An Analysis of Economically Efficient Insurance Schemes. for Automated Vehicles. Brandon Xavier Rhodes June 2014 Advised by Professor Alain Kornhauser

Transcription

1 An Analysis of Economically Efficient Insurance Schemes for Automated Vehicles Brandon Xavier Rhodes June 2014 Advised by Professor Alain Kornhauser Submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Engineering Department of Operations Research and Financial Engineering Princeton University

2 I hereby declare that I am the sole author of this thesis. I authorize Princeton University to lend this thesis to other institutions or individuals for the purpose of scholarly research. Brandon Xavier Rhodes I further authorize Princeton University to reproduce this thesis by photocopying or by other means, in total or in part, at the request of other institutions or individuals for the purpose of scholarly research. Brandon Xavier Rhodes - ii -

3 Acknowledgements I would like to first thank my family for being there to support me throughout my Princeton journey and this process in particular. Your guidance has been invaluable. I would also like to thank Allie, whose gestures of support, from cupcakes to words of encouragement, provided me with persistent motivation. Finally, I would like to thank my advisor Professor Alain Kornhauser who inspired my thesis topic. In addition, Professor Kornhauser provided much appreciated guidance throughout my writing and was always available to discuss ideas or concerns I had. Professor Kornhauser, your excitement and passion for automated vehicles is evident both in the discussions we had about my thesis and in your lectures. It s been a pleasure to work with you on this research. - iii -

4 Abstract Automated vehicles have the potential to have a profound impact on the automobile insurance industry. Preliminary research suggests that various automation systems are correlated with a drastic reduction in the frequency of insurance claims. However, American companies have yet to offer unique plans for customers who own vehicles equipped with an automation system, thereby resulting in economic inefficiency. This thesis suggests various insurance schemes that are designed to insure automated vehicles in an economically efficient manner. First, this thesis provides an overview of the automobile insurance industry and an overview of automated vehicles. Afterwards, the dilemma automated vehicles pose to current liability laws is also examined. This is because the legal treatment of vehicles equipped with automation systems will have a significant effect on how they are insured. Next, before offering suggested insurance schemes, the results of a preliminary analysis on the effect of automation systems on insurance claims are examined. The thesis concludes with a theoretical analysis of the impact automation systems will have on various sectors of the economy once they achieve greater market penetration. - iv -

5 TABLE OF CONTENTS Introduction... 1 Chapter 1: Current Automobile Insurance Schemes Basic Monthly Premium 1.2 Pay- As- You- Drive Chapter 2: Probability Models Claim 2.2 Claim Chapter 3: The Legal Dilemma Posed By Automated Vehicles Common Carrier Laws Applied to Automated Vehicles Implications for the Insurance Industry Implications for the Market 3.2 Product Liability Laws Applied to Automated Vehicles Implications for the Insurance Industry Implications for the Market 3.3 Risk- Utility Test Applied to Automated Vehicles Implications for the Insurance Industry Implications for the Market 3.4 Cost of Litigation 3.5 Conclusion Chapter 4: HLDI Research Study Of Crash Avoidance Systems Acura 4.2 Buick 4.3 Mazda 4.4 Mercedes- Benz 4.5 Volvo 4.6 Volvo City Safety Chapter 5: HLDI Research Study Results categorized by Crash Avoidance System Blind Spot Assistance Systems 5.2 Curve Illumination Systems 5.3 Lane Departure Warning Systems 5.4 Forward Collision Warning Systems without Automated Braking 5.5 Forward Collision Warnings Systems with Automated Braking 5.6 Parking Assistance Systems 5.7 Conclusion Chapter 6: Examination of the Availability of Forward Collision Warning System Availability to Consumers 6.2 Manufacturer s Commitment to Offering the Technology - v -

