Autonomous vehicles: The future of automobiles

Rajasekhar, M V; Jaswal, Anil Kumar

doi:10.1109/itec-india.2015.7386874

Cited by 70 publications

(22 citation statements)

References 2 publications

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“…Minimizing interference may increase network capacity and throughput. (6) Edge computing: A stadium network must be facilitated with the emerging technology of edge computing. Edge computing offers cloud resources closer to the end-users, thereby improving the latency requirements and reducing the backhaul traffic on cloud devices [21].…”

Section: Motivation: Ultra-dense Networkmentioning

confidence: 99%

“…The proliferation of Internet of Things (IoT) devices along with advances in wireless communication technologies, such as multi-access edge computing (MEC) [1], multiple-input and multiple-output (MIMO) [2], millimeter-wave (MMW) [3], and Fifth Generation (5G) and beyond, offer novel opportunities to support applications (e.g., smart cities [4], intelligent health-care systems [5], autonomous vehicles [6], and the industrial IoT [7]) that are becoming an integral part of our modern life. These smart applications further aid the transformation of IoT from concept to reality.…”

Section: Introductionmentioning

confidence: 99%

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A Statistical Performance Analysis of Named Data Ultra Dense Networks

et al. 2019

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Named data networking (NDN) is a novel communication paradigm that employs names rather than references to the location of the content. It exploits in-network caching among different nodes in a network to provide the fast delivery of content. Thus, it reduces the backhaul traffic on the original producer and also eliminates the need for a stable connection between the source (consumer) and destination (producer). However, a bottleneck or congestion may still occur in very crowded areas, such as shopping malls, concerts, or stadiums, where thousands of users are requesting information from a device that resides at the edge of the network. This paper provides an analysis of content delivery in terms of the interest satisfaction rate (ISR) in ultra-dense network traffic situations and presents a final and an adequate statistical model based on multiple linear regression (MLR) to enhance ISR. A four-way factorial design was used to generate the dataset by performing simulations in ndnSIM. The results show that there is no significant interaction between four predictors: number of nodes (NN), number of interests (NI) per second, router bandwidth (RB), and router delay (RD). Moreover, the NI has a negative effect, and log(RB) has a positive effect on the ISR. The NN less than 10 has a significantly higher effect on the ISR compared with other nodes’ densities.

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Section: Motivation: Ultra-dense Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Statistical Performance Analysis of Named Data Ultra Dense Networks

et al. 2019

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“…As Yogi Berra is reputed to have observed, “ prediction is hard, especially about the future.” However, the purpose here, as it has been in other associated works (e.g., Hancock, 2008 ), is to ruminate upon what, with respect to driving, is to come. The magnitude of change that promises to occur with the widespread penetration of autonomous vehicles ( Rajasekhar and Jaswal, 2015 ), is very much in proportion to the extent of driving’s past and persistent longevity as well as its present ubiquity (and see Litman, 2017 ). Thus, the uncertainty which is associated with this anticipated degree of change is great in proportion ( Hancock et al, 2019 ).…”

Section: Introduction: a Short Glance Back – A Long Look Forwardmentioning

confidence: 99%

Driving Into the Future

Hancock

2020

Front. Psychol.

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This work considers the future of driving in terms of both its short-and long-term horizons. It conjectures that human-controlled driving will follow in the footsteps of a wide swath of other, now either residual or abandoned human occupations. Pursuits that have preceded it into oblivion. In this way, driving will dwindle down into only a few niche locales wherein enthusiasts will still persist, much in the way that steam train hobbyists now continue their own aspirational inclinations. Of course, the value of any such prognostication is in direct proportion to the degree that information is conveyed, and prospective uncertainty reduced. In more colloquial terms: the devil is in the details of these coming transitions. It is anticipated that we will see a progressive transformation of the composition of on-road traffic that will be registered most immediately in the realm of professional transportation in which the imperative for optimization exceeds that in virtually all other user segments. The transition from manual control to full automation will be more punctate than gradualist in its evolutionary development. As performance optimization slowly exhausts the commercial sector, it will progressively transition more into the discretionary realm by dint of simple technology transfer alone. The hedonic dimension of everyday driving will be dispersed and pursued by progressively fewer individuals. The traveling window of generational expectation will soon mean that human driving will be largely "forgotten," as each sequential generation matures without this, still presently common experience. Indications of this stage of progress are beginning to be witnessed in the demographic profile of vehicle usage and ownership rates. The purpose of the exposition which follows is to consider and support each of these stated propositions.

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“…In the growing body of literature, various aspects of AVs are examined (mainly through simulations), including the positive and negative impact of AVs on our lives and environment. The advantages of adopting AVs are numerous, such as increased mobility options for everyone, especially for the disabled, drunk, inattentive, senior, and children to have better access and options to fit their transportation needs [2]; more effective traffic flow and reduced traffic congestion [3]; increased safety and declining traffic accidents and fatality rates [4], improved productivity and gains in pleasure while traveling in a car [5]; more smooth and comfortable and less stressful rides [2]; and lower greenhouse gas (GHGs) emissions [6]. The negative impact lies in the possible consequences resulting from safety, security, privacy, and liability related issues [2,7].…”

Section: Introduction and Overviewmentioning

confidence: 99%

Exploring the correlations between spatiotemporal daily activity-travel patterns and stated interest and perception of risk with self-driving cars

Xiao

McBride

et al. 2020

AGILE GIScience Ser.

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Abstract. The key to Autonomous Vehicles (AVs) successful penetration of markets lies in identifying specific needs that AVs satisfy for daily activity-travel participation of individuals. In this paper we explore whether and to what extent people’s exhibited spatiotemporal activity-travel patterns correlate with their stated perceptions about self-driving cars. We investigate the travel diaries of 3,411 survey respondents who live in the Puget Sound region of the U.S. in 2017 using sequence analysis. In parallel, we apply hierarchical clustering to identify people’s attitudes based on their stated interest and perception of risks about AVs. A multinomial regression model is built to examine the correlations between AV attitude clusters and daily activity-travel patterns. Statistically significant correlations are then identified. The model results suggest that people exhibiting different activity-travel behavior patterns also express distinct attitudes towards the uses of AVs. The model shows that people who travel to work during the day are more likely to be positive to AVs. In particular, the group traveling to work later than the regular 8-to-5 schedule shows stronger interest and less concerns to AVs, which can be partially explained by the diverse activities they do throughout the day, the variety of travel modes they use and presumably more schedule flexibility they need than the public transportation system offers.

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Autonomous vehicles: The future of automobiles

Cited by 70 publications

References 2 publications

A Statistical Performance Analysis of Named Data Ultra Dense Networks

A Statistical Performance Analysis of Named Data Ultra Dense Networks

Driving Into the Future

Exploring the correlations between spatiotemporal daily activity-travel patterns and stated interest and perception of risk with self-driving cars

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