On the Road

On the Road to Eliminating Traffic Accidents: Examining the Potential of Autonomous Vehicles

Ben Spencer

The Association for Safe International Road Travel reports that nearly 1.3 million people
die in road crashes each year, and millions more receive injuries (Road Crash Statistics). With
such a looming social problem expected only to rise in coming years, many look for solutions to
combat the cause of accidents. In recent years, car designers and consumers alike have begun
to turn to autonomous vehicles as a potential solution to traffic accidents in the United States
and abroad. Despite concerns with the current state of autonomous vehicle (AV) technology,
research indicates that it holds great potential to significantly reduce traffic accidents and
provide numerous societal benefits, including additional mobility for groups such as the
disabled and the elderly.
Although many view AV technology as a recent development, automated driver
assistance of one kind or another has been in development for the last two decades. Car GPS
Navigation and Adaptive Cruise Control rolled into the market in 1995 (Shubbak 3). Automated
warning systems hit the road in the early 2000’s (Shubbak 3). In 2003, vehicle autonomy
advanced greatly when Toyota launched the first model with an automated parking feature.
With the automated parking feature, sensors on the front and rear of the car allowed it to
parallel park itself into an available lot without driver intervention (Shubbak 3). Since then,
technology industries such as Google and Tesla have made great progress in enabling cars to
operate autonomously in a variety of settings.

Developers and analysts classify AV technology using five levels, each with increased
ability of the car to function autonomously, as shown in the table below, provided by the
National Highway Traffic Safety Administration (cited in Litman):

Currently, Level 2 automation, including automatic braking and lane positioning, is available on
the market. Level 3 autonomation is being heavily tested, and Google has driven its
experimental cars hundreds of thousands of miles under specific road and weather conditions,
while levels 4 and 5 have yet to be developed (Litman 11). Considering the rate of technological
advancement, along with other factors, some predict that nearly 76 million autonomous
vehicles will be sold globally by 2035 (IHS Clarifies Autonomous Vehicle Sales).
Optimistic figures predicting the rise of AV technology in the coming decades have some
sceptics raising red flags. They question the ability of the system of sensors, lasers, and cameras
to perform in less than ideal circumstances. A technology analyst for the New York Times
mentions weather as a “prime cause of system failures” in which automated vehicles couldn’t
make the necessary judgement calls (Boudette). Considering the frequency of difficult weather

conditions and the potentially dangerous consequences of failure, this is a valid concern.
However, while AV technology is certainly not fully developed, it is hardly infantile. A recent
study published by the Victoria Transport Policy Institute states that “current technologies
allow autonomous vehicle operation in approximately 90% of conditions” (Litman 13). When
the automated system detects that weather and/or road conditions impair its navigation
abilities, it indicates the driver to retake control of the vehicle until the destination is reached
or conditions allow the automation to regain control. A report from Navigant Research assures
readers that ever-progressing sensor and computer technologies are already chipping away at
the weather problem and will “eventually” allow autonomous vehicles “to drive in almost all
road and weather conditions without human intervention” (Automated Driving). Until then,
human drivers will easily retake control of the vehicle without harm or accident occurring.
Critics of the development of AV technology may underestimate the safety
improvements made possible by self-driving cars. Traffic accidents are a major cause of death
and injury, and studies have shown that 93% of accidents are attributed to human error
(Shubbak 3). This means that the reduction of human error would plummet the number of
accidents worldwide, and even slight reductions in the numbers of accidents worldwide would
mean hundreds of thousands of lives spared annually. AV technology offers reduced human
error through enhanced perception, faster reaction times, and the elimination of impaired
Human sensory perception is a remarkable trait of nature, but it’s not foolproof. Senses
can be tricked by optical illusions or can fail to detect danger due to a lack of attentiveness. This
problem is magnified with the vision impaired, including many of the senior population. This