6 Chapter 7: Suggested Insurance Schemes for Vehicles Equipped with Automation Technology Level 0 Automation Technology 7.2 Level 1 Automation Technology 7.3 Level 2 Automation Technology 7.4 Level 3 Automation Technology 7.5 Level 4 Automation Technology Chapter 8: Financial Implications of Passenger Vehicles Equipped with Automation Technology Chapter 9: Conclusion Limitations Technology Legal 9.2 Final Conclusions 9.3 Suggestions For Further Research Chapter 10: Literary Review Bibliography vi -

7 Introduction The notion of cars driving themselves is not a new idea. For sometime, people have fantasized about cars that could drive without human input. The advent of autonomous cars would not only revolutionize the way people travel, but would also revolutionize many industries, resulting in a drastic impact on the global economy. Some of the industries that would be affected include, but are not limited to, car manufacturing, transportation services, and the automotive insurance sector. This paper will focus on the automotive insurance industry. Specifically, it will focus on how to price insurance for autonomous cars. Before one considers how to price insurance plans for autonomous cars it is important to first understand the current progress of the technology and the legal implications of introducing cars that drive without human input. It is also beneficial to consider how insurance companies currently price insurance plans, because aspects of the current pricing method can be used when pricing plans for autonomous cars. Before continuing, it is important to realize that there are varying levels of automation. Thus, the National Highway Traffic Safety Administration (NHTSA) has released a report detailing what defines these levels. The first level is 0. At this level, according to NHTSA (2013), drivers are in complete control of the vehicle and there is no automation (p.5). At level 1, NHTSA (2013) states that the vehicle has functionspecific automation (p.5). In the report, NHTSA mentions some examples of functions at this level such as stability control, cruise control, automatic braking, and lane keeping (p.5). At level 1, the automation technology is classified as driver assistance technology

8 Therefore, the driver is to be in control of the vehicle and maintain full attention while driving. NHTSA asserts that at this level, the driver is not to have both hands off of the wheel and feet off of the pedals at the same time (NHTSA, 2013, p. 5). Level 2 automation involves automation of at least two primary control functions designed to work in unison to relieve the driver of control of those functions. (NHTSA, 2013, p. 5) Thus, at level 2, the driver is able to have both hands off the wheel, feet off of the pedals, and cede control of primary driving functions to the vehicle (NHTSA, 2013, p.5). However, the driver is to maintain alertness and must be ready to take immediate control of the vehicle if necessary (NHTSA, 2013, p.5). Level 3 automation is characterized by vehicles with the capability to enable the driver to cede full control of all safety-critical functions under certain traffic or environmental conditions and in those conditions, to rely heavily on the vehicle to monitor for changes in those conditions requiring transition back to the driver. (NHTSA, 2013, p.5) As in the case of level 2, the driver is to be available for taking control of the vehicle, but a key difference is that the driver has to have a sufficiently comfortable transition time (NHTSA, 2013, p. 5). To achieve this transition time, the vehicle alerts the driver when it is no longer capable of using automation to drive. As NHTSA states, the driver is not expected to monitor the roadway with the same diligence when operating a vehicle with level 3 automation as he or she is expected to use when operating a vehicle with level 2 automation (NHTSA, 2013, p.5). Level 4 is characterized by full automation (NHTSA, 2013, p.5). The vehicle is expected to carry out all aspects of driving for an entire trip whether a driver is present in the vehicle or not (NHTSA, 2013, p. 5). A vehicle system with level 4 automation technology is treated as a system that replaces a human driver. A vehicle with level 4-2 -

