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Safety Is What Drives Us: Introducing the NVIDIA Self-Driving Safety Report

This blog post was originally published at NVIDIA's website. It is reprinted here with the permission of NVIDIA.

Autonomous vehicles promise to reduce traffic accidents by replacing unpredictable human drivers with artificial intelligence. But how do manufacturers ensure these new drivers are truly safe?

To help answer that, NVIDIA has released the Self-Driving Safety Report.

With this report, we are opening up our development processes. We show how we harness the unprecedented computing power of GPUs to create functionally safe self-driving systems. Achieving the highest levels of compute enables us to incorporate diversity and redundancy into every solution — from sensor types to processors to algorithms — ensuring there is never just one line of defense in the event of a failure.

As a solutions provider to the vast majority of vehicle makers, suppliers, sensor makers, startups and mapping companies in the autonomous driving space, NVIDIA makes safety our first priority. And we have integrated it into every step of the development process.

We believe safety is at the heart of the transition to autonomy. Our report details how compute performance translates to safety at all stages, from initial data collection to public road testing.

The Four Pillars of Safe Autonomous Driving

Safe autonomous driving is built on four fundamental pillars. With high-performance compute at their core, these tenets illustrate NVIDIA’s dedication to safety and ensure a robust self-driving technology development cycle.

Pillar 1: Artificially Intelligent Design and Implementation Platform

A safe AI driver requires a compute platform that spans the entire range of autonomous driving, from assisted highway driving to robotaxis. It must combine deep learning, sensor fusion and surround vision to enable the car to make split-second decisions based on massive amounts of data.

Pillar 2: Development Infrastructure That Supports Deep Learning

A single test vehicle can generate petabytes of data annually. Capturing, managing and processing this massive amount of data for not just one car, but a fleet, requires an entirely new computing architecture and infrastructure.

Pillar 3: Data Center Solution for Robust Simulation and Testing

The ability to test in a realistic simulation environment is essential to providing safe self-driving vehicles. By coupling actual road miles with simulated miles in a high-performance data center solution, manufacturers can comprehensively test and validate their technology.

Pillar 4: Best-in-Class, Pervasive Safety Program

Self-driving technology development must follow a pervasive safety methodology that emphasizes diversity and redundancy in the design, validation, verification and lifetime support of the entire autonomous system. These programs should follow recommendations from federal and international agencies such as the National Highway Traffic Safety Administration, International Organization for Standardization and the global New Car Assessment Program.

This safety program includes a comprehensive evaluation process before our safety drivers validate NVIDIA technology on public roads. Drivers and co-pilots must complete rigorous training before operating vehicles, which are continuously tested for hardware and software readiness before drives.

Working Together

In addition to these four pillars, significant research and development, as well as industry-wide collaboration, is essential to safely deploying autonomous vehicles.

NVIDIA continues to work with our wide and diverse ecosystem as well as regulators to share knowledge and formulate standards for this budding technology.

Read the full report here.

Danny Shapiro
Senior Director of Automotive, NVIDIA