To study if Google Self-Driving Car will be feasible in Today’s World

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Literature Review

Self-driving cars are quickly evolving technology that only a few years ago was something that people considered as science fiction.  In a context that is dynamic as such, drawing quick intuitions can be misleading as the misconceptions regarding the technology, its influence, and the characteristic of the innovation process abound. Google unveiled a new self-driving car prototype on May 27, 2014; and it became the first company to build a car that has no steering wheel, a brake pedal or an accelerator (Markoff, 2014). The arrival of this car sets a good base for the next stage of Google’s project on a self-driving car that was started as a result of Darpa Grand Challenges for the robotic vehicles in the onset of the 21st century (Miller, 2015). Google; began its self-driving car project in the year 2008 and since then it has been rumbling on first modifying the Toyota Prius and then customizing the Lexus SUVs that took the car’s sensors and added a spinning laser scanner.

The car is the first real driverless electric car prototype that was built by Google as a way of testing its next stage of the self-driving car project that had taken five good years (Brown, 2011).  That is like a cross between the smart cab and a Nissan Micra having two seats as well as a room for accommodating a small amount of luggage. That prototype car operates around California especially around the Mountain View region where the Google Company has its headquarters. It can ferry two people from place to place without involving user interaction.  That car is being summoned by a Smartphone to pick the users from a given location to some set destination. The steering wheel and manual control are absent; what is there is only a start button and an emergency stop button (Brown, 2011).  A small screen is in placement in front of the passengers to show the weather, a countdown animation to be launched, and the current speed.

There are many arguments that are coming up regarding if Google’s self-driving car can be feasible in today’s world or not.  My opinion as per the many literature reviews works I have examined is that it is feasible in today’s world even tough when a technology is coming up there can be a few issues, but with time, as they are ironed out, everything works on well as planned.  The Google self-driving car has also faced some issues that were unplanned for, but there is a possibility that those issues can be dealt with so that the project can deliver its promises (Boxwell, 2014). There are five key issues that Google should address if the project is to be successful and be feasible in today’s world.

Google did a study last year, 2015 and released data showing that the self-driven car had been involved in eleven minor crashes within a period of six years (Miller, 2015). Those findings have raised questions concerning when then autonomous vehicles will be entirely reliable.  The data shows that most of those crashes resulted from human-driven error, and they would not be preventable. Still, as some levels of automation are inclusive in some existing cars, entirely driverless cars having no brakes or wheels for the human drivers may not need much more information (Miller, 2015).  In the US alone, driving is incredibly safe as fatal crashes occur roughly after every three million hours of driving. Therefore, the driverless cars need to be safer than that. That is, however, difficult to accomplish with the current software.

The reason as to why the existing software cannot achieve the level of safety that in the requirement by the driverless cars is because there is no software in phones, laptops, and in modern devices that can operate for extended time periods without crashing, dropping a call, or freezing.  Therefore, if similar errors can occur in cars, that can be very dangerous.  The Google self-driven car avoids that using a backup driver as well as a second person who acts as a monitor to shut off the system when a hint of glitch occurs. However, developing software that is safety-critical, and a fail-safe one for completely self-driven cars will require the reimagining of how the software is designed (Villasenor, 2014).  We do not have a current process that can efficiently develop safe software.

The other things that should be addressed be addressed are the issue of having better maps so as to enable these driverless cars to operate in a seamless manner in any land terrain.  The current Google driverless cars have a virtual-world map that’s contains the street view of the Mountain View of California where they are being driven currently (Miller, 2015).  That means the car knows how the streets look, and it only needs to feel the obstacles such as the pedestrians and other cars. The Google’s self-driven car, with its processing and sensors, may not operate seamlessly minus a detailed map of all the world; although so far Google has mapped 3,220 kilometers of the 6.4 million kilometers of the roadway in the US (Anderson, 2014).  Before the drivers can toss their driving licenses, the driverless car should have the capability of distinguishing between perilous as well as harmless situations. Otherwise, it may slam on the brakes throughout without any reason.

The cars also should be capable of deciding in sufficient time if a pedestrian that is waiting on the sidewalk is probably walking into the traffic or if a bike is likely to swerve left.  The human brains perform a masterful task of differentiating and reacting to those catastrophes on the fly, unlike the present sensors that are not adequately equipped to ensure quick processing of data (Knight, 2013).  Once these self-driven cars start to proliferate, they will have to consist of a better way of communicating with other vehicles on the road. Because different situations and continually emerging, those driverless cars will have to be flexible and adjust to other vehicles on the roadways, talk to other self-driven cars and reroute on the fly (Turban, 2015).  Presently, the communication between the individual driverless cars is very minimal.

