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Self-driving cars were once considered a thing of the future. There are some manufacturers that have managed to use technology to create them, and some are already being tested on the roads today. Most resources I reviewed say by 2030 we could see a lot more of them on the highways. Some people are skeptical as to whether they will outperform human driven cars. Based on studies, some companies think it will improve traffic flow and open up more transportation to disabled people (Dixon et al., 2020). Although studies are limited, data has shown that younger people support, and older people are resistant to the new technology due to concerns with functions or mishaps that could cause accidents.
Safety has been a major concern since this technology has been discovered. Due to this and the others mentioned consumers are not invested enough to trust these cars in real life due to a lack of their safety.
The car itself is all analytical which is similar to a smart computer. This is an advantage due to no emotions or distractions involved while driving and therefore the computers are faster and smarter to take actions (Fathy et al., 2020).
Manufacturers think a future with majority of self-driving cars on the road might be better. The complete system is has a raspberry pi as the main component which runs all the algorithms. The cameras are attached with the raspberry pi which has a variety of sensors.
The Arduino is another component in the system which is responsible for the car’s motor and motion. It receives signals from the raspberry pi to make the appropriate decision and action for the car. Three other sub-systems used are disparity map, anomalies detection, and lane detection along with dual cameras which calculate the map for determining distance. The cameras are also for taking pictures and identifying road lines. All these devices together help with decisions and awareness. During processing phase, real-time disparity map calculation takes place. The car moves while taking live video frames and making calculations on the disparity map for each frame. From there the car decides whether to slow down, keep moving, or stop (Fathy et al., 2020).
The anomalies in the car can detect many things but one of the things that may cause accidents are faded road signs. However, they can detect potholes on a multi-lane highway. During testing this algorithm achieved a 90% accuracy.
I think technology had an impact on this breakthrough. Based on the factors mentioned and from what I read; the production and use of self-driving cars does have a future.
References:
Dixon, G., Hart, P.S., Clarke, C., O’Donnell, N.H., & Hmielowski, J. (2020). What drives support for self-driving car technology in the United States? Journal of Risk Research, 23(3), 275-287
http://vlib.excelsior.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=142436370&site=eds-live&scope=site (Links to an external site.)
Fathy, M., Ashraf, N., Ismail, O., Fouad, S., Shaheen, L., & Hamdy, A. (2020). Design and implementation of self-driving car. Procedia Computer Science, 175, 165-172.
http://vlib.excelsior.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=edo&AN=144992597&site=eds-live&scope=site
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