9 automation is not expected to drive in circumstances that are not suitable for human drivers, such as severe weather conditions. The goal of autonomous cars is to have these cars drive as if there was human input from the best possible human driver. Therefore, the car would minimize the possibility of having an accident in all situations and would maximize other aspects of driving, such as fuel efficiency. In order to do so, the cars must be able to perceive the world around them as humans do. Thus, the cars will need to be aware of their surroundings, the road conditions, traffic lights, lane markings, and so on. To achieve this objective, autonomous cars use a combination of sensors that include cameras, radar, GPS, and LIDAR to gather the necessary information from the world around them and to use that information to see what is needed to drive in a safe manner. The information collected from the sensors is then sent to computers on board the car to be analyzed by algorithms written to understand the information. Once the information is analyzed, commands are sent to the systems that control the steering, braking, and acceleration of the car so it can operate in the proper manner. For example, a camera mounted onboard the car captures an image of the lane markings on either side of the car, while radar detects an object moving in front of the car. The algorithms recognize the image captured by the camera as lane markings, as another algorithm determines that the object moving in front of the car can be classified as another vehicle. As a result, commands are sent to the system controlling the steering so that the steering wheel moves in a manner that allows the car to remain in the lane. Also, a command is sent to the systems controlling the acceleration of the car, directing it to accelerate enough to keep a safe following distance from the car in front of it. This example is clearly a simplification of the process - 3 -

10 involved. However, it is designed to capture the main idea of one of the key capabilities needed for the autonomous car to function properly. The process of interpreting the information collected by the sensors is very technical in nature and as such, is not the focus of this paper. The success of autonomous cars is only possible through seamless connections among the hardware sensors, the data they collect, and the sophisticated algorithms needed to process that data, determine the appropriate action, and respond properly. The unique hardware necessary for autonomous cars includes sensors to collect information about the surrounding world, along with systems to control basic driving actions, such as acceleration, braking, and steering. Currently, the sensors being used by researchers in this field are a combination of radar, GPS, and LIDAR. Different companies use different combinations of these sensors, and some companies have attempted to forgo using LIDAR. The algorithms that are used to process the information can be grouped into four main categories. These categories are: lane detection, object detection, road analysis, and control of systems involved in basic driving actions. The details of the algorithms and the mathematics involved are beyond the scope of this paper. Consequently, the paper makes the assumption that the algorithms used by the different car companies to program for automation are uniform in their functionality and reliability. This assumption aids in reducing the complexity of pricing the insurance plans, for without this assumption one would need to take into consideration the different limitations of each algorithm used, which may affect the performance of the automated function. At the time this thesis was written, car companies and other players in the field have made significant progress in developing reliable level 4 automated cars. The rapid - 4 -

11 innovation in this technical domain could arguably be traced to the 2005 DARPA Grand Challenge. Since then, automobile manufacturers and other companies have been working at a feverish pace to develop this technology, so that it can be introduced to the public. To aid in the maturation of this technology, select states have legalized testing automated cars on public roadways, after the technology has proven to work reliably on closed test tracks. Google has been the most successful company to date. Google s fleet of cars has driven over 300,000 miles while the technology was operating the vehicles and has yet to experience an accident (Murray, 2012). Google is thus seen as a leader in the effort to achieve level 4 automation. Nevertheless, alongside Google, car manufacturers are working towards generating level 4 automation. Car manufacturers differ from Google in that they offer lower level automation systems as driver assistance options on existing vehicles. Mercedes Benz exemplifies these options in the additional safety features available on new 2014 S and E class sedans. Mercedes Benz has been the leader amongst automotive manufacturers in developing and offering automation technology, but other manufacturers have begun to follow suit. When this research was collected, there were at least 24 car manufacturers that offered at least level 1 automation technology as an option for consumers. The addition of these features has a two-fold impact on automobile insurance. First, it immediately increases the expected safety of each vehicle and thus reduces the expected frequency and severity of claims for any vehicle owner with this technology in place. After there is more market saturation and thus, more data is collected, the insurance industry will be better able to understand the impact of automation systems and consequently, will be able to improve models for vehicles equipped with automation technology. Secondly, this addition allows car - 5 -

