Intel and Mobileye Begin Testing Their
Autonomous Fleet in Israel
The following is an opinion editorial provided by Amnon Shashua,
senior vice president at Intel Corporation and the chief executive
officer and chief technology officer of Mobileye, an Intel
company.
This press release features multimedia. View
the full release here:
https://www.businesswire.com/news/home/20180517005390/en/
The first of the Intel Mobileye 100-car
fleet hits the road in Jerusalem in May 2018. Intel and Mobileye,
an Intel Company, announced on May 17, 2018 that they are testing
the first cars of an autonomous vehicle fleet on the streets of
Jerusalem to demonstrate Intel's approach to making safe autonomous
driving a reality. (Credit: Intel Corporation)
The first phase of the Intel and Mobileye 100-car autonomous
vehicle (AV) fleet has begun operating in the challenging and
aggressive traffic conditions of Jerusalem. The technology is being
driven on the road to demonstrate the power of the Mobileye
approach and technology, to prove that the Responsibility-Sensitive
Safety (RSS) model increases safety, and to integrate key learnings
into our products and customer projects. In the coming months, the
fleet will expand to the U.S. and other regions. While our AV fleet
is not the first on the road, it represents a novel approach that
challenges conventional wisdom in multiple areas. Leveraging over
20 years of experience in computer vision and artificial
intelligence, our vehicles are proving the Mobileye-Intel solution
is the most efficient and effective.
The key differentiator of our system is that it is designed to
meet important goals of safety and economic scalability from the
beginning. Specifically, we target a vehicle that gets from point A
to point B faster, smoother and less expensively than a
human-driven vehicle; can operate in any geography; and achieves a
verifiable, transparent 1,000 times safety improvement over a
human-driven vehicle without the need for billions of miles of
validation testing on public roads.
Why Jerusalem?
The obvious answer is because Mobileye is based in Israel. That
makes it convenient, but we also wanted to demonstrate that the
technology can work in any geography and under all driving
conditions. Jerusalem is notorious for aggressive driving. There
aren’t perfectly marked roads. And there are complicated merges.
People don’t always use crosswalks. You can’t have an autonomous
car traveling at an overly cautious speed, congesting traffic or
potentially causing an accident. You must drive assertively and
make quick decisions like a local driver.
This environment has allowed us to test the cars and technology
while refining the driving policy as we go. Driving policy, also
known as planning or decision-making, makes all other challenging
aspects of designing AVs seem easy. Many goals need to be
optimized, some of which are at odds with each other: to be
extremely safe without being overly cautious; to drive with a
human-like style (so as to not surprise other drivers) but without
making human errors. To achieve this delicate balance, the Mobileye
AV fleet separates the system that proposes driving actions from
the system that approves (or rejects) the actions. Each system is
fully operational in the current fleet.
Balancing the conflicting goals of safety and
assertiveness
The part of our driving policy system that proposes actions is
trained offline to optimize an assertive, smooth and human-like
driving style. This is a proprietary software developed using
artificial intelligence-based reinforcement learning techniques.
This system is the largest advancement demonstrated in the fleet,
and you can see the impressive results in the event visuals. But
just like a responsible human driver, in order to feel confident
enough to drive assertively, this “driver” needs to understand the
boundary where assertive driving becomes unsafe. To enable this
important understanding, the AI system is governed by a formal
safety envelope that we call Responsibility-Sensitive Safety.
RSS is a model that formalizes the common sense principles of
what it means to drive safely into a set of mathematical formulas
that a machine can understand (safe following/merging distances,
right of way, and caution around obstructed objects, for example).
If the AI-based software proposes an action that would violate one
of these common sense principles, the RSS layer rejects the
decision.
Put simply, the AI-based driving policy is how the AV gets from
point A to point B; RSS is what prevents the AV from causing
dangerous situations along the way. RSS enables safety that can be
verified within the system’s design without requiring billions of
miles driven by unproven vehicles on public roads. Our fleet
currently implements Mobileye’s view of the appropriate safety
envelope, but we have shared this approach publicly and look to
collaborate on an industry-led standard that is technology neutral
(i.e., can be used with any AV developer’s driving policy).
