After launching its first driverless fleet in Pittsburgh last year, Uber has been a hot topic for the mainstream media. Generating a lot of buzz amongst the business and technology environment while also being widely discussed by the research community, this innovative technology will most likely shape how we perceive and interact with transportation aspects within the next couple of years. The company has a very challenging project in its hands and has invested a tremendous amount of research and development in what seems to be one of the biggest game changing business plans thus far. 

The self-driving fleet project is Uber’s first big step on its plan to replace its one million-plus drivers with autonomous cars, providing customers rides in robotic taxis with completely automatized systems. The company is hoping to tap into a completely unexplored market (often called “blue oceans” by the business community), but has had mixed results thus far from its experimental research stage in Pittsburgh. Customers seem to be extremely averse to this new modern technology and the program itself is still facing numerous structural issues. The discussion at this point is on whether the technology is able to generate a better overall transportation system without completely disrupting the environment in which it is being created. The feasibility of the project challenges our capacity as human beings and whether we are able to fully control and interact in a positive aspect with artificial intelligence. Understanding how to control and operate autonomous cars as well as how to regulate and mitigate their impact over our society is a key component to successfully transition into the modern era of futuristic transportation.

The question at this point is if Uber´s innovative technology will be able to successfully provide benefits to our modern society. It is important to discuss and analyze all aspects of the project and what sort of externalities the autonomous vehicle technology will have on the transportation environment in big cities. The main focus is to understand how driverless cars could potentially optimize the transport system currently dominated by regular drivers and how this advanced artificial intelligence is able to reduce risks of accidents, make traveling less expensive and reduce traffic delays in big cities.

Uber itself started its operations as a small tech startup in early 2009. Based in San Francisco, California, the company was founded by Travis Kalanick and Garret Camp, launching its first prototype beta app in early 2010. After a few months of market research and optimizing the product and its backend logistics system, Uber’s store mobile app officially launched in 2011, focusing initially on its home-market San Francisco and New York, Americas’ biggest city and therefore the logical first step for the business to hit a critical growth rate. With the help of two funding rounds that raised little less than $50 million dollars in direct investment throughout the year, the company was able to achieve its first international expansion by December 2011, starting by expanding into Paris, France, and a few months later into London, United Kingdom.

As of now, the company already operates worldwide. Offering transportation services in more than 70 countries, the phone app allows smartphone users to request rides in almost every single city, connecting a constantly changing dynamic demand with drivers who provide their personal cars to offer lifts. The firm developed a highly sophisticated algorithm that maximizes the connection between customers and drivers, while also optimizing routes for the available cars and updating prices in real time based on supply and demand models. Needless to say, the company has had astronomic success amongst a now – more than ever – hyperconnected internet-dependent generation, and is worth billions of dollars as of today. Uber is considered the number one company in the industry, holding the biggest share of the transportation service market worldwide, while also holding the title of the largest “Unicorn company” in the world – a term used to define private businesses with post-money valuations of $1 billion dollars or more, based on Crunchbase data. It offers a variety of services to accommodate its growing customer base, and has recently started an expansion of its services, offering innovative solutions such as UberRush, UberPool and UberEats, while also allocating a great amount of research & development efforts into a new, completely untapped market: The Self- Driving Uber Rides.

The industry in which Uber’s driverless cars will operate could be defined as the public transportation industry and global car-service market, with special emphasis on taxi-based services and ridesharing. Since the autonomous vehicle relies on a complex technological background, this technology is bound to offer its services mainly in cities that satisfy the needs of its Artificial Intelligence. Therefore, smaller cities and rural areas are unlikely to receive this type of transportation in the near future, with its main focus being metropolitan environments. 

The project currently being developed by Uber aims to create a disruptive platform to develop a steady demand for global taxi services, fundamentally changing inner-city transit flows and dominating local operators with its expanding reach. With the product already being launched and semi-manually operated in big “test cities” such as Pittsburgh and San Francisco, the driverless car is expected to assume the entirety of Uber’s target market in the next few years, providing an innovative tool to reduce costs and improve overall safety for its customers.

