Duke University

Driving into the Future of Autonomous Cars

Abdulla ShahidMath 89S: Mathematics of the Universe

Professor: Dr. Hubert BrayNovember 1st, 2016

Introduction:

Every year, 1.3 million people die in car crashes around the world and 37,000 of those

deaths occur in the United States (3rd most after India and China). In the United States 8,000 of

the deaths that occur involve drivers between the ages of 16 and 20. Furthermore, in the United

States, 2.35 million people are injured or disabled per year by car crashes. There have been

multiple attempts to decrease the number of accidents that happen on the road. These solutions

range from apps that prevent drivers from using their phone to companies creating cars with

“automatic breaking”1. These solutions, however, have failed to significantly decrease the

number of car crashes that occur as they do not address the main problem in all car crashes, the

driver. Ninety percent of all motorized vehicle accidents are caused by human errors which is

why many experts believe that the most effective approach to decreasing the number of accidents

that occur on the road would be to create autonomous cars. In this paper, I will outline a timeline

of autonomous cars, describe their functionality, discuss their economic feasibility, and evaluate

the benefits and disadvantages to using autonomous cars in the future. 2

What are Autonomous Cars?

One of the key concepts to understand when dealing with autonomous cars is what it

means to be “truly autonomous”. A truly autonomous vehicle is a car that is capable of operating

without a human. Currently, we have vehicles that are close to being truly autonomous (Tesla

Motors is currently the frontrunner), however, we are still years away from creating cars that are

able to drive on their own.

1 Cars are able to stop by themselves when they are near an object and the human driver is not braking 2 Statistics taken from ASIRT

Autonomous Cars: An idea from the Past?

Before understanding how current day autonomous vehicles work, we need to understand

how they have been developed up to this point in time. One common misconception that people

have about autonomous cars is about when they first came to exist. Many people believe that

autonomous cars have only been relevant for the past decade however, car manufacturers have

been developing autonomous cars since the 1920s. Below is a brief outline that examines the

history of autonomous cars through the past century:

1920: In the 1920s, a 1926 Chandler was equipped with an antenna to create the Linrrican

Wonder. The technology behind the Chandler was quite simple, a second car would follow the

Chandler and send radio impulses through antennas to the Chandler’s circuit-breakers. These

circuit-breakers were linked to small electronic motors that would ultimately direct the

movement of the Chandler(Mario).

1950: In the 1950s, RCA Labs was able to create a miniature car that was controlled by wires

that were laid on the floor of a laboratory. This ultimately evolved into a full-size system in

Nebraska. Inside the pavement, detector circuits had been buried inside that ultimately send

impulses to navigate the car and determined the speed of other vehicles on the road(Vanderbilt).

1980-90: It was in the 1980s when we first began to see glimpses of autonomous cars that were

able to function without helper devices3. In the start of the decade, we saw the emergence of the

vision-guided Mercedes Benz which was able to achieve a speed of 39 miles per hour on an

empty road. A few years later, the ALV project (Autonomous Land Vehicle) used LIDAR and

computer vision to control a vehicle at speeds of 19 miles per hour(Vanderbilt). By the end of the

3 Referring to the different devices used to guide cars up to this point

decade, Carnegie Mellon University was able to use neural networks4 to steer and control

autonomous cars and is now the foundation of contemporary control strategies(Mario).

2000-Present: In the start of the 21st century, we begin to see the formation of government-

funded programs geared towards creating autonomous vehicles. It is not until 2010 that we begin

to see major automotive and tech companies venturing into the field of autonomous cars. In

2010, VisLab (Italian based) was able to test run an autonomous car (9,900 mile run) which

marked the first intercontinental land journey by an autonomous vehicle (Gerla). Furthermore, in

2010, the Institute of Control Engineering of Technishe Universitat Braunchweig developed the

first autonomous car that was able to drive on the streets of Germany (with traffic). After 2010,

multiple companies such as Google, VisLab, and Nissan were able to create cars that were able

to drive autonomously but were still not ready to be driven on the road as they lacked the ability

to follow signs and other road/driving protocols(Vanderbilt). Finally, in 2015-16, Tesla Motors

released autopilot technology that ultimately allowed their cars to drive autonomously.

