The NVIDIA DRIVE Team is constantly innovating, developing end-to-end autonomous driving solutions that are transforming the industry.
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Short-form videos highlighting the building blocks of our autonomous vehicle technology.
In this episode of DRIVE Labs, we discuss three key advancements from NVIDIA that use generative AI such as text-to-simulation to create realistic environments, generate natural driving behaviors, and edit the resulting scenarios to enable rigorous AV evaluation and training.
NVIDIA’s end-to-end driving model combines detection, tracking, prediction, and planning into a single network with a minimalistic design. The planning input comes directly from a bird’s-eye view feature map generated from sensor data.
Adapting driving behavior to new environments, customs, and laws is a long-standing challenge in autonomous driving. LLaDA (Large Language Driving Assistant) is an LLM network that makes navigating unfamiliar places easier by providing real-time guidance on regional traffic rules in different languages, for both human drivers and autonomous vehicles.
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.
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.
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.
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.
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.
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.
Brief updates from our AV fleet, highlighting new breakthroughs.
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.
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.
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.
In this episode of NVIDIA DRIVE Dispatch, we show advances in traffic motion prediction, road marking detection, 3D synthetic data visualization and more.
In this episode of NVIDIA DRIVE Dispatch, we show advances in driveable path perception, camera and radar localization, parking space detection and more.
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.
See the latest advances in DepthNet, road marking detection, multi-radar egomotion estimation, cross-camera feature tracking, and more.
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.
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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