Covers the architecture, training, and optimisation of neural networks for practical vehicle deployment. Key problems addressed include efficient training data collection from live fleets, trigger classifiers for high-value sensor event selection, and automated ground truth label generation without exhaustive manual annotation.

• System and method for obtaining training data
• Generating ground truth for machine learning from time series elements
• Generating ground truth for machine learning from time series elements (US10997461B2)

Covers the perception pipeline translating raw camera feeds into actionable driving decisions. Patents address processing image data from vehicle-mounted cameras using trained ML models, predicting 3D positions and trajectories of surrounding features, and feeding predictions into vehicle control systems.

• Predicting three-dimensional features for autonomous driving (US20200249685A1)
• Predicting three-dimensional features for autonomous driving (CA3128028A1)
• Systems and methods for labeling images for training machine learning model

Covers the infrastructure for collecting, validating, and routing data from connected vehicle fleets back to centralised training systems. The core technical problem is data economics — operating millions of vehicles as distributed data-gathering nodes without overwhelming network and compute resources.

• Generating ground truth for machine learning from time series elements (SG11202108322QA)
• Generating ground truth for machine learning from time series elements (AU2020215680A1)
• System and method for obtaining training data (WO2020056331A1)

Covers classification and recognition of visual patterns in vehicle sensor data, including identifying road features, object types, and scene semantics from camera images. This domain underpins the labelling and annotation systems that feed Tesla's fleet-learning pipeline.

• Systems and methods for labeling images for training machine learning model
• Video annotation using deep network architectures (US9330171B1)
• System and method for obtaining training data

Covers motion estimation and object tracking within image sequences — the technical foundation for predicting how lane markings, vehicles, and road features move relative to the camera over time. This work underpins 3D trajectory prediction from monocular camera systems, a distinctively Tesla-centric vision-only approach.

• Predicting three-dimensional features for autonomous driving
• Generating ground truth for machine learning from time series elements
• Systems and methods for labeling images for training machine learning model

The ImageNet challenge paper (36,016 citations) established the benchmark infrastructure that drove the deep learning revolution in computer vision from 2012 onward, making it one of the most cited papers in AI history and the foundational reference for the field.

Papers including Deep visual-semantic alignments (2,238 citations) and DenseCap (620 citations) established methods for grounding natural language in visual features, directly anticipating the multimodal models that have become central to modern AI systems including GPT-4o.

The PixelCNN++ paper (366 citations), produced during a 2016 OpenAI collaboration, reflects Karpathy's early engagement with probabilistic generative architectures — a thread that connects his Stanford-era research to his later contributions to GPT-4o at OpenAI in 2024.