Driverless Technologies and Their Effects on Insurers and the State: An Initial Assessment

• Date: 01 December 2018
• Author: Juliann Ping,Martin F. Grace
• DOI: http://doi.org/10.1111/rmir.12110
• Published date: 01 December 2018

Risk Management and Insurance Review

C

Risk Management and Insurance Review, 2018, Vol.21, No. 3, 413-433

DOI: 10.1111/rmir.12110

FEATURE ARTICLE

DRIVERLESS TECHNOLOGIES AND THEIR EFFECTS ON

INSURERS AND THE STATE :ANINITIAL ASSESSMENT

Martin F. Grace

Juliann Ping

ABSTRACT

This article explores the impacts of new auto technologies and their financial

effects on insurance markets, a set of complementary services, and state rev-

enues. Weuse data from the National Association of Insurance Commissioners,

the National Highway TrafficSafety Administration’s Fatality Analysis Report-

ing System, the Bureau of Justice Statistics, and the Census Bureau to create

a data set that links industry and state finance variables to a set of variables

related to driving. Our purpose in this initial assessment is to estimate the sen-

sitivity of these financial variables to different indices of driving including the

number of drivers, the number of cars licensed per year, and the number of

vehicle miles driven. The resulting estimates are used to create elasticities to

show how sensitive each is to changes brought about by the new technologies.

INTRODUCTION

One of the most salient social risks, the risk of automobile crashes, is predicted to

change with the introduction of new driverless or autonomous technologies. Also, other

benefits associated with of driverless technologies may also reduce other costs associated

with driving such as its associated pollution, the demand for oil, and the widespread

productivity losses due to both traffic congestion and crashes. This article attempts to

document the effect of driverless technologies on insurance markets specifically as well

as state revenues and services related to automobile insurance. As a first endeavor, we

try to analyze the macro effects of a reduction in driving activity and its corresponding

impact on losses and other types of accident-related expenditures.

The United States experiences a significant cost due to auto crashes. A National High-

way Traffic Safety Administration (NHTSA) report (2015) estimates the cost of driving

crashes to be about $836 billion in 2010 (in 2018 dollars, $960 billion), which—in addition

to the deaths, injuries, and property damages—also includes costs due to pollution, con-

gestion, and reductions in quality of life. One of the reasons autonomous vehicles are so

Martin F. Grace is the Harry Cochran Professor of Risk Management at Fox School of Business,

Temple University, Philadelphia, Pennsylvania; e-mail: martin.grace@temple.edu. Juliann Ping

is a research assistant in the Department of Risk, Insurance and Healthcare Management at Fox

School of Business, Temple University, Philadelphia, Pennsylvania.

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interesting is because of their potential for significantly reducing these costs. Evidence

that even the lowest level of automation, so-called Level 1 automation, which implies

one automatic activity (like automatic braking systems [ABS], blind spot monitoring,

lane departure warning, or forward collision warning) has reduced crashes.1

Manufacturers claim that self-driving cars will be significantly safer than human-driven

cars as driverless technology will allow for more precise driving and quicker deci-

sion making. This increase in safety potential reduces the propensity for auto crashes

(Litman, 2014). However, self-driving cars in combination with human-driven cars on

today’s public roads may temporarily hinder the ideal prospects of a driverless society.

Conjecture exists that most self-driving cars will produce lower noxious emissions as

the cars will be designed as lightweight, two-passenger vehicles (Burns, 2013). Further,

these cars could be 10 times more energy efficient than today’s typical car (Burns, 2013).

Additionally,since one need not "drive" a self-driving car, the opportunity cost of transit

will be diminished (Frisoni et al., 2016). Driverless technology thus becomes an attractive

opportunity for automakers and consumers alike.

By utilizing the Society of Automotive Engineers (2016) international levels and defi-

nitions of driving automation, we can approach the projections of autonomous driving

with more uniformity and clarity. The levels are as follows:

1. Level 1: driver assistance,

2. Level 2: partial automation,

3. Level 3: conditional automation,

4. Level 4: high automation,

5. Level 5: full automation.

Different projections have been announced by various vehicle and auto parts manufac-

turers on their products and plans. Table 1 illustrates the level of automation that each

manufacturer expects to release in the form of a fleet of cars for either taxis or commercial

sale.

As seen in Table1, the majority of manufacturers estimate their releases of Level 4 vehicle

technology to be by 2020. Waymo, the division of Alphabet, has already released a fleet

of autonomous cars without safety drivers for testing in the Phoenix, Arizona metro area

(Ohnsman, 2017). Levels 1 and 2 are being used in vehicles today. These technologies

range from ABS to lane monitoring to unassisted parking. Level 3 represents a car that

the driver can shift certain functions to the vehicle to carry out but is still able to take

over if needed.

Levels 4 and 5 have significant automation capabilities, and the difference between

them lies in the fact that Level 5 automation requires self-driving cars to be reliable in

all driving conditions (i.e., bad weather or a rural environment). Before cars advance

1ABS, for example, while not effective in reducing fatal crashes, reduce nonfatal crashes by 6-8

percent(NHTSA, 2009). See also Harper et al. (2016) who conclude that these Level 1 technologies

could reduce fatal crashes by over 10,000 per year.