shortcoming of human perception is responsible for traffic accidents, especially those involving
pedestrians and cyclists. Technology analyst John Miller lists as one of the benefits of the rise of
autonomous vehicle technology “the broad range of sensors” that provide detailed tracking of
“the movement of other vehicles, pedestrians, and potential obstacles” and can do so “much
more thoroughly than the average driver” (par 3). Human senses are limited to a single
direction, that is, we can only look one way at a time. But the various cameras and sensors of
autonomous vehicle can scan the surrounding area in all directions, and the on-board computer
can take in and process all this data at incredible rates.
In addition to traditional RADAR and cameras providing the sensory detail for area
imaging, autonomous vehicles use LiDAR or Light Detection and Ranging. A rotating LiDAR
scanner atop the vehicle constantly scans the area around it and “every. . .LiDAR point reflects
full X, Y, Z reflectivity data over time” giving a dynamic 3D image of the surroundings at all
times (Lecklider). Provided with this “instantaneous visual information from multiple vantage
points,” the AV’s computer is able to “[analyze] millions of points” and know with precision the
environment through which the car drives, including “the lay of the road, road conditions, foot
traffic, approaching pedestrians, and vehicle traffic” (Lecklider). High detail imaging and smart
on-board computers are able to recognize and classify moving object as bicycles, pedestrians,
cars, etc. (Lecklider). With their powerful sensory abilities and matchless computational
interpretation of the data, autonomous cars have the potential to widely surpass human driver
perception. This increased perception in turn translates to safer roads, with less car accidents,
especially accidents involving pedestrians, bicyclists, and motorcyclists, all of which have proven
notoriously difficult for drivers to detect.

Autonomous cars don’t only bring a high quantity of sensory information received and
processed, but a higher quality as well. A human driver’s vision at night is limited to the beams
cast by headlights, a few hundred feet in advance and only directly ahead of the car. But
thermal cameras, which use Infrared radiation, rather than visible light, to create an image, can
see just as well in complete darkness as it can during the day. AdaSky, a company driven to

make fully autonomous vehicles a reality, has developed a thermal camera specifically for self-
driving cars called Viper (Driven to Save Lives). The company launched a video that contrasts

the view of a human driver using the headlights with the view of the Viper camera (Driven to
Save Lives). The side-by-side comparison of the two shows a stunning difference. Objects seen
by the headlights only seconds before the car passed them were seen clearly from great
distances by the IR camera. When it comes to having a clear picture of the road, a group of
pedestrians, or even a dog crossing the street at night, there is no question that the
autonomous car, equipped with a thermal camera, has the advantage.
Self-driving cars also bring promises of faster reaction times in cases of emergency. AVs
will be able to safely react to danger and hazards much quicker than can human reflexive
capabilities (Miller). The system’s already heightened computational abilities are further
assisted by vehicle-to-vehicle communication known as v2v. While using v2v, cars are
connected, or “talk to each other,” through a reserved, special frequency Wi-Fi connection
(Milford). This wireless connection allows cars close to each other to synchronize their actions,
giving rise to a phenomenon known as “platooning,” or close following by neighboring cars.
Platooning has the potential to increase road capacity and fuel efficiency, without
compromising safety (Milford). In fact, passengers in cars that closely share the road while

using v2v are actually safer. In a traditional car, any driver following behind another must first
realize that the car in front of them is braking, and then follow suit. If the human driver is
following too closely, doesn’t notice in time, or simply doesn’t react fast enough, he/she may
“rear-end” the car in front, which can be especially dangerous at highway speeds. When the
cars are autonomous and using vehicle-to-vehicle communication, the car(s) behind the leader
know when the other vehicles are going to brake even before they do so, and can match their
deceleration (Milford).
The leading cause of automobile-related accident and death in the U.S. is impaired
driving. Alcohol impaired drivers accounted for nearly a third (29%) of all traffic deaths in 2015
(Impaired Driving). Additionally, the influence of drugs (other than alcohol) is present in 16% of
motor vehicle crashes (Impaired Driving). Add to that the percentages of fatal accidents caused
by distracted (10%) and drowsy (21%) driving, and it becomes easy to see the source of the
problem (National Society for Statistics and Analysis; Tefft). If impaired driving of one kind or
another can be identified as the primary cause of fatal accidents, then autonomous vehicles
provide a clear and powerful solution. It is logical to assume total autonomation would entirely
solve the problem, after all, zero drunk drivers means zero drunk driving accidents. But even
partial autonomation (levels 2,3 and 4) offers huge benefits. There will always be those who
choose to get behind the wheel of a car when they shouldn’t, whether it be because of alcohol,
drugs, or fatigue. But the less they have to do to get home after they are behind the wheel is
something that we can change. Autonomous vehicles hold enormous potential to reduce the
amount of accidents caused by impaired driving. And every percentage point of impaired
driving accidents decreased translates to thousands of lives saved per year.

