For generations, the vehicle industry has been a basis of modernization as well as cost-effective way to enlarge economy. The skill to drive is a sign of mobility and liberty that span generations. It has become apparent that automobiles play a pivotal role in the daily life and come up with many benefits towards the society (Howard and Dai, 2014).
Technology has changes rapidly over the time frame and mostly for the benefits of humanity. While, there is no point to say that such a huge technological advancement eases the way of life, there is few adverse effect of it. Nevertheless for all the payback conferred on humanity, no other innovation in the history of technological advancement has affected as much damage as the vehicle industry. It has seen that around 1 million people died in traffic accident each year all over the globe, which mean in every 30 seconds, someone dies in a traffic accident (Kim et al, 2013). In the U.K., car accidents are the most important reason of death for population between the ages of 3 and 34. What the most interesting fact is, because of human mistake more than 90% of automobile accidents took place all around the world (Brett, 2016).
If the UK Transportation Department data has been taken into consideration, then it can be seen that the quality of life also somehow getting affected due to this. According to the KPMG report, people around the globe spent on an average 52 minutes per day for driving. If such opportunity cost is measured in terms of productivity or the nature of spending nature with friends as well as family, it is the fact that this augments the stress individual level. Hence, it can be said that manual driving is no doubt a significant concern in the contemporary world.
Autonomous vehicles or the self driving car (SDC) could ease or entirely get to the bottom of these severe problems. Decision makers in the automobile industry consider that there are enough stimuli for both the customer and company to produce self-driving cars a certainty, as quicker than most might imagine. The long-standing revolution to the vehicle industry as well as the standard of living of consumers will be across-the-board (Schoettle and Sivak 2014). However, such changes are most likely to come about slowly but surely over the next 3-10 years.
With their impending benefits, conversely, SDC technologies carry a new set of issues both at the means of transportation and organization levels. In specific the impact on metropolitan transportation, boundaries on a vehicle’s driving choice, apparatus steadfastness, data isolation assurances, as well as unease over transportation investment, and moral allegations for safety. It has been argued that SDCs initiate new set of concerns in social impartiality, upsetting the allotment of benefits and load in society. By privileging an optimistic as well as without any problems related to SDC technology; up to date design practices and institutions are turning around these new vehicles and steadily ignored significant moral and social challenges.
The strategy of autonomous vehicle accomplishment is merely one of many inclinations likely to influence future transportation load and costs, and consequently planning decisions, and not essentially the most imperative one. Its eventual impacts count on how it works together with other aspects, taken for example, shifts from individual to shared cars. It is perhaps not a “game changer” neither a “paradigm shift” in view of the fact that it does not essentially transform how transport problems is defined; to a certain extent, it strengthen obtainable automobile-oriented transportation arrangement.
Individual safety aspects, infrastructure effectiveness, quality of life as well as a stand by consumer base are merely a few of the essential features that will lend a hand to build self-driving cars a reality. Technology is progressing speedily, both incrementally from obtainable merchants and from new competitors. A car operational with existing technology can receive in more information rapidly and consistently, and then process it to put into practice an accurate decision about a multifaceted circumstances. Slow but sure preambles of these features in associated with physically powerful cost-effective motivators are definite to conquer such obstacles.
It has been observed that till now, the traffic frameworks across world are not adequately advanced to assess or restrained driver disruption, whether it is from single source or from multiple sources. According to the World Health Organization (WHO) report near about 1.3 million people dies every year due traffic related accidents. Not only has that, the WHO also estimates that by the end of 2030 it will consider as the fifth most prevailing large-scale cause of death (Lutin et al., 2013). While assessing the causes of such traffic accidents, it has been found that driver’s error was the primary reason. This happened because of loss of concentration (Blyth et al., 2015). Without a doubt, it is predicted as the most dangerous issue towards the society as research reports have shown that at every places, between 16% and 80% of traffic accidents are unswervingly or obliquely resulted to driver disruptions (Merat et al., 2015). Considering its severity and adverse effect towards society; it is not revelation that driver distraction has previously been taken as the significant aspect of comprehensive study. In this context, a list of notable literatures as well as peer reviewed journal articles can be considered, for example, (Narla, 2013). From these studies, the depictions of this situation become clear to all. All these studies have shown that around 70% of single car or rear end accidents occurred due to inattention (Bojarski et al., 2016), and rest 30% of the accident cases are taken places because of un-avoidance scenario of driving distraction (Payre et al., 2014). Regardless of this, there is at present not a common characterization of driver disruption even with commonalities in how it is comprehended as well as explained (Fagnant and Kockelman, 2014).