Throughout the past year, Tesla released multiple software patches that increased the

autonomous abilities of their cars (ex: allowed Tesla vehicles to self-park). This month, October

2016, all of Tesla vehicles are now at an SAE level of 55. Tesla still, however, has not yet been

able to release full self-driving vehicles as they need to continue testing their cars. Tesla recently

stated that they plan to reach full self-driving by the end of 2017 to the start of 2018.

The Functionality of Autonomous Cars:

Before delving into how autonomous cars function, it is important that one understands

how autonomous cars are classified. The SAE International (Society of Automotive Engineers)

developed a classification system that is based on the “amount of driver intervention and

4 Neural Networks are essentially computerized systems that model the brain/CNS 5 SAE LEVELS are explained in the next section

attentiveness needed rather than capabilities of the vehicle/autonomous system” (Stephen).

Below is a table published by the SAE that describes the different levels:

This table may be difficult to understand, especially for those with no driving experience, so I

have created a table on the next page that summarizes the different levels without the mechanical

jargon:

Level 0 An automated system does not control the vehicle. The system may issue warnings or alerts. Ex: An alarm that beeps when a car is near an object when being reversed

Level 1 An automated system where the driver should be ready to take control of the vehicle at any time. Ex: Cruise Control

Level 2 An automated system where the system is in charge of driving, steering, and braking and the human is required to detect objects. Ex: low level automated vehicles

Level 3 The driver does not have to do anything when driving in a known environment such as a highway. The driver should still be ready to intervene if need be.

Level 4 The driver does not have to anything in almost all environments except for the case of an outlier (bad weather)

Level 5 An automated system where the only human interaction required is to set the destination point.

How do Autonomous Cars work?

Now that we have a general idea of how autonomous cars are developed, we should now

be able to better understand how autonomous cars work. For this paper, the main vehicles that

we will be looking at will be cars from Google and Tesla as these companies have been the most

successful in the autonomous driving industry. Below is a detailed description of how Google

and Tesla autonomous driving systems work:

Google:

Computer Vision6:

One of the main differences between Google and Tesla’s automotive systems is Google’s

usage of Light Detection and Ranging technology (LIDAR). Lidar is a surveying method that

measures the distance to a target by illuminating that target with a laser light(Nicholls). In

Google’s car, the LIDAR technology consists of 64 lasers that spin at 9000 rpm, ultimately

producing a 360-degree view. The survey of the environment created by the LIDAR is then

converted to create a 3D graphical representation of the surrounding environment so that

obstacles in the environment can be viewed in real time. Once the 3D representation is created,

the car uses its attached sensors to determine the position of the different obstacles and objects

present in the environment so that the car can move accordingly. The car also has a video camera

(attached to the console) that enables the car to see pedestrians and stoplights that are difficult to

catch with the LIDAR. One of the advantages of the Google Car is its ability to sync Google

Maps, one of the premier mapping services available today. Google uses Google Maps to help

determine the path that the car will follow and also the cars current position(Times). Since

correct positioning is crucial for an autonomous car, there is also a position estimator on the left

wheel of the car that detects the speed of the car which ultimately results in the system having a

more accurate position of the car. The graphic below illustrates the process described above in a

more detailed manner:

Tesla:

6 Both companies use different methods to create “Computer Vision”. Computer Vision is the process of creating digital data from the real world (analog data).

In addition to the technologies described above, Google is also attempting to implement a new

technique known as vehicular communication. Vehicular communication is a method that will

allow cars to communicate with each other on the road through cloud servers. Vehicles would

then be able to pull this information down from the server, and thus have a better understand of

how traffic flow is in certain areas which would ultimately aid them by helping them determine

the most efficient path (quickest and safest) to go from one place to another.