In addition to preventing traffic accidents across the board, AV technology offers
important social benefits. Typically, older drivers tend to have decreased cognitive, motor, and
sensory functions which puts them at greater risk of accident. They become more likely to do
harm to themselves, their passengers, and others, including pedestrians and other drivers on
the road. Autonomous vehicles solve this by decreasing the strain placed on drivers by full or
partial automation of normal driver tasks. Lane departure automation, forward collision
automation, and blind spot automation are examples of simple ways that AV technology
relieves the strain placed on drivers, particularly elderly drivers. (Harper et al.).
Additionally, “many seniors (those over age 65) and people with medical conditions
often face challenges traveling freely and independently” (Harper et al.). All those who are
unable to drive themselves “must rely on family, friends, government, or other providers to
meet their basic mobility needs” (Harper et al.). Transporting these individuals involves a

significant investment of time, energy and resources, and can create a burden for these above-
mentioned groups (they just don’t tell Grandma that). Automated vehicles represent a pathway

that could increase the mobility of the senior and disabled populations, without reliance on a
third party for transportation. It also follows that if the elderly and disabled didn’t have to rely
on “family, friends, government, or other providers” for basic transportation and mobility,
these groups would be able to reallocate their resources elsewhere, saving time, energy, and
The societal benefits of the development of AV technology don’t end at increased
mobility. As mentioned previously, vehicle-to-vehicle communication allows cars to share the
roads more efficiently. As a result, traffic congestion will decrease, and production of

atmospheric pollutants produced by cars will be reduced (Shubbak 5). Much of the urban space
currently used for parking would be unnecessary, freeing it up for other use. There is even the
possibility that city planners adapt to the oncoming changes, narrowing lanes and reducing
unnecessary traffic stops (Litman 10). It is worth exploring more into the possibilities of social
benefits that AV technology could bring to our society.
No one is expected to believe that autonomous vehicle technology is perfect. There are
no perfect solutions to looming social and economic problems such as traffic accidents.
Economists often preach that there is no such thing as a “free lunch,” or in other words, we
can’t expect to get something for nothing. And while AV’s current capabilities have some way to
go before they can fully deliver on the promises made, such promises are worth waiting and
working for. The vast number of deaths and injuries prevented merit our attention and interest.
The social benefits of increased mobility for the elderly and disabled justify our investment and
research. Change in the way that we get around is nearer than we think. One should expect to
see autonomous cars increasingly in the news, on the road, and someday, even in his/her own


Works Cited

“Automated Driving Vehicle Technologies.” Navigant Research, Navigant Consulting, Inc., 13

Sept. 2017, www.navigantresearch.com/research/automated-driving-vehicle-

Boudette, Neal E. “A Ball Bounced into the Road, and Other Hazards.” New York Times,

vol. 165, no. 57255, 2016a, pp. B5, https://www.lib.byu.edu/cgi-


“Driven to Save Lives.” AdaSky, AdaSky, www.adasky.com/.
Harper, Corey D., et al. “Estimating Potential Increases in Travel with Autonomous Vehicles for
the Non-Driving, Elderly and People with Travel-Restrictive Medical Conditions.”
Transportation Research: Part C, vol. 72, Nov. 2016, pp. 1-9. EBSCOhost,
“IHS Clarifies Autonomous Vehicle Sales Forecast – Expects 21 Million Sales Globally in the Year
2035 and Nearly 76 Million Sold Globally Through 2035.” IHS Online Newsroom, IHS

Inc., 9 June 2016, news.ihsmarkit.com/press-release/automotive/autonomous-vehicle-

“Impaired Driving: Get the Facts.” Centers for Disease Control and Prevention, Centers for
Disease Control and Prevention, 16 June 2017,

Lecklider, Tom. “Autonomous mining equipment years ahead of car development.” Evaluation
Engineering, NP Communications, LLC, 22 Feb. 2017,


Litman, Todd. “Autonomous Vehicle Implementation Predictions.” Victoria Transport Policy
Institute, Victoria Transport Policy Institute, 8 Sept. 2017, www.vtpi.org/avip.pdf.
Milford, Michael. “Coming soon to a highway near you: truck platooning.” The Conversation, 19

Nov. 2017, theconversation.com/coming-soon-to-a-highway-near-you-truck-platooning-

Miller, John. “Self-Driving Car Technology’s Benefits, Potential Risks, and Solutions.” The Energy
Collective, Energy Post Productions, 20 Aug. 2014,


National Center for Statistics and Analysis. (2017, March). Distracted driving 2015. (Traffic
Safety Facts Research Note. Report No. DOT HS 812 381). Washington, DC: National
Highway Traffic Safety Administration.
“Road Crash Statistics.” ASIRT, Association for Safe International Road Travel,
Shubbak, Mahmood H. “Self-Driving Cars: Legal, Social, and Ethical Aspects.” By Mahmood

H. Shubbak: SSRN, 13 Mar. 2017, papers.ssrn.com/sol3/papers.cfm?abstract_id=
Tefft, Brian C. “Prevalence of Motor Vehicle Crashes Involving Drowsy Drivers, United States,
2009 – 2013.” AAA Foundation, AAA Foundation for Traffic Safety, Nov. 2014,


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