While analyzing the causes of distraction, it has found from several studies that talking on mobile phone during car driving is a prominent characteristic of the distraction. It is measured as the most essential aspect, nevertheless, to be familiar with other prospective basis of driver distraction, Fagnant and Kockelman (2015) has performed a survey in his study, in which he shown that there are some other aspects that causes driver’s distraction like, concentration break, problem with adjustment of vehicle equipments, lesser view of external citizens, things or events, chatting with passengers, and drinking or eating or smoking (Strayer, 2015). This study has also shown that while use of mobile phone while driving is a moderator contributor, all these factors mentioned above also have equal contributions towards traffic crashes. Again, as opined by Okuda et al., (2014), rather talking on mobile phone during driving, other activities like scripting text messages, checking text messages as well as using a mobile telephone are caused to traffic accident. It has become apparent that there is an inducement to inspect driver interruption in undemanding non-systemic ways at what time, even though in the case of its own devices such as the mobile phone; the extent for added complication is huge. Take as an example, a received text message catches the attention of the driver by the display lighting up, frequently went together with by an audio signal, both of which contend for the already restricted attention. It is the fact that the driver might connect with the device and by hand enter a reply, using the key pad, while dealing the steering wheel single hand (Kyriakidis et al., 2015). This automatically decreases their substantial control over the car. Additional cognitive command is put forth in replying to this message. Thus, it can be concluded that together with visual, manual as well as cognitive interruptions may take place while the car is on the way (Howard and Dai, 2014).
Without a doubt, there is a gap between the existing literatures along with the most up to date forms of new automobile technological advancement. This is an alarm for the reason that the technological advancement in automobile industry is increased speedily (Kim et al., 2013) and thus study on driver distraction has to continue considering this technological up gradations. The contemporary automobile industry is no more dealing with disconnected devices with a definite interruption alleyway, however multiple devices as well as manifold means by which driving-emphasized cognitive resources might be wrinkled. Driver interruption, for that reason, turn out to be a systems concern. Systems thoughts itself has an enormously elongated inheritance (Brett, 2016), but it is only moderately in recent times that the perception are beginning to come across more extensive exercise in the road safety field (Schoettle and Sivak, 2014).
Without a doubt, to take a guide from (Lutin et al., 2013) at times contentious work on systems thoughts, and lengthen this allegory to its perimeter, even the phrase ‘driver distraction’ may no more be suitable. The obvious question is whether it is simply the driver who is unfocused? Or else is it the transportation arrangement that allows them to befall unfocused as well as permit unplanned belongings to disseminate further? This is a controversial aspect. Less debatable is the substantiation for interruption, its involvement as a most important cause in highway traffic accidents, and the communal as well as financial cost in individual suffering (Blyth et al., 2015).
What leftovers imperative is to comprehend, pigeonhole and test the organization, extent and influence of driver interruption from a ‘systems’ standpoint. To accomplish this, a foremost methodical reconsider of driver’s involvement with Multiple Additional-to-Driving (MAD) tasks is carried out. It searched for addressing three main aspects:
(i) How many journals have unambiguously measured the issue?
(ii) What is the outcome of driver’s distraction on driving?
Transportation of people and commodities underpins the contemporary built-up society. With over one billion cars in the world these days, the mechanical road vehicle is the major support of up to date transportation. Since the commencement of the 20th century, the vehicle industries has revolutionized the places, customs, traditions, and individuality through the multifaceted matrix of technological, monetary, financially viable, supporting, and societal linkages decisive with its design, assemble, manufacture, and use (Merat et al., 2014).
At this moment, mechanical road transportation is set to modernize the society once again on an enormous scale. A technological advancement is in progress, moving towards self-driving car (SDC) technology enabled cars that are anticipated to make use of computational algorithms, sensors, as well as communication apparatus to involuntarily find the way in a diverse environment exclusive of human drivers (Narla, 2013). SDCs also assure amplified safety, speediness and ease, as well as abridged energy use.
Several researches have shown that autonomous vehicles will endow with momentous user expediency, safety, overcrowding reductions, energy savings, and contamination reduction benefits (Payre et al., 2014). However, such statements may be flashy. Taken for consideration, researchers have argued that as the driver distractions caused above 90% of traffic accidents; introduction of self-driving cars will diminish the accident rates by 90% (Fagnant and Kockelman, 2014). Even if there is a system failure in SDC technology, cyber terrorism (Fagnant and Kockelman, 2015), as well as ricochet effects, the reduction of accident still remain around 90% in against human driving car technology (Strayer, 2015). It has also seen that when people sense safer, the tendency to use of seat belt reduces significantly. At the same time the outside people who walk around the roads are also become less casuals.
Now, seeing this, human drivers may be attracted to fasten together with autonomous vehicle squads. Consequently this will bring in new risks and enforcement needs. Thus, Okuda et al., (2014) truly said that pedestrians to be converted into less vigilant and accountable more or less for autonomous vehicles. In this context, an in depth analysis performed by Kyriakidis et al., (2015) concluded that self driving cars may be no safer than a normal driver and may amplify total accident cases due to such self- and human-driven vehicles mix. At the same time if the economic aspect is taken into consideration, then it can be said that autonomous cars surely diminish the demand for public transit travels, which will negatively affect people who work in public transportation department. Again, it also encourages more sprawled development prototypes which cut down transport facilities and augment total vehicle travel.