Current sensor setup: only cameras. Why?
During this initial phase, the fleet is powered only by cameras.
In a 360-degree configuration, each vehicle uses 12 cameras, with
eight cameras providing long-range surround view and four cameras
utilized for parking. The goal in this phase is to prove that we
can create a comprehensive end-to-end solution from processing only
the camera data. We characterize an end-to-end AV solution as
consisting of a surround view sensing state capable of detecting
road users, drivable paths and the semantic meaning of traffic
signs/lights; the real-time creation of HD-maps as well as the
ability to localize the AV with centimeter-level accuracy; path
planning (i.e., driving policy); and vehicle control.
The camera-only phase is our strategy for achieving what we
refer to as “true redundancy” of sensing. True redundancy refers to
a sensing system consisting of multiple independently engineered
sensing systems, each of which can support fully autonomous driving
on its own. This is in contrast to fusing raw sensor data from
multiple sources together early in the process, which in practice
results in a single sensing system. True redundancy provides two
major advantages: The amount of data required to validate the
perception system is massively lower (square root of 1 billion
hours vs. 1 billion hours) as depicted in the attached graphic; in
the case of a failure of one of the independent systems, the
vehicle can continue operating safely in contrast to a vehicle with
a low-level fused system that needs to cease driving immediately. A
useful analogy to the fused system is a string of Christmas tree
lights where the entire string fails when one bulb burns out.
The radar/lidar layer will be added in the coming weeks as a
second phase of our development and then synergies among sensing
modalities can be used for increasing the “comfort” of driving.
Computing hardware on the road: today vs. tomorrow
The end-to-end compute system in the AV fleet is powered by four
Mobileye EyeQ®4s. An EyeQ4 SoC has 2.5 Terra OP/s (TOP/s) (for deep
networks with an 8-bit representation) running at 6 watts of power.
Produced in 2018, the EyeQ4 is Mobileye’s latest SoC and this year
will see four production launches, with an additional 12 production
launches slated for 2019. The SoC targeting fully autonomous is the
Mobileye EyeQ®5, whose engineering samples are due later this year.
An EyeQ5 has 24 TOP/s and is roughly 10 times more powerful than an
EyeQ4. In production we are planning for three EyeQ5s to power a
full L4/L5 AV. Therefore, the current system on roads today
includes approximately one-tenth of the computing power we will
have available in our next-gen EyeQ5-based compute system beginning
in early 2019.
The Mobileye-Intel approach is contrary to industry common
practice in the field, which is to over-subscribe the computing
needs during R&D (i.e., “give me infinite computing power for
development”) and then later try to optimize to reduce costs and
power consumption. We, on the other hand, are executing a more
effective strategy by under-subscribing the computing needs so that
we maintain our focus on developing the most efficient algorithms
for the sensing state, driving policy and vehicle control.
We certainly have much work ahead of us, but I’m extremely proud
of the Mobileye and Intel development teams for their hard work and
ingenuity to enable this first significant step. Our goal, in
support of our automaker customers, is to bring this system to
series production in L4/L5 vehicles by 2021.
More: Autonomous Driving at Intel | Mobileye News | More
Visuals Related to This Column
Professor Amnon Shashua is senior vice president at Intel
Corporation and the chief executive officer and chief technology
officer of Mobileye, an Intel company.
About Intel
Intel (NASDAQ: INTC) expands the boundaries of technology to
make the most amazing experiences possible. Information about Intel
can be found at newsroom.intel.com and intel.com.
Intel and the Intel logo are trademarks of Intel Corporation in
the United States and other countries.
View source
version on businesswire.com: https://www.businesswire.com/news/home/20180517005390/en/
IntelDanielle Mann, 973-997-1154danielle.mann@intel.com
Intel (NASDAQ:INTC)
Historical Stock Chart
From Apr 2024 to May 2024
Intel (NASDAQ:INTC)
Historical Stock Chart
From May 2023 to May 2024