自动驾驶

Python sample codes and textbook for robotics algorithms.

OpenPCDet Toolbox for LiDAR-based 3D Object Detection.

:zap: The Mobile Robot Programming Toolkit (MRPT)

Learn autonomous driving by building it: Python simulations and an open textbook where the system grows one stage at a time. Swap and compare localization, planning and control algorithms in the same system.

PointCNN: Convolution On X-Transformed Points (NeurIPS 2018)

😎 Awesome LIDAR list. The list includes LIDAR manufacturers, datasets, point cloud-processing algorithms, point cloud frameworks and simulators.

[ICLR 2025 Oral] The official implementation of "Diffusion-Based Planning for Autonomous Driving with Flexible Guidance"

自动驾驶笔记，以解析各模块知识点、整合行业优秀解决方案进行阐述，以帮助自己及有需要的读者；包含深度学习、deeplearning、无人驾驶、BEV、Transformer、ADAS、CVPR、特斯拉AI DAY、大模型、chatgpt等内容.

[ICCV 2025] Official implementation of the paper “MagicDrive-V2: High-Resolution Long Video Generation for Autonomous Driving with Adaptive Control”

🌐 Vision-Language-Action Models for Autonomous Driving: Past, Present, and Future

Create a digital-twin style traffic visualization using only mp4 CCTV footage and its Google Maps location.

[CVPR 2025, Spotlight] SimLingo (CarLLava): Vision-Only Closed-Loop Autonomous Driving with Language-Action Alignment

Real-time 3D LiDAR obstacle detection, tracking & trajectory prediction for ROS 1 / ROS 2: ground segmentation, clustering, 2.5D occupancy, optional GPU CenterPoint (TensorRT). ~10 ms/scan on one CPU core.

[CVPR 2023 Highlight] LaserMix for Semi-Supervised LiDAR Semantic Segmentation

[Survey Paper] This repository collects research papers of large Foundation Models for Scenario Generation and Analysis in Autonomous Driving. The repository will be continuously updated to track the latest update.

Pytorch code for ICCV'23 paper. NEO 360: Neural Fields for Sparse View Synthesis of Outdoor Scenes

a comprehensive and critical synthesis of the emerging role of GenAI across the full autonomous driving stack

Developer Hub for NVIDIA Alpamayo, containing ready-to-use recipes for fine-tuning, reinforcement-learning post-training, quantization, and deployment.

[ECCV 2024] Embodied Understanding of Driving Scenarios