NVIDIA 2018自动驾驶安全报告：智能设计与安全保障关键

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NVIDIA 2018年的自动驾驶安全报告详述了公司在推动自动驾驶技术发展中的关键立场和策略。该报告强调了安全性在自动驾驶领域的核心地位，由NVIDIA创始人兼CEO Jensen Huang亲笔强调，他们的目标是开发出一个安全、可靠且普适的自动驾驶平台，以实现更安全的道路、减少交通拥堵并确保所有人的移动性。报告分为四个主要支柱： 1. 人工智能设计与实施平台：这是实现自动驾驶的基础，NVIDIA专注于构建高效的人工智能平台，包括强大的GPU技术，如CUDA和TensorRT，这些工具支持深度学习算法的高效运行，使车辆能够理解和处理复杂的驾驶环境。 2. 深度学习开发基础设施：报告中提到的基础设施对于支持自动驾驶车辆的持续学习和优化至关重要。这包括高效的软件框架，如用于训练和优化神经网络的工具，以及能够快速迭代和更新的开发流程。 3. 数据中心解决方案：NVIDIA认识到模拟和测试在自动驾驶验证中的重要性，因此提供了强大的数据中心解决方案，通过仿真技术进行大规模的场景测试，确保车辆在各种环境和条件下的性能和安全性。 4. 最佳实践的普及性安全程序：NVIDIA建立了全面的安全管理体系，不仅关注硬件和软件层面，还涵盖整个产品生命周期，从设计到部署，严格遵循最高标准，确保每一环节都考虑到安全因素。报告总结了NVIDIA在自动驾驶安全上的整体架构，强调了他们在软硬件结合、数据驱动决策以及持续的安全评估方面的创新和努力。硬件部分着重于高性能计算单元，软件则围绕实时决策和路径规划展开；传感器技术是自动驾驶感知能力的核心，确保车辆能准确感知周围环境；数据中心则作为虚拟实验室，提供强大的仿真测试环境。这些支柱共同构建了NVIDIA的自动驾驶平台，使其能够在实现自动驾驶愿景的同时，确保安全始终处于最前沿。通过这份报告，NVIDIA展示了其在推动自动驾驶行业向商业化迈进时对安全的执着追求，以及其在技术创新和风险管理方面的成熟度。

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 SELFDRIVING 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

. The National

Highway Trafﬁc Safety Administration estimates that 94 percent of trafﬁc accidents

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

. 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

\ 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

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