Johnathan B.

Wheeldon

Professor Jenna De Gruy

ENG1101

2 December 2020

Self-Driving Cars: The Future

In 2019 4,400,000 people were involved in car accidents that were serious

enough to require medical attention within the United States (about 1.34% of

Americans). 38,800 of these injuries resulted in death (National Safety Council). A large

majority of these cases are caused by distracted driving, over ¼ of these incidents

alone are caused by texting and driving. Over the years, many alternatives have been

mentioned, one of which being self-driving cars. Self-driving cars take away the aspect

of distracted driving, making your level of sobriety, awareness, and fatigue, all negligent

factors. Eating a sandwich in your car will no longer put you at risk of being involved in

an accident. Self-driving cars have the opportunity to save thousands of lives every year

and should be more accepted in society as a real solution.

To begin with, self-driving cars have three primary types of sensors, camera,

radar, and LiDAR. Camera sensors detect RGB information (colors) in the surroundings

(Badue). However, these sensors can be blinded by sunlight and cannot detect contrast

and depth. Radar sensors can detect range information, and unlike camera sensors,

radar can detect in harsh and extreme weather conditions. LiDAR sensors are the most

accurate of the three, being able to detect depth to a precise distance. The only

downside is they do not work as well as radar sensors in bad weather conditions. In an

article about public ethics and health of autonomous (self-driving) cars, Jane Fleetwood,
Ph.D., discusses the application of these sensors, “​Autonomous vehicles constantly

obtain information from their environment, using a variety of sophisticated cameras and

sensors that rely on ultrasound, radar, and laser-based ranging, or “lidar.” A variety of

advanced technologies enable autonomous vehicles to correct for human mistakes and

“learn” from the “experience” of other autonomous vehicles. Because all store sensor

data, engineers are able to reconstruct events of a crash and examine what the vehicle

sensed through its multiple inputs and analyze the logic it used to determine its course.”

Using all three of these sensors, self-driving cars are able to work precisely and

accurately in all weather conditions, better than humans can. Also, through vigorous

testing, they have been refined to make tough decisions in dangerous situations.

Furthermore, as briefly being alluded to in the previous paragraph, self-driving

cars go through tremendous amounts of testing. With only four main factories known as

“Gigafactories,” Tesla has a total of 48,016 employees as of December 2019. The

number of hours that go into engineering, testing, and manufacturing these vehicles is

remarkable. About 2 hours outside of Fairbanks, Alaska in the town of Delta Junction is

Tesla’s Winter Testing Facility. With an average high temperature of below 0 degrees

Fahrenheit in winter months and having year-round ice and snow, Tesla scientists and

engineers test their electric cars and their self-driving capabilities at this facility. Tesla is

an electric car company founded in Palo Alto, California by its CEO Elon Musk. They

are currently the front runner in the self-driving car industry. A reporter, Tim Stevens,

was invited to this facility to check it out and said “​The best place to test this was on the

massive snowfield, just off the back straight of the oval. That means you can easily

drive into snow at triple-digit speeds if you're feeling randy. With a laptop and a few
clicks, one of Tesla's engineers shut off all the car's stability systems and invited me to

do an emergency lane change at 65 mph, the sort of thing you may have to do in Alaska

when encountering a wayward moose who's on the hunt for some trimmed greens.”

Stevens talks about the sheer conditions these cars are tested in and the remarkable

decisions they can make in extreme circumstances. This is just one example of the

amount of testing that goes into perfecting these cars.

On the contrary, one counterargument to this hypothetical utopia of self-driving

cars is that self-driving cars cannot make decisions as well as humans. One metaphor

for this idea is the trolley problem, which goes as follows. A train is on a track with five

people on it, and the only way to save the 5 people is to pull a switch to put the train

onto a path with only one person. Do you pull the switch or not? This is the trolley

problem (Nyholm). This idea can be applied to a self-driving car. How do you program

Artificial Intelligence to make a decision like this?

While at first, this seems like an unsurpassable dilemma. However, finding a

solution to this problem is extremely realistic. Scenarios that are as black and white as

the trolley problem do not occur with self-driving cars. No human or Artificial Intelligence

can accurately predict the outcome of deciding on a dangerous scenario. However,

Artificial Intelligence can use data it has stored from extensive testing and can perform

thousands of calculations in a split-second to quickly calculate the safest scenario. This

would be far safer than any decision a human could make under pressure. In an article

about applying the trolley theory to self-driving cars, Sven Nyholm said, “This does not

carry over to the case of self-driving cars. Rather, the decision-making about self-driving

cars is more realistically represented as being made by multiple stakeholders – for

car-manufacturers, etc. These stakeholders need to negotiate a mutually agreed-upon

solution. And the agreed-upon solution needs to be reached in light of various different

interests and values that the different stakeholders want to bring to bear on the

decision.” Agreeing on a solution to this issue is extremely achievable. This is a very

small dilemma to overcome for being a technological development that has so many

benefits.

Lastly, self-driving cars have the potential to save thousands of lives a year.

“​Autonomous vehicles, which could reduce traffic fatalities by up to 90% by eliminating

accidents caused by human error—estimated to be 94% of fatalities—could save more

than 29 000 lives per year in the United States alone. Around the world, autonomous

cars could save 10 million lives per decade, creating one of the most important public

health advances of the 21st century” (Fleetwood). This data shows the urgency of

implementing self-driving cars into our everyday lives. If 90% of accidents can be

eliminated, there needs to be an urgent push for implementing these vehicles into our

society.

Clearly, self-driving cars need to be more accepted by society. Self-driving cars

sadly have a false stigma of being unsafe, inaccurate, and unpredictable. The truth is,

self-driving cars and their technologies have been tested to such an extent that they are

safer in every way than human driving is. Hopefully, in the future, self-driving cars will

be a staple in society, and the accident rate and fatality rate will drastically decrease.
Works Cited

Achenbach, J., et al. “The Ethics of Accident-Algorithms for Self-Driving Cars: an

Applied Trolley Problem?” ​Ethical Theory and Moral Practice​, Springer

Netherlands, 1 Jan. 1970, link.springer.com/article/10.1007/s10677-016-9745-2.

Badue, Claudine, et al. “Self-Driving Cars: A Survey.” ​Expert Systems with Applications​,

Pergamon, 4 Aug. 2020,

www.sciencedirect.com/science/article/pii/S095741742030628X.

Fleetwood, Janet. “Public Health, Ethics, and Autonomous Vehicles.” ​American Journal

of Public Health​, American Public Health Association, Apr. 2017,

www.ncbi.nlm.nih.gov/pmc/articles/PMC5343691/.

National Safety Council. “Motor Vehicle Deaths Estimated to Have Dropped 2% in

2019.” ​Fatality Estimates - National Safety Council,​ 2019,

www.nsc.org/road-safety/safety-topics/fatality-estimates.

Stevens, Tim. “An Exclusive Look at Tesla's Extreme Cold Testing Facility.” ​Roadshow,​

4 Jan. 2019, www.cnet.com/roadshow/news/tesla-alaska-exclusive/.