Research Related to Autonomous Vehicles

Ever since the beginning of the Information Age, job automation has been gradually working its way into the everyday workplace. Job automation is the replacement of jobs traditionally done by humans by automatic systems, namely computers or robots. It is most commonly seen in industrial fields where the introduction of automation has massively improved speed and efficiency in factories, however, it generally comes with a significant loss in jobs, especially in more developed countries. According to Christopher Pissarides, depending on a country’s level of development, machines could replace anywhere from 3 to 14 percent of jobs by the year 2030. A significant player in this job loss could be the introduction of autonomous vehicles to the workforce, replacing most of the 1.7 million trucking and transportation jobs in America(Freedman). Autonomous vehicles (AVs), more commonly known as self-driving cars, vehicles that are able to drive themselves without human intervention.

By most definitions, the level of autonomy is graded in “levels” from zero to five, with a level zero vehicle lacking any automation and a level five being fully autonomous in all situations (Schoettle). Currently, level two autonomy is the only relatively common form of automation in production cars, with most car companies having some form of Automated Driving Assistance Systems (ADAS) in their vehicle models (O’Toole). These features are typically situational convenience or safety features such as “parallel park assist” or “automatic braking systems”. The development of automated vehicles is progressing extremely rapidly, however, as the first “level 3” production vehicle is set to be released by Audi in 2019, with Toyota and BMW set to release similar products in the two years following. AV technology has been evolving extremely rapidly in the past decade, but the idea of driverless vehicles has been around for nearly a century.

The first example of an AV was Francis Houndina’s “radio controlled vehicle”, which he demonstrated in 1925. The idea was not reintroduced in any major way, until the Defense Advanced Research Projects Agency (DARPA) announced its “Grand Challenge” in 2002 for researchers to build an autonomous vehicle that could navigate its way through the Mojave Desert. When the competition began in 2004, the “winning” entry set on fire after multiple hours and just eight miles of driving. Despite the failure, research has gained remarkable traction in recent years with millions of miles being driven by AVs by leaders in the industry such as Alphabet owned Waymo, Uber, and the GM owned Cruise (Dormehl). While partially for the benefit of technological advancement, there are a variety of practical reasons for the implementation of AVs. The largest and most obvious of these reasons is the potential to save lives by the reduction of traffic accidents.

Over 37,000 traffic deaths occur each year in the United States, with over 90% of them being attributed to human choice or error (Singer). Even if the best case scenario for AVs were a 10% reduction in crash rates, the potential to save countless lives in the long term makes the development of AVs priceless. On top of saving lives, the development of AVs will result in a sizable reduction of energy consumption through techniques such as “eco-driving” and “platooning” that are impossible for human drivers (Wadud). Mobility is also a huge boon to the development of AVs, as they could grand newfound ability for the disabled, physically impaired, and elderly to safely transport without the assistance of others. As a result of these numerous advantages over traditional vehicles and other forms of transportation, the implementation of AVs is inevitable, and an effort should be made to speed their implementation rather than wait for “perfect” vehicles.

With the majority of traffic accidents caused by human error, driverless vehicles are bound to improve safety for all users of the road. Due to the lack of long-term implementation of AVs on public roads, exact figures on the effectiveness of autonomous technology compared to human drivers do not exist. Nevertheless, the National Highway Traffic Safety Administration (NHTSA) stated that the introduction of Tesla “Autopilot” systems in their Model S and X vehicles reduced crash rates by 40% (Hawkings), pointing out that current technology is heading in the right direction. The speed of implementation for AVs will also be a major factor in saving lives. According to research done by the RAND Corporation, by beginning to implement AVs when they are 10% more safe than human drivers could save up to 200,000 lives in the time that it would take for AVs to become 90% safer than human drivers. Actual numbers may vary depending on the date of implementation, the extent of usage, along with the amount of unpredictable non-AV fatality rates.

For example, fatality rates were steadily decreasing from the 1950s onward, yet, the last decade has seen a plateau, followed by a 5.4% increase in fatalities from 2015-16. Regardless of the numerous variables, essentially every simulation run in the RAND study resulted in significantly more lives saved in the long term by immediately implementing AVs at a 10% increase in efficiency (Kalra). Even so, the general population may be understandably resistant to the implementation of AVs at a 10% increase as personal control is exchanged for the “greater good’ (Bonnefon). Furthermore, many studies have found that there is almost no tolerance for errors made by machine systems in comparison to their human counterparts. While it is important to compromise with public outcry, waiting for nearly perfect driverless technology could cost the deaths of thousands in motor vehicle accidents.

Playing an equally important role in the implementation of AVs is the increased efficiency these vehicles would grant. Congestion in massive urban areas such as Seattle (where only 11% of vehicles in the city are in use at any given time) would be heavily reduced. Small gains in efficiency could also be gained through minor applications such as errand running or use of transit times for productive activities, however, major applications may include the automation of semi trucks, ride sharing, and granting extra mobility to the elderly, disabled, and children (Alessandrini). Commercial use of this technology would result in noticeable economic gain, through the potential reduction of freight cost and time (Freedman). According to a study by the Earth Institute at Columbia University, ride sharing encouraged by the introduction of AVs in urban areas could reduce the amount of cars on the road by up to 50% (Radwha).

