Self-Driving Car Dataset Guide#

Complete Self-Driving Dataset Workflow with nuScenes, Multi-Sensor Data, and Advanced Analysis

Level: Intermediate | Estimated Time: 25-40 minutes | Tags: Self-Driving, Autonomous Vehicles, nuScenes, Multi-Sensor, Video Sequences, 3D Data

This step-by-step guide will walk you through a complete self-driving car dataset workflow using FiftyOne. You’ll learn how to:

  • Load and organize complex multi-sensor self-driving datasets

  • Work with video sequences and temporal data

  • Handle 3D bounding boxes and camera projections

  • Apply advanced filtering and curation techniques

  • Use embeddings and similarity for dataset analysis

  • Integrate with the FiftyOne Model Zoo for enhanced insights

Guide Overview#

This guide is broken down into the following sequential steps:

  1. Loading Self-Driving Datasets - Learn how to load complex self-driving datasets into FiftyOne, working with multi-frame video sequences, sensor metadata, and associating labels with frames

  2. Advanced Self-Driving Techniques - Dive into advanced tools for managing and analyzing self-driving datasets, including filtering by events, syncing labels across sequences, and curating key frames

Prerequisites#

Who Is This Guide For

This tutorial is designed for computer vision engineers working with self-driving car datasets. Whether you’re dealing with large-scale video data, sensor fusion, or frame-level labels, this guide shows how FiftyOne can streamline your workflow.

Required Knowledge

You should be familiar with the FiftyOne dataset structure and have a basic understanding of working with grouped datasets. If not, we recommend starting with the Getting Started with Grouped Datasets guide first.

Packages Used

The notebooks will automatically install the required packages when you run them. The main packages we’ll be using include:

  • fiftyone - Core FiftyOne library for dataset management and visualization

  • nuscenes-devkit - nuScenes dataset SDK for loading and processing data

  • open3d - 3D data processing and visualization

  • torch & torchvision - PyTorch framework for deep learning operations

  • transformers - Hugging Face transformers for embedding models

  • umap-learn - Dimensionality reduction for visualization

  • matplotlib - Visualization and plotting

Each notebook contains the necessary pip install commands at the beginning, so you can run them independently without any prior setup.

System Requirements

  • Operating System: Linux (Ubuntu 24.04), macOS (Run All (only the sam2 section doesn’t work))

  • Python: 3.10 recommended for compatibility

  • Memory: 16GB RAM recommended for large datasets

  • Storage: 10GB free space for nuScenes dataset

  • Notebook Environment: Jupyter, Google Colab, VS Code notebooks (all validated)

The nuScenes Dataset#

The nuScenes dataset is a public dataset for autonomous driving that contains 1,000 scenes of 20 seconds each, captured in Boston and Singapore. It includes data from 6 cameras, 1 LIDAR, 5 RADAR, GPS, and IMU sensors, making it perfect for learning multi-sensor data handling.

The dataset includes: - Multi-camera video sequences with synchronized timestamps - 3D bounding box annotations in global coordinates - LIDAR point clouds and RADAR data - GPS and IMU sensor data - Scene metadata and weather conditions

Multi-Sensor Data Handling#

Camera Data

  • Multi-Camera Setup - Working with 6 synchronized cameras (front, back, left, right, front-left, front-right)

  • 3D to 2D Projection - Converting 3D bounding boxes to 2D camera coordinates

  • Temporal Sequences - Managing video sequences with frame-level annotations

  • Sensor Synchronization - Aligning data across different sensor modalities

Advanced Features

  • Grouped Datasets - Organizing data by scenes and sensor types

  • Dynamic Group Views - Creating flexible views across different sensor combinations

  • Embedding Analysis - Using CLIP embeddings for semantic search and visualization

  • Model Integration - Applying SAM2 and other models for enhanced analysis

Self-Driving Analysis Workflow#

This tutorial demonstrates a complete self-driving workflow that combines:

  1. Data Ingestion - Loading complex multi-sensor datasets with proper organization and metadata

  2. Temporal Analysis - Working with video sequences, understanding frame relationships, and managing temporal data

  3. Advanced Curation - Using embeddings, similarity search, and model predictions to identify key moments and edge cases

  4. Multi-Sensor Fusion - Coordinating data across cameras, LIDAR, and other sensors for comprehensive analysis

This integrated approach gives you the tools to not just load self-driving data, but to understand complex multi-sensor relationships, identify critical scenarios, and prepare datasets for model training and validation.

Ready to Begin?#

Click Next to start with the first step: Loading Self-Driving Datasets with FiftyOne.