Awesome Autonomous Vehicles

Overview

Awesome Autonomous Vehicles provides comprehensive resources for self-driving car development and research, including datasets, simulation tools, perception algorithms, and academic research in autonomous driving technology.

Key Categories

• Datasets - Annotated driving data for training and testing
• Simulation Environments - Virtual testing platforms
• Perception - Computer vision and sensor processing
• Planning & Control - Path planning and vehicle control
• Localization & Mapping - SLAM and HD mapping
• Research Papers - Academic publications and surveys
• Open-Source Projects - Complete autonomous driving stacks

Major Datasets

Image & Video Datasets

• KITTI - Benchmark suite for autonomous driving
• nuScenes - Full sensor suite dataset from Waymo
• Waymo Open Dataset - High-quality multi-modal sensor data
• Argoverse - HD maps with trajectory forecasting
• BDD100K - Diverse driving video dataset

3D Point Cloud Datasets

• SemanticKITTI - Semantic segmentation for outdoor scenes
• Lyft Level 5 - Prediction and motion forecasting dataset
• A2D2 - Audi Autonomous Driving Dataset

Simulation Environments

Open-Source Simulators

• CARLA - Open-source simulator for autonomous driving research
• LGSVL Simulator - Unity-based autonomous vehicle simulator
• AirSim - Microsoft's simulator for drones and cars
• SUMO - Traffic simulation suite

Commercial Simulators

• NVIDIA DRIVE Sim
• IPG CarMaker
• rFpro

Perception Systems

Computer Vision

• Object detection (YOLO, Faster R-CNN, SSD)
• Semantic segmentation (DeepLab, SegNet)
• Lane detection algorithms
• Traffic sign recognition

Sensor Fusion

• LiDAR and camera fusion
• Radar integration
• Multi-modal sensor processing
• Kalman filtering techniques

3D Object Detection

• PointPillars
• SECOND
• VoxelNet
• PointRCNN

Localization & Mapping

SLAM (Simultaneous Localization and Mapping)

• ORB-SLAM - Visual SLAM system
• LIO-SAM - LiDAR-Inertial Odometry
• LOAM - LiDAR Odometry and Mapping
• Cartographer - Google's real-time SLAM

HD Mapping

• OpenStreetMap for autonomous vehicles
• Lanelet2 - Map format for automated driving
• HD map generation from sensor data

Planning & Control

Path Planning

• A* and Dijkstra algorithms
• RRT (Rapidly-exploring Random Trees)
• Lattice-based planning
• Learning-based planning

Vehicle Control

• PID controllers
• Model Predictive Control (MPC)
• Pure pursuit and Stanley controllers
• Reinforcement learning for control

Open-Source Autonomous Driving Stacks

• Autoware - Open-source software for autonomous driving
• Apollo - Baidu's autonomous driving platform
• CARLA - Includes reference autonomous driving agents
• Openpilot - Comma.ai's open-source ADAS

Research Centers

• Waymo Research
• Aurora Innovation
• MIT CSAIL
• CMU Robotics Institute
• Stanford AI Lab

Safety & Regulations

• ISO 26262 functional safety
• SAE autonomy levels (L0-L5)
• Verification and validation methods
• Safety case development

Pricing

Free and open source (GitHub repository)