Why driverless cars will be safer than human drivers – Business Insider

Posted: Thursday, November 17, 2016


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Tesla autopilot
Tesla’s latest Autopilot
system uses Nvidia’s computer platform for autonomous
vehicles.

Tesla

Automakers and tech companies are
racing to get the first self-driving car on the road.
 

Ford, GM, Tesla, Lyft, Google, and more all have plans to have
some form of autonomous car ready for commercial use within the
next five years. In fact, it’s estimated that by 2030 driverless
cars could make up as much as 60% of US auto sales, according to
Goldman Sachs.

While companies all have their own reasons for investing in this
technology, they all agree that one of the biggest benefits of
autonomous cars will be improved safety.

How, might you ask, is a car with no human driver, no steering
wheel, and no brake pedal safer than the cars we currently drive?

Well, it all comes down to the tech used to enable autonomous
vehicles.

The sensors

Driverless cars are designed to have almost a superhuman-like
ability to recognize the world around them. This is because they
use loads of sensors to gather tons of data about their
environment so that they can seamlessly operate in a constantly
changing environment.

Essentially, companies developing autonomous vehicles are really
just trying to replicate how a human drives using these
sensors. 


Google driverless car
Google self-driving
car project


“As a human you have senses, you have your eyes, you have your
ears, and sometimes you have the sense of touch, you are feeling
the road. So those are your inputs and then those senses feed
into your brain and your brain makes a decision on how to control
your feet and your hands in terms of braking and pressing the gas
and steering. So on an autonomous car you have to replace those
senses,” Danny Shapiro, senior director of Nvidia’s automotive
business unit, told Business Insider.

Some of the sensors used on autonomous cars include cameras,
radar, lasers, and ultrasonic sensors. GPS and mapping technology
are also used to help the car determine it’s position. 

All of these have different strengths and weaknesses, but
essentially they enable a lot of data to flow into the car.

The brain 

All of this collected data is then fed into the car’s computer
system, or “brain,” so to speak, and is processed so that the car
can make decisions.


nvidia drive px2 driverless car brainNVIDIA

One of the leading companies building the brains for these cars
is the chipmaker Nvidia. In fact, Tesla’s Autopilot system uses
Nvidia’s Drive PX2, which is the company’s newest computer system
for autonomous cars.

Drive PX2 is a powerful computer platform about the size of a
license plate that uses a variety of Nvidia’s chips and software
to take all of the data coming in from the sensors on an
autonomous car to build a three-dimensional model of the car’s
environment, Shapiro said. 

“In the brain of the car, it almost looks like a video game. We
are essentially recreating the world in a virtual 3D space,” he
said.

To do this, Nvidia and other companies developing driverless tech
use a little something called machine learning.

How it learns

Machine learning is a way of
teaching algorithms by example or experience and companies are
using it for all kinds of things these days. For example, Netflix
and Amazon both use machine learning to make recommendations
based on what you have watched or purchased in the past.
 

So to train a self-driving car, you would first drive the car
hundreds of miles to collect sensor data. You would then process
that data in a data center identifying frame by frame what each
object is.


google driverless car view
How Google’s self-driving car sees the world using
lidar sensors.

YouTube/SXSW

“Initially, the computer doesn’t know anything. We have to teach
it. And so what we want to do is if we want to teach it to
recognize pedestrians, we would feed it pictures of pedestrians.
But what we can do is feed it millions pictures of pedestrians
because pedestrians look different,” Shapiro said.

“The more data we feed it the more vocabulary it has and the more
it can recognize what a pedestrian is. And we do the same thing
with bicyclists, cars, trucks, and we do it at all times of day
and different weather conditions. So again, essentially it has
this infinite capability to build up a memory and understanding
of what all of these different types of things could encounter
would look like,” he said.



Listen to Cadie Thompson talk about how self-driving
cars see the world. From season 2 of Codebreaker, the podcast
from Marketplace and Business Insider. Click here for full
episodes

 

It might sound complicated, but when you think about it, it’s not
that different from how humans learn.

When you were born, you didn’t know what anything was. But your
parents or whoever raised you repeatedly pointed things out so
that you could identify different objects and people.

Superhuman driving skills

Once a computer model is created, then it’s loaded into the car’s
brain and hooked up to the rest of the car’s sensors to create
real-world model of the car’s environment.

The car uses this model to make decisions about how it should
respond in different situations. And because the car has sensors
all around it, it has access to a lot more data than a human
driver to help it make those decisions.

“There are insightful factors that get factored in. For example,
if you are driving along and there’s a parked car with nobody in
it, the vehicle will proceed next to that car,” Shapiro said.

“But if it sees the door is slightly open and there is somebody
in it, well the expectation is that the door will open at any
moment and someone will to try and get out of that car. So at
that point, when the car senses that, it’s either going to slow
down, or switch lanes if it can, and proceed with caution. And
because it has a full 360 degrees view around the car, it can be
tracking multiple objects, with much greater things happening,
with much greater accuracy than any human.”



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