By reducing this many vehicles on highways, the mitigated congestion would result in a nearly 3% increase in fuel efficiency (Wadud). Even without accounting for potential increases in ride sharing, AVs are able to engage in many actions that are impossible for human drivers that could help aid in the reduction of over congestion and fuel emissions. One such ability is being able to safely drive much closer in proximity to each other than human drivers (O’Toole). This ability could also be used in “platooning”, or the coordinated use of close driving to increase aerodynamic efficiency. Energy use on light weight vehicles engaging in platooning could decrease anywhere from 3-25% on the freeway, depending on the car’s location in the “platoon”. Gains for semi trucks are just as substantial, reducing energy use from 10-25%. Another potential technique that could be used by AVs is “eco-driving” or the operation of a vehicle engine at its most efficient points. The resulting effects on emissions through eco-driving vary wildly with the situation, however, a noticeable reduction would likely take place over time (Wadud).

In addition to these techniques, AVs would likely have less “downtime” in their operation due to their more efficient navigation in situations such as parking. Combining with the AV exclusive techniques, most automated vehicles would be lighter and more fuel efficient vehicles, further contributing to the reduction of fuel related emissions (Alessandrini). The implementation of AVs would immediately significant reductions in vehicle emissions and congestion.One of the many advantages of AVs over other forms of “modern” public transport is its widespread availability. A common proposed alternative to the development of AVs in urban areas is the expansion of public transport, however AVs hold many advantages over these methods. In comparison to services such as buses or rail lines, vehicles in general are much more flexible in where and when they go. This is especially important in areas that are not incredibly urbanized, where public transport is virtually nonexistent. In comparison, more than 95% of US households have access to a personal transport vehicle.

Of those who do not own a vehicle, nearly a quarter of them still transport to work in employer borrowed vehicles. Public transit also suffers from a massive upfront cost. In 2012 alone, taxpayers spent $24 Billion on public transit services. In addition, transit receives three dollars in government subsidies for each dollar spent in fares. In 2012, only five percent of urban workers rode transit to their jobs. In addition, the average urban resident rides the transit an average of 44 times per year. Regardless, proponents of public transit push the lower per mile cost. In 2012, transit fares averaged out to $0.25 per mile compared to the $0.37 per mile of personal vehicles. However, after taking into account ride sharing between two or more people, personal cost drops significantly below that of transit.

Finally, advocates of public transit cite the large increase in safety that public transit provides as an advantage over traditional vehicles (O’Toole). In 2016, commuters reduced their crash risk by 90% in comparison to personal vehicles (Williams). However, those numbers are comparable to the likely reductions made by AVs in the future (Filler). Due to its lack of significant use, large upfront cost, and the objective inconvenience, support for public transit should instead be shifted to support of AVs in urban areas as the transportation of the future. An additional proposed alternative to widespread AV use are “smart highways”. Smart highways are roadways that contain terminals capable of relaying information to and/or guiding automotive vehicles. In order for vehicles to use this technology, they would require hardware that would allow them to use vehicle-to-infrastructure (V2I) features. Proponents of this technology state that the addition of these features, along with vehicle-to-vehicle communication (V2V) could remove the need for fully autonomous sensors and other hardware, as all driving tasks would be controlled by the smart highways themselves. In addition, all vehicles in an area would instantly respond to the actions of other vehicles through V2V technology. This idea comes with a plethora of issues, however, namely the increased susceptibility to abuse and hacking.

In a smart highway using this technology, a security breach any piece of infrastructure manipulate entire roadways and all the vehicles present on it. This large increase in risk comes with essentially no benefit, as in most cases, ADAS systems are just as effective as V2V and V2I systems. Smart highways also come with the requirement of constant and expensive government maintenance, which is unlikely to be present in all cases, as seen in the poor conditions of many current roadways. This also limits autonomous driving to roadways equipped with this infrastructure, which severely limits large scale improvements in driving safety. Smart highways pose many risks for almost no benefit over AVs, making them a poor alternative improving roadway safety. (O’Toole)The largest blockade to the implementation of driverless vehicles are the recent deaths occurring involving AVs. The first fatal crash involving a driverless vehicle occurred on May 7, 2016 when a Tesla Model S operating in “Autopilot” attempted to drive under the bottom of a semi trailer (Levin).

Tesla responded to the crash in an open letter which stated, “This is the first known fatality in just over 130 million miles where Autopilot was activated. Among all vehicles in the US, there is a fatality every 94 million miles.”. Additionally, at the time of the crash, the driver was distracted by watching a movie despite the “Autopilot” system explicitly stating that the driver’s attention must be focused on the road at all times while the system is in operation (Levin). More recently, the first pedestrian death occurred on March 18, 2018 when a self-driving Uber hit a pedestrian at 10 pm while walking her bike across the street in Tempe, Arizona. The pedestrian crossed the middle of the poorly lit road while wearing a black coat without making an attempt to avoid or acknowledge the vehicle. With these conditions, it is incredibly unlikely that any human driver would have been able to do a better job in avoiding the collision (Griggs).

Even disregarding this, national averages suggest that over 100 human deaths occurred on the same day, and four deaths occurred in the same hour (IIHS). Despite these factors, Arizona legislature has indefinitely suspended Uber’s ability to test self-driving vehicles in the state. By limiting the opportunities for leaders in the industry to test AVs, the pace of development drastically slows simply to ease invalid concerns. Due to AVs numerous advantages over traditional vehicles, it is inevitable that they will eventually replace human drivers. Furthermore, by waiting to implement these vehicles until they are nearly perfect verses gradually implementing as technology improves will cost thousands of lives. Along with this, AVs will be a significant help in decreasing congestion in heavily populated areas, decreasing fuel emissions, and providing extra mobility to the physically impaired. As safety technologies continue to improve, AVs will further grow its personal appeal and cost efficiency over forms of public transit. It is unavoidable that driverless technology will hit many costly road bumps on its way to implementation, however, the costs of waiting are far greater.

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