The self- driving car idea itself was announced by the company at the beginning of 2016, and it was put in practice later in the same year. According to its researchers, the car was designed to maximize the user’s experience and engage customers into easier and safer transportation, given that a high amount of traffic accidents is directly correlated to human mistakes. The driverless cars are therefore vehicles that interact directly with Uber’s app and a series of different autonomous systems, operating through the detection of surroundings by using a variety of highly advanced artificial intelligence techniques.

While Uber is the behemoth of the transportation industry, it’s worth noticing that there are many other companies that provide similar services and are willing to follow the driverless-car innovation trend. Currently, Uber’s biggest competitor in the regular taxi service industry is Lyft. The company is expected to follow Uber’s driverless car project and already started researching its own AI system to offer a similar service by the end of 2017. As of 2016, Lyft had already partnered up with several strategic partners such as Waymo (Google’s self-driving vehicle division) and General Motors, who invested $500 million in the company in exchange for a 10 percent stake and an agreement to build a network of self- driving cars together, working strongly and actively towards cutting the edge between both companies. Now, Lyft is also set to start a strategic partnership with another giant of the car industry – Ford – showing the strength of the company as a close follow-up of Uber and the importance of the driverless market. The company works similarly to Uber, offering multiple levels of transportation services through its own smartphone app while also providing a dynamic pricing structure. And while the difference between both companies’ annual revenues, capital and market share is still significantly sharp, Lyft has been experiencing an exponential growth in a lot of key metrics, claiming considerable increases in number of unique customers and licensed drivers, and hinting towards a potential stronger competition in the years to come with higher market shares and aggressive strategies to overthrow Uber.

Regardless of the recent competition efforts, Uber is still the main company in the industry and is expected to be the pioneer of driverless cars services. The self-driving fleet project is Uber’s first big step on its plan to replace its one million-plus drivers with autonomous cars, providing customers rides in robotic taxis with completely automatized systems. Designed to maximize the user experience and engage customers into easier and safer transportation, the driverless cars are vehicles that interact directly with Uber’s app and operate through the detection of surroundings by using a variety of highly advanced artificial intelligence techniques such as radars, lidars, GPS, odometry, and computer vision. Working over an extremely sophisticated platform developed by talent from Carnegie Mellon University’s Robotics Institute, as well as from the National Robotics Engineering Center 3 , the autonomous taxi fleet generates a whole new dimension to modern transportation systems and produces a feasible solution to Uber’s groundbreaking labor disputes. The success of this artificial intelligence obviously relies on numerous aspects and advances, but a fundamental role of the model derives from the company’s ability to interconnect and use the vast number of autonomous agents incorporated in the ultra-complex logistics system developed throughout the last couple of years. Thanks to Uber’s core tracking system, the company is able to recreate an intelligent decentralized collective behavior that emerges when several technologies work as a unit, generating a swarm intelligence strong enough to forecast the most complex problems that the systems alone could not manage by themselves (Neapolitan and Xia, 297).

Developing this core technology, vehicles, and all of its associated infrastructure in Pittsburgh, the company deployed its first 100 modified Volvo XC90s outfitted with self-driving equipment to operate in its Pittsburgh taxi fleet by the end of 2016. Each vehicle is still being staffed by one engineer who can take the wheel as or when needed (given that the machine learning technology behind the project is still in a “learning” phase and therefore prone to make a few calculation errors), and a co-pilot to observe and take notes in the initial phases of the self-driving fleet project. As mentioned before, the technology implemented by Uber’s fleet is believed to be the foundation of a series of innovative solutions across the transportation industry with capability of reducing the number and severity of traffic collisions caused by human-driver errors (such as delayed reaction time, tailgating, and other forms of distracted or aggressive driving), serving as a milestone for potential improvements traffic flow management efforts, better safety conditions and reduction in costs of traffic police, vehicle insurances or road signage.