Tesla:

Computer Vision:7

As mentioned above, Tesla and Google have different mechanisms to create a visual

representation of a car’s environment. Both Google and Tesla revolve heavily around sensors,

however, the main difference as mentioned above is Google’s usage of LIDAR. Tesla’s

autonomous system (also known as Tesla Autopilot System) is composed of multiple sensors

that are placed around the car to help it understand its environment. The image below shows how

a driver sees the Autopilot system:

7 Most of the information listed was accessible on Tesla’s website

In terms of hardware, Tesla’s vehicles currently “includes a forward radar, a forward-looking

camera, a high-precision digitally-controlled electric assist braking system, and 12 long-range

ultrasonic sensors placed around the car” (Thompson). These ultrasonic sensors are strategically

placed around the vehicle so that the car is able to sense when something is too close and gauge

the appropriate distance so that it can slow down. One important detail to understand about these

sensors is that they can report inaccurate information if there is something that is interfering with

them. We saw this in the fatal Tesla accident, where a man died in a car accident because of an

“issue with the autopilot system”. While the exact reasoning is unknown, many experts believe

that the Tesla sensors were affected by the brightness of the sun which causes the sensors/radar

to believe that the truck was actually an overhead road sign because of its large height. While

this is a problem, we also need to be reminded that truly autonomous cars do not currently exist

(no car is at an SAE level 5 right now), so a driver using Tesla’s Autopilot needs to be ready to

take over at all times. Going back to the sensors, the image below shows the “circle” that is

created by the sensors placed around the car:

Tesla’s autopilot system ultimately takes the data inputted by the sensors and cameras and

creates a digital representation of its surroundings (stationary and moving objects). The image

below shows an example of how the digital representation looks for a car:

As you can see from the image, the digital representation created is quite similar to the image

created by Google’s car which ultimately emphasizes how despite using different methods to

learn, both autonomous systems yield similar graphics to display the information that they have

learned.

Decision Making:

In addition to learning about its surroundings, one of the other important parts to an

autonomous system is its decision making algorithm/ability. Currently, no car is truly

autonomous as they still have drivers, however in the near future, these cars will be required to

make decisions on whether they should stop, slow down, or swerve when in an emergency

situation. While Tesla and Google have not yet fully developed autonomous cars, many people

are already discussing the ethical dilemmas that will result from a computerized system making a

decision about life or death. While there will be some ethical challenges, I believe that the

challenges are being over-exaggerated due to the lack of knowledge people have about these

systems. One common misconception about the decision making process is that many people

believe the system will be a series of if/else statements (ex: If a person is in front of Car- turn

right). In reality, however, these machines will rely on machine learning and pattern recognitions

to make decisions ultimately allowing them to mimic the decision making process of a

human(Galceran). By using the elements of AI, autonomous cars will be able to learn and

analyze how “good” drivers drive and ultimately replicate their driving ability. Once autonomous

cars reach an SAE level of 5, the next issue is the “Trolley Problem”. Currently, engineers from

both companies are saying that they are teaching their systems to hit a stationary object

whenever there is a chance that it might hit a human being. While this is easy to program, the

real problems begin to occur when the system has to decide between hitting one person vs

another. Engineers have stated that through machine learning, cars will be able to detect which

path would lead to the least amount of distress and follow that path(Galceran). Overall while

these problems must be considered, one needs to realize two things, the first is that these

hypothetical situations rarely occur in the real world and the second is that a computer system

will most likely make better decisions (in terms of what to do in an accident/situation) in

comparison to a bad driver.

Economic Feasibility:

Currently, the safest small car to buy is a 2018 Honda Civic which has an MSRP of

$18,600. While Google has not yet released its car in the market, the current cost of just LIDAR

is $75,000 per vehicle which is already almost four times the price of a Honda Civic(Stephen).