The ethical decisions are also another ting that these driverless cars need to put into consideration. Sometimes the driver has to make a decision of whether to swerve left or right or to injure a pedestrian or a person potentially on a bike (Currie, 2016). These ethical dilemmas need software in the driverless car to weigh all the possibilities and various outcomes and then come up with the final solution on their own.  A machine with that capability is, however, unprecedented in human history.  Even the drones that are used to target enemies in a battle are usually remotely controlled by humans that have a final say regarding the killing.


The research on the study that decides if the Google self-driven car is feasible in the current world will follow the Action Research process. That is because the AR methodology is the only one that is sure of bringing about positive results altogether. The research will follow that methodology whereby there will be five iterations each consisting of the phase of action planning, performing the action, observation, and then reflecting on the results. These iterations will follow one another in a chronological manner so as to allow for the refining of actions with the aim of making sure that a positive outcome is realized in the long run.  Below is a brief overview of the iterations that this research comprises.  The first one is orientation, the second is training, the third is an examination of the characteristics of the self-driven car, the fourth is determining the feasibility, and the fifth is drawing conclusions and making recommendations in light of the findings of the fourth iteration.

Iteration 1: Orientation

The first iteration on orientation is where I will meet some experts that have the knowledge on the cars and the need for automation.  These experts will help to shed more light on the area of the accidents through carrying out a market analysis of the same to validate the findings that support the need for automation. That will prepare me for the next iteration on brainstorming and training.

Iteration 2: Brainstorming and Training

The brainstorming and iteration are the second iterations of my research on determining the feasibility of Google’s self-driven car in today’s world. I will get training from experts on the driverless cars and the issues that are involved in the design of the driverless cars and the general development lifecycle of software.  They will also handle the process of adopting technology and how to deal with resistance to the technology.

Iteration 3: Examination of the Characteristics of the Self-Driven Car

In the third iteration of my research, I will have thoroughly to examine the features of Google’s self-driven car.  I will also solicit the help of experts, particularly the ones with the knowledge on the Google’s driverless car. They will shed light on the way this vehicle functions including its features, advantages, and disadvantages.

Iteration 4: Determining the Feasibility of the Self-Driven Car

The fourth iteration is where the thorough examination of the possibility will take place to find if this new technology is possible.  The researcher will put several issues into consideration while cooperating with other experts as required by Action Research.  The supporting hardware, software, the issue of ethics, the current roads, and the communication issues will have a discussion during this iteration to find out the possibility of achieving the target of the driverless car.

Iteration 5: Drawing Conclusions and Making Recommendations

In the iteration on drawing the conclusions and making recommendations, there will be an in-depth examination of the possibility of the project delivering its promises based on the findings from the fourth iteration. There will be the critiquing of the findings and the examination of all aspects that may hinder the feasibility of the project after which recommendations will be made.



Anderson, J. M., Kalra, N., Stanley, K. D., Sorensen, P., Samaras, C., Oluwatola, O. A., Rand Corporation,, … Rand Transportation, Space, and Technology (Program),. (2014). Autonomous vehicle technology: A guide for policymakers.

Boxwell, M. (2014). The electric car guide: Your guide to buying and owning an electric car. Cham: Springer.

Brown, A. S. (2011). Google’s autonomous car applies lessons learned from driverless races. Mechanical Engineering-CIME, 133(2), 31-32.

Currie, S. (2016). Self-driving car. Chicago, Illinois: Norwood House Press.

Knight, W. (2013). Driverless Cars. Technology Review, 116(6), 44-49.

Markoff, J. (2014). Google’s next phase in driverless cars: No steering wheel or brake pedals. New York Times.

Miller, M. (2015). The internet of things: How smart TVs, smart cars, smart homes, and smart cities are changing the world. Pearson Education.

Turban, E. (2015). Electronic commerce: A managerial and social networks perspective.  Cham: Springer.

Van Themsche, S. (2015). The Advent of Unmanned Electric Vehicles. Springer.

Villasenor, J. (2014). Products liability and driverless cars: Issues and guiding principles for legislation.

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