Swarm Wisdom for Smart Mobility - The Next Generation of Autonomous Vehicles

In the advent of realising Level 3 autonomous driving, the race of cities towards achieving Smart Mobility has recently escalated with promises of green, accessible, affordable, efficient, and ethical transport. Thus, the paradigm of smart mobility is intertwined with the challenges of autonomous driving and these are best addressed jointly. At the same time, breakthrough advances in artificial intelligence innovations have transformed the dream of autonomous driving to a near future tangible possibility. The challenges of scalability, accountability, trust, and incurred cost are nonetheless hindering the speedy deployment of the autonomous driving features and the societal acceptance of such technologies. In this paper, we offer an indepth analysis of the Smart Mobility and autonomous driving state of the art technologies and identify the mainstream trends and shortcomings. Furthermore, we offer a new hybrid ecosystem that curtails these obstacles and unlocks the potentials of smart mobility by creating the ubiquitous Swarm Wisdom.

[1]  Sidney N. Givigi,et al.  A Q-Learning Approach to Flocking With UAVs in a Stochastic Environment , 2017, IEEE Transactions on Cybernetics.

[2]  Arokia Paul Rajan,et al.  Evolution of Cloud Storage as Cloud Computing Infrastructure Service , 2013, ArXiv.

[3]  Philip Koopman,et al.  Autonomous Vehicle Safety: An Interdisciplinary Challenge , 2017, IEEE Intelligent Transportation Systems Magazine.

[4]  Zheng-Guo Wang,et al.  Road traffic injuries. , 2003, Chinese journal of traumatology = Zhonghua chuang shang za zhi.

[5]  S. P. Akpabio World Health Organisation , 1983, British Dental Journal.

[6]  Jason M. Allred,et al.  ASP: Learning to Forget With Adaptive Synaptic Plasticity in Spiking Neural Networks , 2017, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

[7]  Liang Wang,et al.  Wave Equation of Suppressed Traffic Flow Instabilities , 2018, IEEE Transactions on Intelligent Transportation Systems.

[8]  Danilo Giordano,et al.  UMAP: Urban mobility analysis platform to harvest car sharing data , 2017, 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI).

[9]  Yasser Abdel-Rady I. Mohamed,et al.  Data Lake Lambda Architecture for Smart Grids Big Data Analytics , 2018, IEEE Access.

[10]  Paolo Santi,et al.  Driving Behavior Analysis through CAN Bus Data in an Uncontrolled Environment , 2017, IEEE Transactions on Intelligent Transportation Systems.

[11]  Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles , 2022 .

[12]  Dariusz Mrozek,et al.  Fuzzy Join for Flexible Combining Big Data Lakes in Cyber-Physical Systems , 2018, IEEE Access.

[13]  Sanjay E. Sarma,et al.  A Survey of the Connected Vehicle Landscape—Architectures, Enabling Technologies, Applications, and Development Areas , 2017, IEEE Transactions on Intelligent Transportation Systems.

[14]  Christian Blum,et al.  Swarm Intelligence: Introduction and Applications , 2008, Swarm Intelligence.

[15]  A. Hyder,et al.  Road Traffic Injuries , 2017 .

[16]  Stephen M. Casner,et al.  The challenges of partially automated driving , 2016, Commun. ACM.

[17]  Itamar Arel,et al.  This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 1 Ensemble Learning in Fixed Expansion Layer Network , 2022 .