Toyota Research Institute-Advanced Development, NVIDIA partner to accelerate use of autonomous vehicles and AI technologies
Toyota Research Institute-Advanced Development (TRI-AD) and NVIDIA announced a new collaboration to develop, train and validate self-driving vehicles. The partnership builds on an ongoing relationship with Toyota to utilize the NVIDIA DRIVE AGX Xavier AV computer and is based on close development between teams from NVIDIA, TRI-AD in Japan and Toyota Research Institute (TRI) in the United States.
The broad partnership includes advancements in:
AI computing infrastructure using NVIDIA GPUs
Simulation using the NVIDIA DRIVE Constellation platform (earlier post)
In-car AV computers based on DRIVE AGX Xavier or DRIVE AGX Pegasus
The agreement includes the development of an architecture that can be scaled across many vehicle models and types, accelerating the development and production timeline, and simulating the equivalent of billions of miles of driving in challenging scenarios.
Our vision is to enable self-driving vehicles with the ultimate goal of reducing fatalities to zero, enabling smoother transportation, and providing mobility for all. Our technology collaboration with NVIDIA is important to realizing this vision. We believe large-scale simulation tools for software validation and testing are critical for automated driving systems.—Dr. James Kuffner, CEO of TRI-AD
Self-driving vehicles for everyday use and commercial applications in countless industries will soon be commonplace. Everything that moves will be autonomous. Producing all these vehicles at scale will require a connected collaboration for all elements of the system. Our relationship with TRI-AD and TRI is a model for that collaboration.—NVIDIA founder and CEO Jensen Huang
AI, and specifically deep learning, has become a vital tool for the production of next-generation automated vehicles, particularly because of the need to recognize and handle the nearly infinite number of scenarios encountered on the road.
Simulation has proven to be a valuable tool for testing and validating AV hardware and software before it is put on the road. As part of the collaboration, TRI-AD and TRI are utilizing the NVIDIA DRIVE Constellation platform—introduced at GTC (NVIDIA’s GPU Technology Conference) last year—for components of their simulation workflow.
DRIVE Constellation is a data center solution, comprising two side-by-side servers. The first server—Constellation Simulator—uses NVIDIA GPUs running DRIVE Sim software to generate the sensor output from a virtual car driving in a realistic virtual world.
The second server—Constellation Vehicle—contains the DRIVE AGX car computer, which processes the simulated sensor data. The driving decisions from Constellation Vehicle are fed back into Constellation Simulator, aiming to realize bit-accurate, timing-accurate hardware-in-the-loop testing.
This end-to-end simulation toolchain will help enable Toyota, TRI-AD and TRI to bring automated vehicles to market.
DRIVE Constellation is an open platform into which ecosystem partners can integrate their environment models, vehicle models, sensor models and traffic scenarios. By incorporating datasets from the broader simulation ecosystem, the platform can generate comprehensive, diverse and complex testing environments.
Toyota Research Institute-Advanced Development, Inc. focuses on the advanced development of software for automated driving efforts. Its mission is to build the world’s safest automated driving car, as well as strengthening coordination with Toyota Research Institute (TRI) and the research and advanced development teams within the Toyota Group.
Activities include developing automated driving software, leveraging data-handling capabilities and creating a straight line from research to commercialization.
Toyota Research Institute is a wholly owned subsidiary of Toyota Motor North America under the direction of Dr. Gill Pratt. The company, established in 2016, aims to strengthen Toyota’s research structure and has four initial mandates: 1) enhance the safety of automobiles; 2) increase access to cars to those who otherwise cannot drive; 3) translate Toyota’s expertise in creating products for outdoor mobility into products for indoor mobility; and 4) accelerate scientific discovery by applying techniques from artificial intelligence and machine learning.