Artificial intelligence in the transportation sector
it would take off on its own, and re-design itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldn't compete and would be superseded” Technological changes are continuously being integrated to address problems that people face. Different people hold different views about the capabilities that technology is acknowledging to human beings. Rometty criticizes these perceptions by arguing that “some people call this artificial intelligence, but the reality is this technology will enhance us. So instead of artificial intelligence, I think we'll augment our intelligence. Self-driving cars Many car designers and manufacturers like Tesla are at the forefront of integrating artificial intelligence into vehicles in the last decade. The increased research and development in the car industry have enabled the adoption of sophisticated technologies to help simulate and monitor the capability of a vehicle engine.
Indeed, artificial intelligence plays a critical role in shaping the driving capability of cars. Investments in the transportation sector has been intense in the past decade. According to Davies (2018), there has been a shift from “maybe possible” to “inevitable. The installation of the computer programs with an ability to perceive the environment are helping realize efficiency in the transport sector by taking over tasks that were conventional left for human drivers. The introduction of autonomous vehicles in the streets are offering enormous solutions to social evils like traffic. The Google’s Waymo and Tesla’s products are increasingly becoming desirable in the transport sector since they can perceive, and continuously adjust the preinstalled rules and commands to navigate the road safely.
The adoption of autonomous technology is likely going to shape the transport sector since it will mitigate occurrences of accidents. Though free technology is necessary, it could also be a setback in the transport industry. Other facets of the issue would include its overall impact on the economy (Pearlstein 2014). The position of autonomous busses Many bus builders in Australia, China, Finland, California, Japan, Sweden, Switzerland, and Singapore have invested heavily in the autonomous bus for public transport (Rogers 2018). The bus has no driver and has the capability of manoeuvring the regular traffic by continuously relying on a series of integrated sensors to perceive likely hazards. The bus has the advantage of responding to risks faster than human especially, upon realization of impending danger (Smith 2013).
The use of autonomous buses s quickly taking a course in Europe. It constitutes the IBM Watson’s Internet of Things technology with a capability to aid provision of responses to set questions whenever given instructions. The autonomous trucks come with several implications for the transport sector. Unlike cars that share routes with passengers, these trucks use railroad, which not only makes it less susceptible traffic but also faster movement. Indeed, the merger of Uber and Otto which cost the US $700 million is a success story in the implementation of neural networks in trucks. An autonomous truck travelled a distance of 132 miles without the intervention of human input from Colorado Springs to Fort Collins to delivering 50,000 beer cans. According to Copelland (2000), AI applications are essential in the design of monitoring and control architecture.
The control systems promise actualization of desired output especially in the area of traffic control. Effective AI application will help control traffic at the road junctions by efficiently checking on traffic flows. Other areas of engagement will entail controlling air traffic at the airports, guiding routes at the airports as well as for the rail trucks. In spite of this functionality, challenges of rerouting would set in an impact on the smooth flow of traffic especially when many individual drivers all receive the same information about alternative routes to avoid traffic congestion will actually reduce congestion on that route while creating congestion on another route suggested by the app (Cavanillas, Curry & Wahlster 2016). AI will continue to provide the needed value and capability to the computer-aided designs, especially in the design making process (Marr 2018).
The future development and maintenance of roads and railways will exploit the AI application in determining priorities due to the extensive nature of the economy. Transport officials and other policymakers will depend on the AI application to decide on roads to build, how much financial commitments such projects require, as well as sections of the roads and bridges to maintains. Such decisions will go with adequate consideration of the traffic conditions. According to Pearlstein (2014), AI will present policymakers with the accurate conceptualization of the optimal use of transport facilities. The bus model will have a maximum speed of 70km/h, with the ability to recognize pedestrians and obstacles on the road. The model is suitable for the BRT systems that have become a standard practice in many cities.
If the development of autonomous cars is realized, then developing trucks and busses with the AI capabilities will be easy. The agitating factors for the development of the AI cars include the architectural design of the city, traffic patterns, availability of human skill as well environmental conditions (Cornet et al. AI will influence the future management practices as well as the design of transport channels used in the transportation system. Conclusion AI is a necessary evil in the transportation sector. The need for data and intelligence are becoming certain aspects of human life. Over time, big data play a crucial role in opening opportunities for developers, regulators, and consumers. Synchronization of intelligent systems into cars enables faster decision making as well as safety for vehicles.
Some of the associated merits for the intelligence confer to the ability of these systems to comply with the Asimov's three laws of Robotics strictly. What is Artificial Intelligence? Retrieved from http://www. alanturing. net/turing_archive/pages/reference%20articles/what%20is%20ai. html Cornet, A, Kässer, M, Müller, M, & Tschiesner, A. (September 2017). The 5 Autonomous Driving Levels Explained: Understand the different levels of driving automation to know where we stand with this rapidly advancing technology. Retrieved from https://medium. com/iotforall/the-5-autonomous-driving-levels-explained-b92a5e834928 Hengstler, M. , Enkel, E. , & Duelli, S. (1969) Perceptrons: An Introduction to Computational Geometry (First edition). MIT Press, Cambridge, Massachusetts. Pearlstein, S. (January 17, 2014). Review: ‘The Second Machine Age,’ by Erik Brynjolfsson and Andrew McAfee. Path planning for autonomous underwater vehicles.
From $10 to earn access
Only on Studyloop