibility

The repository itself is likely under a permissive license (e.g., MIT or Apache, common for GitHub projects), but the content consists of notes on research papers, each with its own original licensing. Users should refer to the individual papers for their specific licensing terms.

Limitations & Caveats

This repository contains reading notes and summaries, not executable code or models. The depth of analysis for each paper can vary, and it is a personal collection, meaning it reflects the author's specific interests and interpretations.