Viability

It can be said that the use of artificial can be most efficient that can be done in order to enhance the process of driving. The artificial intelligence can easily access and properly analyse the informations of the drivers [3]. Also, as the technology now is a readily available hence it can be said that the use of technology is one of the most economically viable thing that can be implemented. The use of AI can mostly help in the sectors that directly use the data generated by the customers.

It can be said that although the use of artificial intelligence in the present time is not a tough thing to find, but using this in this concept is something new that is to be implemented. The use of AI can be found in every sector of the world [5]. The AI systems are been used by many of the firms like Google, Facebook and other organisational behaviour for the process of enhancing the technology they are using. It can be said that the use of Artificial Intelligence can be implemented in each and every sector of the world in the coming future.

Architectural Overview

The use of the AI for the process of implementing and ensuing proper driving conditions for a diver. The design that is presented in the paper can be said that it is using the logical chunks. Logical chunking can be explained as an approach which uses the effective use of the short term memory by grouping information [1]. That is, it is breaking down the long strings of information into smaller chunks. In this case it is using the information of the drivers’ actions in a fixed interval of time and producing results accordingly. When the driver is not in action, it uses the previous responses of the driver analyses the same and provides result. When driving continues for more than 15 minutes after the first warning has been chimed and without taking a break, a second warning is given, this time with a louder and more attentive chime [4]. The warning will continue until the driver gives a physical input to stop the chime by pressing the OK button on the steering wheel menu control. Thus it can be said that it is using short term results for the process of analysing information.

Yes the project is open, and can be used for the process of the evolution. The process that are explained in the paper can be used for designing the process. Further, development can be easily done with the help of using the API and other systems [6]. This system can also be developed for other modes of the transports if developed in a proper manner.

In the process of developing, monolithic application defines the single-tiered software in which the user interface and data access code are combined into a single program from a single platform [2]. Thus, it can be said the software can not only run using the API and it can download the SDKs or the user can enable it as and on when required. The system uses the software for the process of enhancing the project process of development of artificial intelligence.

Technologies required:

In order to run this system in a proper manner, a huge number of modern technologies have been used. Initially the major hardware components that are used are camera, sensors, wireless chips and others. While in the software part the major concepts of the big data analytics, machine learning and programming languages like python [1]. In order to properly develop the solution there are a number of modern technologies that have been used. All these technologies is used in order to develop the solution.

The system can be developed in the sectors like the automobile industry. AS the application is specifically designed for the drivers while they are driving hence the solution must be deployed in this platform only. This is the major platform can help in the process of enhancing the driving of the customers [3]. The aim of this project is to hopefully try and solve, improve, reduce and hopefully eliminate all accidents on the road due to driver drowsiness.

References

[1] Russell, Stuart J., and Peter Norvig. Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited,, 2016.
[2] Nilsson, Nils J. Principles of artificial intelligence. Morgan Kaufmann, 2014.
[3] Wenger, Etienne. Artificial intelligence and tutoring systems: computational and cognitive approaches to the communication of knowledge management. Morgan Kaufmann, 2014.
[4] Bond, Alan H., and Les Gasser, eds. Readings in distributed artificial intelligence. Morgan Kaufmann, 2014.
[5] Hutter, Frank, Lin Xu, Holger H. Hoos, and Kevin Leyton-Brown. "Algorithm runtime prediction: Methods & evaluation." Artificial Intelligence 206 (2014): 79-111.
[6] Charniak, Eugene, Christopher K. Riesbeck, Drew V. McDermott, and James R. Meehan. Artificial intelligence programming. Psychology Press, 2014.