The NVIDIA DRIVE Team is constantly innovating, developing end-to-end autonomous driving solutions that are transforming the industry. 

Experience Our Latest AV Innovations

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  • NVIDIA DRIVE Dispatch

Short-form videos highlighting the building blocks of our autonomous vehicle technology.


Self-Supervised Reconstruction of Dynamic Driving Scenarios

Autonomous vehicle simulation is effective only if it can accurately reproduce the real world. The need for fidelity increases—and becomes more challenging to achieve as scenarios become more dynamic and complex. In this episode, learn about EmerNeRF, a method for reconstructing dynamic driving scenarios.


Ensuring Precision with Dynamic View Synthesis

As automakers integrate autonomy into their fleets, challenges may emerge when extending autonomous vehicle technology to different types of vehicles. In this edition of NVIDIA DRIVE Labs, we dive into viewpoint robustness and explore how recent advancements provide a solution using dynamic view synthesis.


Pruning AI Models for Peak Performance

HALP (Hardware-Aware Latency Pruning), is a new method designed to adapt convolutional neural networks (CNNs) and transformer-based architectures for real-time performance. In this video, learn how HALP optimizes pre-trained models to maximize compute utilization.


Taking Autonomous Vehicle Occupancy Prediction Into the Third Dimension

The concept of "3D occupancy prediction" is critical to the development of safe and robust self-driving systems. In this episode, we go beyond the traditional bird's eye view approach and showcase NVIDIA's 3D perception technology, which won the 3D Occupancy Prediction Challenge at CVPR 2023.


Enhanced Obstacle Avoidance for Autonomous Parking in Tight Spaces

Early Grid Fusion (EGF) is a new technique that enhances near-field obstacle avoidance in automatic parking assist. EGF combines machine-learned cameras and ultrasonic sensors to accurately detect and perceive surrounding obstacles, providing a 360-degree surround view.


Enhancing AI Segmentation Models for Autonomous Vehicle Safety

Precise environmental perception is critical for autonomous vehicle (AV) safety, especially when handling unseen conditions. In this episode of DRIVE Labs, we discuss a Vision Transformer model called SegFormer, which generates robust semantic segmentation while maintaining high efficiency. This video introduces the mechanism behind SegFormer that enables its robustness and efficiency.


Generating Potential Accident Scenarios for Autonomous Vehicles Using AI

Testing autonomous vehicles (AVs) in potential near-accident scenarios is critical for evaluating safety, but is difficult and unsafe to do in the real world. In this episode of DRIVE Labs, we discuss a new method from NVIDIA researchers called STRIVE (Stress-Test Drive), which automatically generates potential accident scenarios in simulation for AVs.


Helping AVs Better Understand Speed Limit Signs

Understanding speed limit signs may seem like a straightforward task, but it can quickly become more complex in situations in which different restrictions apply to different lanes, or when driving in a new country. This episode of DRIVE Labs shows how AI-based live perception can help AVs better understand the complexities of speed limit signs, using both explicit and implicit cues.


How AI Improves Radar Perception for Autonomous Vehicles

Diverse and redundant sensors, such as camera and radar, are necessary for AV perception. However, radar sensors that leverage only traditional processing may not be up to the task. In this DRIVE Labs video, we show how AI can address the shortcomings of traditional radar signal processing in distinguishing moving and stationary objects to bolster AV perception.

Brief updates from our AV fleet, highlighting new breakthroughs.


November, 2023

In the latest edition of NVIDIA DRIVE Dispatch, learn about generating 4D reconstruction from a single drive as well as PredictionNet, a deep neural network (DNN) that can be used for predicting future behavior and trajectories of road agents in autonomous vehicle applications. We also take a look at testing for the New Car Assessment Program (NCAP) with NVIDIA DRIVE Sim.


January, 2023

See the latest advances in autonomous vehicle perception from NVIDIA DRIVE. In this dispatch, we use ultrasonic sensors to detect the height of surrounding objects in low-speed areas such as parking lots. RadarNet DNN detects drivable free space, while the Stereo Depth DNN estimates the environment geometry.


February, 2022

DRIVE Dispatch returns for Season 2. In this episode, we show advances in end-to-end radar DNN-based clustering, Real2Sim, driver and occupant monitoring, and more.


July, 2021

In this episode of NVIDIA DRIVE Dispatch, we show advances in traffic motion prediction, road marking detection, 3D synthetic data visualization and more.


June, 2021

In this episode of NVIDIA DRIVE Dispatch, we show advances in driveable path perception, camera and radar localization, parking space detection and more.


March, 2021

In this episode of NVIDIA DRIVE Dispatch, we show advances in synthetic data for improved DNN training, radar-only perception to predict future motion, MapStream creation for crowdsourced HD maps and more.


February, 2021

See the latest advances in DepthNet, road marking detection, multi-radar egomotion estimation, cross-camera feature tracking, and more.


January, 2021

Explore progress in parking spot detection, 3D location in landmark detection, our first autonomous drive using an automatically generated MyRoute map and road plane, and suspension estimation.


December, 2020

Check out advances in scooter classification and avoidance, traffic light detection, 2D cuboid stability, 3D freespace from camera annotations, lidar perception pipeline, and headlight/tail light/street light perception.

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