DRIVE

Jan 29, 2024

Emulating the Attention Mechanism in Transformer Models with a Fully Convolutional Network

The past decade has seen a remarkable surge in the adoption of deep learning techniques for computer vision (CV) tasks. Convolutional neural networks (CNNs)...
13 MIN READ
Using Synthetic Data to Address Novel Viewpoints for AV Perception
Nov 13, 2023

Using Synthetic Data to Address Novel Viewpoints for Autonomous Vehicle Perception

Autonomous vehicles (AV) come in all shapes and sizes, ranging from small passenger cars to multi-axle semi-trucks. However, a perception algorithm deployed on...
7 MIN READ
Sep 26, 2023

Validating NVIDIA DRIVE Sim Radar Models

Sensor simulation is a critical tool to address the gaps in real-world data for autonomous vehicle (AV) development. However, it is only effective if sensor...
15 MIN READ
Aug 31, 2023

Deploying YOLOv5 on NVIDIA Jetson Orin with cuDLA: Quantization-Aware Training to Inference

NVIDIA Jetson Orin is the best-in-class embedded platform for AI workloads. One of the key components of the Orin platform is the second-generation Deep...
11 MIN READ
Image of a car backing into a parking spot using object perception.
Jul 12, 2023

Near-Range Obstacle Perception with Early Grid Fusion

Automatic parking assist must overcome some unique challenges when perceiving obstacles. An ego vehicle contains sensors that perceive the environment around...
5 MIN READ
May 18, 2023

Bringing Far-Field Objects into Focus with Synthetic Data for Camera-Based AV Perception

Detecting far-field objects, such as vehicles that are more than 100 m away, is fundamental for automated driving systems to maneuver safely while operating on...
7 MIN READ
DNN input
Mar 13, 2023

Detecting Obstacles and Drivable Free Space with RadarNet

Detecting drivable free space is a critical component of advanced driver assistance systems (ADAS) and autonomous vehicle (AV) perception. Obstacle detection is...
6 MIN READ
A gif showing cars on a street with potential collision
Feb 23, 2023

Generating AI-Based Potential Accident Scenarios for Autonomous Vehicles

Autonomous vehicles (AVs) must be able to safely handle any type of traffic scenario that could be encountered in the real world. This includes hazardous...
4 MIN READ
Jan 20, 2023

Validating NVIDIA DRIVE Sim Lidar Models

Autonomous vehicle development is all about scale. Engineers must collect and label massive amounts of data to train self-driving neural networks.  This...
2 MIN READ
Jan 12, 2023

Explainer: What’s the Difference Between Level 2 and Level 5 Autonomy?

To define the path to fully realized autonomy, the Society of Automotive Engineers (better known as SAE International) detailed six categories of...
1 MIN READ
Graphic featuring car
Jan 10, 2023

Updated Course: Integrating Sensors with NVIDIA DRIVE

Learn how to integrate your sensor of choice for NVIDIA DRIVE. This updated self-paced course now uses DriveWorks 5.8 and includes lidar examples.
1 MIN READ
Jan 09, 2023

Explainer: What Is Edge Computing?

Edge computing is the practice of processing data physically closer to its source.
1 MIN READ
Jan 04, 2023

Explainer: What Is Active Learning?

Finding the right self-driving training data doesn’t have to take a swarm of human labelers.
1 MIN READ
Automotive
Jan 04, 2023

Upcoming Webinar: Transforming Transportation with the Metaverse and AI

Learn how NVIDIA Omniverse and NVIDIA DRIVE Sim are used to create digital twin environments to train, test, and validate autonomous driving systems.
1 MIN READ
Dec 21, 2022

Explainer: What Is an AI Cockpit?

Intelligent interiors are transforming transportation.
1 MIN READ
Dec 14, 2022

Explainer: What Is MLOps?

Machine learning operations, MLOps, are best practices for businesses to run AI successfully with help from an expanding smorgasbord of software products and...
1 MIN READ