The benefits of this solutions could therefore have a major impact over our society, if used properly. The technology’s reduction of human-driver errors could have a significant improvement of highway safety and reduce an estimated $625 billion dollars fatalities and injuries’ cost across the globe; alleviate heavy traffic congestion in big cities through the implementation of Intelligent Traffic Systems synchronized to the driverless car technology; and reduce air pollution by providing greater options of ride sharing services such as the Uber taxi service itself (West, 9). Reductions on traveling costs are also a direct effect of this new solution. It has been empirically proven that autonomous vehicle systems can enhance the efficiency of our current transportation main transportation method (conventional vehicles), reducing both travel and social costs significantly (Chen et. al, 57). 

On a broader spectrum, if implemented correctly, the technology would also allow a quick adaptation of our society’s transportation methods and change dramatically the way we live in big cities. It tackles not only inefficiencies of our current ground traveling system within overpopulated environments, but also adapts to our evolution as a society in general. For example, without the need of driving, disabled and retired citizens – a population percentage that has been on a steady increase with all the medical advances our society has been experiencing in the recent past – would perceive a significant improvement on their transportation possibilities based on physical and/or visual limitations (West, 8).

Yet, those solutions could also expose a series of new challenges to our system, generating problems that could hinder the potential benefits of the driverless car technology. The use of artificial intelligence and neural networks in autonomous cars is an extremely hard challenge for our current legal systems and present extremely hard problematics such as product liability concepts, assumptions of risk and non-uniform state vehicle laws (Brodsky, 861). We do not know how to approach certain problems regarding those regulations issues simply because once most of the human errors in the transportation system are eliminated, it’s impossible to find the culprit in a highly-complex interconnected system in which multiple agents and artificial intelligent components work as a decentralized intelligence unit to generate the technology behind those cars. 

Similarly, given that the technology would rely almost exclusively on a broad network of computerized agents, the overall security of the autonomous vehicle system would be jeopardized considerably. External effects such as bad weather, poor highway structures or even inadequate spectrums of frequencies could limit and deteriorate the much needed infrastructure behind the driverless cars, with the potential of generating massive undetected and unpredictable chaos possibilities (West, 11).

If this wasn’t already enough, the autonomous vehicle technology also presents a strong challenge given our risk-adverse nature. Even if the benefits of this new technology are able to outweigh the potential problematics, “it takes a while for individuals to accept new models and different ways of navigating” (West, 13). Transparency on the accuracy of the systems behind the driverless cars is extremely important to reduce the perceived risk factors by its users, as well as providing possibilities that allow the passengers to recover control in situations whenever they so desire (Choi and Yong, 699). Overall, our behavior suggests that it is extremely hard for us to allow a computer to fully control our vehicles. Given our nature, we understand the danger of being in a car and – no matter how safe the systems are – it will always generate a triggering effect in our mind knowing that we would rely exclusively on a computer making the best decisions for us on a 60mph highway. 

Uber’s driverless car fleet is therefore a project that relies on our ability to progressively implement a disruptive technology to our society without greed. Given the fact that the core system behind the autonomous cars is extremely complex and therefore needs an extensive amount of collaborating little systems, rushing prematurely into this new generation of vehicles only to capitalize on a blue ocean market (a term used to characterize an untapped market with potential high profits) would be the only mistake that could doom the fate of this ambitious idea. The company, as well as its researchers, should be able to fully develop the machine learning background behind the autonomous car’s artificial intelligence components before launching the service to its customers. It is important that the research stages current being hold in the test-cities can conclude without being pressured by the amounting competition of Lyft, since the potential challenges to the system are very acute and dangerous. Being patient enough to await the maturation of other autonomous car projects and our intelligent transportation systems in general would allow the project to have a much lower chance of initial failure and reduce risks of potential external errors interfering with our first driverless cars. Yet, we are living in the era of economic growth. A society driven by profits is what makes our innovative jumps to be bigger each time, but it often carries with it a ricochet effect – corporate greed. 