The incremental expenses of developing autonomous cars are indecisive. They necessitate an assortment of extraordinary sensors, computers as well as controls, which at this time cost tens of thousands of dollar however to be expected to turn out to be cheaper with mass production (Howard and Dai, 2014). On the other hand, since system failures could be incurable to both vehicle occupiers along with other road users, all decisive apparatus will require to congregate elevated manufacturing, setting up, repair, testing and safeguarding standards, close to maintenance standards for aircraft components. This clearly indicates that development of such self driving cars will be relatively expensive. At the same time autonomous car operation is also require a particular map-reading and navigation service subscription (Brett, 2016).
Autonomous cars can be programmed to maximize occupier ease. Narla (2013) in this context argued that due to this reason, vehicle passengers seems to be more susceptible to increase of velocity than drivers, and occupiers can utilize travel time to work or rest. It is reasonable that for relieve sake users will program their car for inferior speeding up/ speeding down characteristics than human-driven cars, causing to lessening in total metropolitan roadway facility.
Conclusion
Thus, it can be concluded that self-driving car (SDC) will result fewer vehicle crashes. At the same time, the flow of traffic will become more efficient and consequently congestion will reduce to a significant level. However, it is also the fact that the occupations like people in public transits, automobile industries will reduce. Considering both the aspect, it is recommended that further research work needs to be conducted regarding cost and benefit analysis. Again, as the technology improved frequently, the policy maker’s needs to consider facts that subsidize taxes and other associated costs.
References
Blyth, P.L., Mladenovic, M.N., Nardi, B.A., Su, N.M. and Ekbia, H.R., 2015, November. Driving the self-driving vehicle: Expanding the technological design Horizon. In Technology and Society (ISTAS), 2015 IEEE International Symposium on (pp. 1-6). IEEE.
Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L.D., Monfort, M., Muller, U., Zhang, J. and Zhang, X., 2016. End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316.
Brett, J.A., 2016. Thinking Local about Self-Driving Cars: A Local Framework for Autonomous Vehicle Development in the United States (Doctoral dissertation, University of Washington).
Fagnant, D.J. and Kockelman, K., 2014. Preparing a nation for autonomous vehicles: 1 opportunities, barriers and policy recommendations for 2 capitalizing on self-driven vehicles 3. Transportation Research Board.
Fagnant, D.J. and Kockelman, K., 2015. Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transportation Research Part A: Policy and Practice, 77, pp.167-181.
Howard, D. and Dai, D., 2014. Public perceptions of self-driving cars: The case of Berkeley, California. In Transportation Research Board 93rd Annual Meeting (Vol. 14, No. 4502).
Kim, J., Kim, H., Lakshmanan, K. and Rajkumar, R.R., 2013, April. Parallel scheduling for cyber-physical systems: Analysis and case study on a self-driving car. In Proceedings of the ACM/IEEE 4th International Conference on Cyber-Physical Systems (pp. 31-40). ACM.
Kyriakidis, M., Happee, R. and de Winter, J.C., 2015. Public opinion on automated driving: results of an international questionnaire among 5000 respondents. Transportation research part F: traffic psychology and behaviour, 32, pp.127-140.
Lutin, J.M., Kornhauser, A.L. and MASCE, E.L.L., 2013. The revolutionary development of self-driving vehicles and implications for the transportation engineering profession. Institute of Transportation Engineers. ITE Journal, 83(7), p.28.
Merat, N., Jamson, A.H., Lai, F.C., Daly, M. and Carsten, O.M., 2014. Transition to manual: Driver behaviour when resuming control from a highly automated vehicle. Transportation research part F: traffic psychology and behaviour, 27, pp.274-282.
Narla, S.R., 2013. The evolution of connected vehicle technology: From smart drivers to smart cars to… self-driving cars. Institute of Transportation Engineers. ITE Journal, 83(7), p.22.
Okuda, R., Kajiwara, Y. and Terashima, K., 2014, April. A survey of technical trend of ADAS and autonomous driving. In VLSI Technology, Systems and Application (VLSI-TSA), Proceedings of Technical Program-2014 International Symposium on (pp. 1-4). IEEE.
Payre, W., Cestac, J. and Delhomme, P., 2014. Intention to use a fully automated car: Attitudes and a priori acceptability. Transportation research part F: traffic psychology and behaviour, 27, pp.252-263.
Schoettle, B. and Sivak, M., 2014. A survey of public opinion about autonomous and self-driving vehicles in the US, the UK, and Australia.
Strayer, D.L., 2015. Is the technology in your car driving you to distraction?. Policy insights from the behavioral and brain sciences, 2(1), pp.157-165.
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