When adding all other costs, the current price of a car from Google is looking to be over

$100,000 dollars making it virtually unaffordable to almost all Americans (average car budget

for a person living in the U.S is $17-$33K)8. It is because of this absurd price that Google has

stated numerous times that the price of LIDAR is going to drop significantly over the next

decade ultimately making the car more affordable. Tesla on the other hand is now producing cars

with autonomous systems that have a starting MSRP of $30,000 as they already have cars with

elements of autonomous driving (SAE level 2-4) that go for anywhere from $66,000 to

$110,000. As one can see, it is far more affordable to buy a Honda Civic than it is to buy an

autonomous car, however as advancements in technology continue, the cost of manufacturing

these cars will decrease.

Conclusion/Future:

8 Figures reported by multiple financial magazines

Overall, while we still are far away from having truly autonomous cars, we have made

great strides in creating cars that have some elements of autonomous driving. Companies such as

Google and Tesla are the frontrunners in this field, and other companies are trying to join the

movement. Just recently, rumors about Apple purchasing McLaren began to circulate ultimately

showing their interest in also joining the autonomous driving industry. It is because there are so

many different companies involved in this industry, there are a variety of different approaches to

creating an autonomous car. Some of the common elements that can be found in all of the cars

developed up to this point are computer vision, the usage of a variety of sensors, cameras, and

forms of neural networks. Decision-making is still an unclear process as fully autonomous cars

(SAE Level 5) have not yet been released to the public, however, the usage of artificial

intelligent learning methods looks to be an efficient way for these systems to make decisions.

Overall, I believe that autonomous cars, over the next few decades, will become cheaper, and

safer than the cars we currently have today and will ultimately help us drive into a safer future.

Word Cited

Galceran, Enric, Alexander Cunningham, Ryan Eustice, and Edwin Olson. "Multipolicy Decision-

Making for Autonomous Driving via Changepoint-based Behavior Prediction." Robotics:

Science and Systems XI (2015): n. pag. Web. 1 Nov. 2016.

Gerla, Mario. "Internet of Vehicles: From Intelligent Grid to Autonomous Cars and Vehicular Clouds."

(2014): n. pag. Web. 1 Nov. 2016.

"How an Autonomous Car Gets Around." The New York Times. The New York Times, 25 Oct. 2012.

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Nicholls, Keith W. "High-Latitude Oceanography Using the Autosub Autonomous Underwater

Vehicle." Limnology and Oceanography 53.5, Part 2. Autonomous and Lagrangian Platforms

and Sensors (ALPS) (2008): 2309-320. Web. 1 Nov. 2016.

"Road Crash Statistics." Road Crash Statistics. N.p., n.d. Web. 01 Nov. 2016.

Stephen. "Elon Musk Says That the LIDAR Google Uses in Its Self-driving Car ‘doesn’t Make Sense

in a Car Context’." 9to5Google. N.p., 17 Oct. 2015. Web. 01 Nov. 2016.

@teslamotors. "Tesla Press Information." Press Kit. N.p., n.d. Web. 01 Nov. 2016.

Thompson, Cadie. "Here's How Tesla's Autopilot Works." Business Insider. Business Insider, Inc, 01

July 2016. Web. 01 Nov. 2016.

Vanderbilt, Tomq. "Autonomous Cars Through the Ages." Wired.com. Conde Nast Digital, n.d. Web.

01 Nov. 2016.

"What Is LIDAR." US Department of Commerce, National Oceanic and Atmospheric Administration.

N.p., n.d. Web. 01 Nov. 2016.

"Where To? A History of Autonomous Vehicles." A History of Autonomous Vehicles. N.p., n.d. Web.

01 Nov. 2016.

Images:

SAE Levels Diagram: http://www.sae.org/misc/pdfs/automated_driving.pdf

Tesla Images: https://www.tesla.com/

Google LIDAR Diagram: http://www.nytimes.com/interactive/2012/10/28/automobiles/how-an-autonomous-car-gets-around.html