Nvidia Corporation

Miguel Sainz

Gordon Grigor

Minwoo Park

John R Zedlewski

Pekka Janis

Zuoguan Wang

Davide Onofrio

Chia-Chih Chen

Yifang Xu

Carolina Parada

Vijay Chintalapudi

Ozan Tonkal

Jan Nikolaus Fritsch

I-Kuei Chen

Xin Liu

Mehdi Sajjadi Mohammadabadi

David Nister

Gary D Hicok

Michael Brian Cox

Ruchi Bhargava

Ratin Kumar

Justin Ebert

Timo Roman

Tony C Tam

Martin Hempel

Rouslan Lyubomirov Dimitrov

Louis Bavoil

Simon G Green

Wladimir Van Der Laan

Chin Shih

Chin-Hsien Shih

In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.

Real-time detection of lanes and boundaries by autonomous vehicles

A system and method for an on-demand shuttle, bus, or taxi service able to operate on private and public roads provides situational awareness and confidence displays. The shuttle may include ISO 26262 Level 4 or Level 5 functionality and can vary the route dynamically on-demand, and/or follow a predefined route or virtual rail. The shuttle is able to stop at any predetermined station along the route. The system allows passengers to request rides and interact with the system via a variety of interfaces, including without limitation a mobile device, desktop computer, or kiosks. Each shuttle preferably includes an in-vehicle controller, which preferably is an AI Supercomputer designed and optimized for autonomous vehicle functionality, with computer vision, deep learning, and real time ray tracing accelerators. An AI Dispatcher performs AI simulations to optimize system performance according to operator-specified system parameters.

Systems and methods for computer-assisted shuttles, buses, robo-taxis, ride-sharing and on-demand vehicles with situational awareness

A method for rendering a particle-based fluid surface includes generating a depth image of a plurality of particles which form a fluid surface, and smoothing the depth image to generate a smoothed depth image. From the smoothed depth image, a smoothed surface position and a smoothed surface normal for each of a plurality of pixels included within the smoothed depth image is determined, and a shaded surface of the fluid is rendered as a function of the smoothed surface positions and the smoothed surface normals.

System and method for rendering a particle-based fluid surface

The method includes receiving a plurality of graphics primitives for rendering at a GPU of a computer system and rendering graphics primitives into pixel parameters of the pixels of a display, wherein the parameters include pixel depth values and pixel normal values. For each pixel of the display, an ambient occlusion process is performed. The algorithm takes as input a ND-buffer containing pixel depth values and pixel normals. Based on the pixel 3-D position and the pixel normal vector, horizon heights are computed by sampling the ND-buffer and an occlusion term is computed for each pixel based on the horizon heights. Based on the pixel 3-D position, the pixel normal vector, a normal occlusion term is computed by sampling the ND-buffer above the horizon in multiple directions. An ambient occlusion illumination value is computed by combining the horizon occlusion term and the normal occlusion term.

Horizon split ambient occlusion

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Palo Alto, CA, United States of America

Miguel Sainz