12 manufacturers to amass data on the performance of their automation technology, using lower level systems as a baseline point of comparison in preparation for vehicles equipped with higher levels of automation systems. Looking to the future, successful implementation of levels 3 and 4 automation technology will most likely involve the abandonment of LIDAR. LIDAR has been extremely useful and performs very well. However, it is doubtful its success will be implemented in any role greater than as equipment used for testing. While LIDAR is extremely successful and reliable, it is also prohibitively expensive. Currently, the LIDAR Google uses on its fleet of automated cars is approximately $70,000 per unit (Priddle &Woodyard, 2012). In most cases this one piece of equipment is more than the MSRP of current mid range vehicles, and in some cases it is more than the base MSRP of luxury vehicles. Unless there is a severe drop in the cost of LIDAR, it would not be cost effective for car manufacturers to equip onto vehicles. The cost is too high to warrant the technology as an option for consumers, because the cost prevents high market penetration. Because cameras, GPS, and radar units are inexpensive, it is predicted that a combination of these technologies will be used for vehicle systems offered to the public with levels 3 and 4 automation. This thesis will examine the impact automation technology will have on the automobile insurance industry. Automobile insurance has been driver-centric. Insurance companies use historical data to assess the risk of insuring a particular driver. With the advent of automation technology, automobile insurance will have to shift focus away from a predominant emphasis on individual driver performance to an increased emphasis on vehicular performance. Because the importance of the driver compared to the vehicle - 6 -

13 varies amongst automation levels, vehicles equipped with different levels will need to be treated differently by insurance companies. As such, the models used to insure the owner of the vehicle will have to vary accordingly. Level 0 is driven purely by a human driver and thus should have a purely driver-centric model; Level 4 is purely driven by the vehicle and therefore should have vehicle-centric model; and since the driving operations are performed by a combination of the human driver and the vehicle in levels 1 through 3, these models should have a corresponding mixture of driver and vehicle elements. This thesis will examine different methods of insuring vehicles of each level type. In addition, the legal issues automation technology has caused will be discussed. How vehicles with automation technology are treated in the legal system will have a direct impact on how the automobile industry insures them. Finally, to conclude, this thesis will offer suggestions for future research

14 Chapter 1 Current Automobile Insurance Schemes The objective of insurance companies is to maximize expected profits, while minimizing expected losses. To do so, insurance companies pool policyholders with similar characteristics into groups, or risk classes. Each broad risk class is then further subdivided so as to determine a base rate that accurately reflects the level of risk the company is assuming by insuring a member of that group. With regard to automobile insurance, these divisions are made on the basis of, but are not limited to, age, gender, marital status, type of vehicle owned, and zip code. The company then uses information unique to the consumer, such as previous driving record, to determine the specific rate for that policyholder. Insurance companies charge premiums whose total expected sum for the risk pool will be greater than its expected sum of claim payments (Anderson & Brown, 2005, p.2). This differential is achieved by using historical statistics to predict both the estimated number of claims and the expected severity of each claim for the pool. If an insurance company deems a driver to be too high risk, they reserve the right to refuse to voluntarily insure him or her. Insurance companies have adopted various pricing schemes to maximize their expected return. After examining the current schemes used, this paper will address whether a variation of the available schemes should be applied to autonomous cars

15 1.1 Basic Monthly Premium The most basic pricing plan is a monthly premium. Insurance companies take the information they have gathered and determine a monthly price to be paid by customers that maximizes their expected profit. By looking at historical data of drivers with similar characteristics, insurance companies predict the expected cost associated with each new customer. Examples of the descriptive information used by insurance companies when determining the premium to be charged include the applicant s gender, age, marital status, zip code, type of car to be insured, and driving history. The mathematical models most commonly used by insurance companies will be discussed in detail later in the paper. After the applicant supplies this information he or she then can choose from varying levels of coverage. Coverage levels range from the state minimum to the maximum amount of coverage permitted by the company. Another pricing plan is a monthly premium with a deductible. This plan requires the insurance company to only reimburse a policyholder if the cost of the accident exceeded a predetermined threshold (Anderson & Brown, 2005, p.7). If an accident occurs and its expense exceeds the threshold value, the insurance company pays the difference between the cost of the accident and the threshold (Anderson & Brown, 2005, p.7). Typically, the monthly premiums of a plan with a deductible are less than those of a comparable plan without one, thereby incentivizing some policyholders to choose a plan with a deductible (Anderson & Brown, 2005, p.7)

