INTRODUCTION
Two decades ago, NVIDIA invented the GPU, sparking a revolution
in computing. This core technology was born in the gaming
and professional visualization industries, and has now translated
to revolutionary leaps in high-performance and accelerated
computing, as well as artificial intelligence (AI). As we’ve scaled our
business and taken on new challenges, our systems and products
have pushed boundaries in robotics, healthcare, medicine, space
exploration, and entertainment. NVIDIA is now applying our futuristic
vision, computational performance, and energy efficiency to the
transportation industry—helping automakers around the world realize
the dream of safe, reliable autonomous vehicles.
Autonomous vehicles (AVs) are transforming the transportation industry. They have
the potential to save lives by drastically reducing vehicle-related accidents, reduce traffic
congestion and energy consumption, increase productivity, and provide mobility to
those who are unable to drive. NVIDIA partners with automakers, suppliers, sensor
manufacturers, mapping companies, and startups around the world to develop the best
solutions for the new world of mobility. We provide the systems architecture, AI
supercomputing hardware, and core software required to build all types of autonomous
vehicles—from automated cars and trucks to fully autonomous shuttles and robotaxis.
It all starts with NVIDIA DRIVE
™
, our highly scalable platform that can enable all levels
of autonomous driving as defined by the Society of Automotive Engineers (SAE). These
range from advanced driver-assistance system features (SAE Level 2: driver-assisted)
through robotaxis (SAE Level 5: full automation).
The computational requirements of fully autonomous driving are enormous—easily
up to 100 times higher than the most advanced vehicles in production today. With NVIDIA
DRIVE, our partners achieve an increase in safety, running sophisticated software
with many levels of diverse and redundant algorithms, in real-time.
To streamline development, we’ve created a single scalable architecture that advances
each level of autonomy with additional hardware and software while preserving
the core architecture. The same strategy holds for safety. Our architecture enriches
the overall system with elements to consistently improve safety.
\ THE BENEFITS OF SELFDRIVING VEHICLES
Data collected by the U.S. Department of Transportation in 2016 highlights the urgent
need for autonomous driving solutions. The number of road deaths increased by 5.6
percent over the previous year—more than in any of the previous 50 years
2
. The National
Highway Traffic Safety Administration estimates that 94 percent of traffic accidents
3
are caused by human error, including distracted driving, drowsiness, speeding, and
alcohol impairment.
Fortunately, technology that augments or replaces the driver can mitigate the vast
majority of those incidents. It can also significantly reduce the number of hours commuters
waste in traffic each year (currently averaging 42 hours) and the $160 billion lost to traffic
congestion
4
. Additionally, automated driving leads to more efficient traffic patterns,
so it can reduce the amount of air pollution the transportation industry contributes, estimated
in 2016 to be 28 percent of all U.S. greenhouse gas emissions
5
.
\ COMPUTE ENABLES GREATER SAFETY
NVIDIA uniquely provides the high-performance computing necessary to enable redundant
sensors, diverse algorithms, and additional diagnostics to support safer operation. We equip
cars with many types of redundant sensors for sensor fusion. Then, multiple diverse
AI deep neural networks and algorithms for perception, mapping localization, and path
planning are run on a combination of integrated GPUs, CPUs, deep learning accelerators
(DLAs), and programmable vision accelerators (PVAs) for the safest possible driving.
NVIDIA has created essential technologies for building robust, end-to-end systems
for the research, development, and deployment of self-driving vehicles. We offer a range
of hardware and software solutions, from powerful GPUs and servers to a complete
AI training infrastructure and in-vehicle autonomous driving supercomputer. We also
support academic research and early-stage developers, partnering with dozens of
universities worldwide and teaching courses on AI development at our Deep Learning
Institute. As we identify challenges, we turn them into opportunities and build solutions.
This report provides an overview of NVIDIA’s autonomous vehicle technologies and
how our unique contributions in safety architecture, co-designed hardware and software,
design tools, methodologies, and best practices enable the highest possible levels of
reliability and safety.
The underlying principle for safety is to introduce redundancy and diversity into the
system. NVIDIA applies this principle when architecting processors and computing
platforms, designing algorithms for driving and mapping, and integrating sensors into
the vehicle. We address safety at every phase of AV development and design in the
computational requirements to achieve the highest quality levels.
As an example, a car equipped with 10 high-resolution cameras generates 2 gigapixels
per second of data. Processing that data through multiple deep neural networks converts
to approximately 250 TOPS (trillion operations per second). Add other sensor types and
in-vehicle AI, and that performance requirement increases. For self-driving cars, compute
translates to safety
1
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