Uber Self-Driving Taxis’ Threats and Opportunities

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Table of Contents

The challenges and threats facing Uber self-driving taxis
An internal analysis of Uber self-driving taxis
References

The challenges and threats facing Uber self-driving taxis

The idea of the creation of an autonomous vehicle is not exactly new. Companies have been considering it since as far as the 1980s. Nowadays, many major companies are paying attention to this prospective market and are working with IT companies dedicated to the research and development of such vehicles. The partnership between Uber and Volvo is one such example (Uber to Deploy Self-Driving Cars in Pittsburgh 2016).

Are they inventing a blue ocean with their driverless taxi project? Yes and no. “Creating a blue ocean” means developing a new market space rather than trying to claw their way into an established one and face the competitive challenges. They are not the first to come to this idea. Nevertheless, so far, the market for autonomous cars is relatively empty and could still be considered a blue ocean. The reason for that is simple – none of the companies involved in similar projects have yet released a model into the auto market en masse. Many obstacles are still in the way, ranging from prices to safety to the inability of old traffic regulations to adapt to this new reality.

Volvo and Uber will face the same challenges as all other developers of autonomous cars. One of these challenges is the unwillingness of the customers to delegate control to a computer. The idea of letting a machine carry you to your destination is unsettling for many customers right now. It was the same with automatic elevators, years ago. It will take some time to build up such trust, and any major accidents involving autonomous prototypes are going to cause long-term trust issues. Another major issue that most companies are struggling with right now is making the computer understand the nonverbal cues from pedestrians and regulators. Since every person is different, their cues differ one from another. The differences make it difficult for a machine to recognize them since there are too many different patterns to the program. Some autopilots are not even capable of recognizing the pedestrians yet, and could only interact with other vehicles. The computer must be able to navigate on the road just as well as any human can.

Lastly, Volvo and Uber are going to face competition with a high-tech giant named Google, which is also developing its own cars. As it stands, the key to creating a safe and efficient autonomous car lies not in the ability actually to produce high-quality cars, but in developing the appropriate hardware and software, to make the car truly autonomous. Volvo excels in making cars. Google excels in programming and robotics. Uber is a relatively young company and does not have as much experience as Google. However, they certainly made progress in that direction by bringing former Google employees and key figures involved in the project into the field. Other competitors include Tesla and Ford. The latter collaborates with Velodyne to create an autonomous vehicle of their own (Snavely 2016). It remains to see how this competition goes.

An internal analysis of Uber self-driving taxis

The news about the Uber autonomous taxi cabs is relatively fresh, and most of the technical details are hidden from the public. Informed predictions could be made based on what we know about the project and the company. The success of such enterprises relies on several key factors.

The first factor is financial resources. Even Volvo, who had declared several years back that the development of self-driving cars is a waste in the short-term perspective, ended up being Uber’s partner in the endeavor. That means they are interested in a long-term perspective, as revenues from these cars are not likely to come at least for the next several years. It requires time and money to conduct such a project, and Volvo is willing to offer its cars, its engineers, and its money to succeed. This puts Uber on par with other competitors, as far as finances are concerned.

The second factor is professional competence. Uber used to be at a disadvantage here when compared to a massive multi-national corporation such as Google. However, the company managed to even the odds by employing former specialists from Google, Tesla, and Apple, like Anthony Levandowski, Lior Ron, and Claire Delaunay (Markman 2016). This synthesis of specialists from different companies has put Uber on par. If not above, most other competitors vying for this market, as they had the base to start building the project up instead of inventing everything from scratch.

The third factor is speed. The company that would be able to launch the first autonomous vehicle into the masses will claim the dominant seat in the market. Google has been taking its time testing out their cars. However, it did not launch a massive public testing campaign yet. Tesla has been experiencing a negative backlash due to a fatal accident involving their autonomous car in June 2016 (Vlasic 2016). It raised serious concerns regarding the safety of their autopilot system, which means that the introduction of free, autonomous taxicabs in Pittsburgh is going to be the first large public campaign of such a sort, which will no doubt attract a lot of attention (Uber to Deploy Self-Driving Cars in Pittsburgh 2016). Many citizens would likely be interested in taking a ride. Should this campaign be successful, it will propel Uber way ahead of its competitors. However, should an accident like the one with Tesla happen, it may set the company back. Uber’s autonomous cars will not remove the human factor completely. The human will remain as a supervisor and will be able to take control of the vehicle whenever he or she feels it is necessary. Uber’s approach is more catering to the current mentality of the potential customers, who would prefer to remain in control of their vehicle. Uber has every chance to succeed, it seems. It has all the tools to take the lead, and now the success or failure largely depends on how well their vehicles perform in the field.