16 1.2 Pay-As-You-Drive A relatively new type of insurance scheme some companies are offering globally is Pay As You Drive (PAYD) insurance. In America, General Motors Acceptance Corporation Insurance is an example of a company that offers a discount for drivers who elect to choose PAYD insurance (Todd Litman, 2005, p.9). Todd Litman (2005) argued that PAYD makes vehicle insurance more actuarially accurate (Abstract). In addition, Litman (2005) asserted that, PAYD pricing is technically and economically feasible, and can provide significant benefits to motorists and society. (Abstract) Thus, PAYD offers consumers the option to have their monthly premium be variable instead of the industry standard of having it be fixed. One option for PAYD pricing is to charge a per mile premium (Todd Litman, 2005, p.1). The premium would be calculated in conjunction with the current risk class assessment used by all automobile insurance companies. This pricing method therefore improves actuarial accuracy because it uses previous data alongside data of how often the vehicle is driven per month (Todd Litman, 2005, p.1). However, a more actuarially accurate option is to use incentive based pricing. Automobile insurance companies would install a device on a policyholder s vehicle that records when the vehicle is driven, the time of day, the miles driven, and driving characteristics such as hard braking and quick accelerations (Iqbal & Lim, 2006, p.5). In fact, Progressive Insurance has experimented with this mode of pricing in the United States by offering their product TripSense to consumers (Iqbal & Lim, 2006, p.3). This method is more actuarially accurate than a per mile method because there is additional information about both the driver and the driving conditions. The recorded speed and the

17 frequency of hard braking and quick accelerations gives insight into whether the driver tends to be more aggressive or defensive. The record on the number of miles driven and the time of day reveal how frequent and how long the driver is in conditions that are known to be of high risk for an accident. The most actuarial accurate option is to use GPS based pricing (Todd Litman, p.1). To achieve this option, automobile insurance companies would either use the GPS system already installed on the vehicle or a GPS unit if the vehicle does not have one already. The use of GPS technology allows insurance corporations to use data on how many miles were driven, where they were driven, and a what time of day they were driven in conjunction with driver data to determine the monthly insurance premium. The new data provided by GPS technology is hugely important, because the risk of an automobile accident is dynamic and changes depending on the multitudinous factors for which the GPS technology provides information. Those factors include whether the vehicle is being driven in an urban or suburban area, at what time of day the vehicle is driven, and how often a vehicle is driven. If a customer elects this option then the insurance company installs a device that monitors the number of miles driven per month. The user then pays a premium on a per mile basis. Some insurance companies have taken this concept even further and offer discounts based on how the customer drives. By collecting additional data such as where one drives, the average driving speed, and the amount of braking force applied, insurance companies can further determine the risk associated with the specific customer. This additional information not only benefits insurance companies, but also drivers that elect this option. Assuming that only a safe driver would elect to have this information about

18 their driving habits collected, he or she would now be able to receive previously unavailable discounts. Thus, these safe driving discounts eliminate excess spending on behalf of the customer, while simultaneously providing additional information on which drivers are riskier than others. While Pay As You Drive insurance seems attractive, especially for low mileage drivers, there are nonetheless privacy concerns associated with it. An insurance company knowing a driver s exact location at any given time is disconcerting for many people. There are the concerns of who will have access to this information, for how long they will have access, and how securely the information will be held

