commaai/research
Research
This research framework provides a deep learning driving simulator and a multimodal data pipeline for autonomous vehicle research. It centers on the creation of synchronized autonomous vehicle datasets, which combine high-frequency vehicle telemetry with camera frames to train neural networks.
The project implements a convolutional neural network trainer specifically designed to predict steering angles and vehicle transition states from visual data. It features generative capabilities, using autoencoders and transition models to synthesize driving environments and simulate future vehicle movements based on recorded data.
The framework covers the full lifecycle of driving research, from recording and synchronizing multimodal sensor data to training supervised learning pipelines. It includes tools for visualizing prediction accuracy by comparing generated driving paths against ground-truth telemetry.
Features
- Autonomous Vehicle Simulators - Provides a deep learning driving simulator to synthesize environments and predict vehicle movements.
- Simulation Data Generators - Implements generative models that synthesize synthetic driving scenarios and visual data to train autonomous vehicle models.
- Telemetry Sequence Generators - Captures high-frequency hardware telemetry into structured files to create a training corpus for driving simulators.
- Generative Image Models - Builds generative models using camera footage and vehicle states to synthesize images of driving environments.
- State Transition Training - Implements training for transition models that predict future vehicle states to simulate autonomous driving movements.
- Multimodal Data Pipelines - Implements a system for recording, synchronizing, and aligning sensor data and video frames for autonomous driving research.
- Visual State Trainers - Provides tools for building and evaluating image-based models that predict vehicle steering and transition states.
- Steering Prediction Models - Trains neural networks to predict correct steering inputs based on visual camera feeds and telemetry.
- Multimodal Dataset Formatting - Organizes raw sensor logs and image frames into standardized formats suitable for deep learning model consumption.
- Supervised Learning Pipelines - Provides supervised learning pipelines to train neural networks mapping visual inputs to steering angle predictions.
- Autonomous Vehicle Dataset Curation - Implements curation pipelines for recording and structuring high-frequency sensor data and images for vehicle control models.
- Driving Scenario Simulators - Provides a research framework for training neural networks to simulate driving environments and predict steering angles.
- Autonomous Vehicle Datasets - Creates synchronized collections of camera frames and high-frequency telemetry for training deep learning models.
- Multimodal Data Synchronizers - Aligns high-resolution image frames with vehicle measurements using a uniform time base to ensure data consistency.
- Sensor Data Synchronization - Aligns high-frequency sensor telemetry with camera frames using a shared clock to ensure temporal consistency.
- Vehicle Telemetry Loggers - Records high-frequency telemetry from various hardware sensors into structured files to build training datasets.
- Model Prediction Evaluation - Compares predicted driving paths against ground-truth telemetry to evaluate the precision of steering models.
- Autoencoder Compression - Uses autoencoders to learn compressed representations of driving data for image synthesis and model checkpoints.
- Vehicle Telemetry - Provides tools for visualizing vehicle performance data and comparing predicted driving paths against actual measurements.
- Trajectory Accuracy Evaluation - Quantifies simulation fidelity by comparing predicted driving paths against ground-truth telemetry.