19 Chapter 2 Probability Models 2.1 Claim Automobile insurance companies typically model claim frequency as a Poisson random variable. There are alternative random variables that companies sometimes use to model claim frequency, however. For instance, the negative binomial variable is an alternative that can be implemented (Boucher, Denuit, and Guillen). However, the Poisson distribution will be used in this thesis because it is the most widespread approach to model claim frequency. In addition, the analyses done by the HLDI referenced in chapters 5 and 6 use the Poisson distribution. The equations below define the probability mass function, the expection, and the variance for a Poisson random variable X.!!!! p i = P X = i = e!! E X = λ Var X = λ, i = 0,1, λ > 0 From the equations, it is evident that the accuracy of λ is essential for the insurance companies because it represents both the expected value and variance of the claim frequency. An inaccurate λ could potentially have severe consequences for the profitability of the company. As a result, insurance companies implement sophisticated

20 methods to calculate λ. As stated previously, driver characteristics such as the driver s age, gender, marital status, ZIP code, type of vehicle owned, and previous accident record are used to group drivers of similar characteristics into risk pools. These driver characteristics are then used as explanatory variables in a regression analysis conducted with historical accident data to estimate the appropriate λ for each risk pool of drivers. The complexity and type of regression model implemented varies, and the details of the various types commonly implemented are beyond the scope of this thesis. As a result, the regression model used by the insurance companies is assumed to be the best option available, as the focus of this thesis is not the effectiveness of different regression models, but rather the effectiveness of insurance schemes as applied to automated vehicles. Thus, the research explores the effectiveness of the explanatory variables used in the regression. 2.2 Claim The other component of determining the insurance plan is predicting the accident severity. There is a vast amount of data on automobile accident severity; fortunately, this data is applicable to all levels of automation. The best approach in assessing accident severity has yet to be determined, but modeling it as a Gamma random variable appears to be best available option. In fact, HLDI used a Gamma distribution when they modeled accident severity in their studies on accident avoidance technology (HLDI, 2011). The Gamma random variable X has parameters (α, λ). The density, expectation, and variance of X are given in the equations below

21 f x = λe!!" λx!!! Γ α 0 x < E X = α λ Var X = α λ! To increase the accuracy of the model, there should be distinctions based on location. For example, it is suggested that there be a variable for vehicles registered in suburban areas and one for those registered in urban area because in urban locations one expects to see multiple accidents that are small in financial severity. However, for suburban locations, one anticipates a small number of accidents that are more financially severe. This pattern occurs because the density of vehicles is higher in urban locales while speed limits and congestion prevent the car from reaching high velocities. In contrast, suburban locations experience less vehicle density, and the speed limits are higher. This combination, on average, permits cars to drive faster than vehicles in urban locations; the increase in speed increases the accident severity. While this assertion seems plausible more research should be done to validate it. Insurance companies may also wish to consider separating the predicted accident severity for vehicles with automation technology from those without it. It is suggested to take this measure if corporations see a noticeable trend in the difference of the average severity for vehicles with automation technology compared to the average for those without it. It may be that on average, vehicles with automation technology do not have minor accidents, but tend to have accidents that are very severe. The trend to have severe accidents could be the result of accidents that only result in a catastrophic failure in either

22 the hardware or software. This theoretical phenomenon can only be substantiated after the technology is introduced to the public and sufficient data is gathered. Companies may also consider classifying accident severity by vehicle manufacturer and vehicle type. For example, foreign vehicles are typically more costly to repair compared to their domestic counterparts. In addition, luxury vehicles are typically constructed with more expensive parts and are therefore more expensive to repair after an accident. Insurance companies may also wish to take into account the cost and location of the equipped automation technology when estimating the expected average accident severity. It is highly unlikely that the LIDAR Google is currently using will be implemented in vehicles offered to the public. But, if it were to be used, the LIDAR system costs approximately $70,000 (Priddle & Woodyard, 2012). Clearly, this cost will need to be taken into consideration when predicting accident severity. The location of the technology is also important because if, for instance, it is known that a rear end collision is the most prevalent accident in an urban area and that the vehicle is equipped with expensive sensors on the rear bumper, it is likely that on average when the vehicle is in a collision the sensor will be damaged and need replacement