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Publication Type : Conference Paper
Publisher : Proceedings of the 9th International Conference On Cloud Computing, Data Science and Engineering, Confluence 2019
Source : Proceedings of the 9th International Conference On Cloud Computing, Data Science and Engineering, Confluence 2019, Institute of Electrical and Electronics Engineers Inc., p.134-140 (2019)
ISBN : 9781538659335
Keywords : Automation, Automobile steering equipment, Cloud computing, Feedback, Feedback capabilities, Firmware, Industrial automation, Intelligent learning, Intelligent navigation, Low-latency communication, Navigation, Power steering systems, Programmable logic controllers, Society of automotive engineers, Structural integration, Vehicle to vehicle communications
Campus : Bengaluru
School : School of Engineering
Department : Electronics and Communication
Year : 2019
Abstract : Intelligent transit mechanism has become the need of the hour. The topical panacea seems to centre on the development of labyrinthine Automated Vehicles. With the inevitable boom in areas like Machine Learning, Industrial Automation coupled with highly convergent heuristics and the advent of highly efficient low latency communication devices, the idea of Automated Vehicles inches closer to practical realization every passing day. Design for any vehicle can be described as a function of various interdependent parameters, which are generally defined by the level of convolution and the level of automation defined for a system.This study explores the controller system design of Quasi-Automated Electric Vehicles. Categorically, this paper is an attempt at examining novel and innovative ways of designing a schematic for control of speed and navigation subsystems along with an exhaustive feedback capability designed to give users a real time virtual emulation of the vehicle. A wide ranging discussion on possible topologies for effective implementation of feedback have also been depicted in this paper. A column-type power steering system has been investigated as a control system for navigation of direction while an inverter based speed control mechanism has also been proposed. Furthermore, the discussed control algorithms have been rigorously tested and consequently proved capable of providing 1st level of autonomy, as defined by Society of Automotive Engineers standards while also reflecting a potential schematic for integration of intelligent learning firmware in the near future. © 2019 IEEE.
Cite this Research Publication : N. Sumedh, Srinivasan, M. S., Vivek, K. R., and Sagar B., “Quasi-automated firmware in e-automobiles: Structural integration”, in Proceedings of the 9th International Conference On Cloud Computing, Data Science and Engineering, Confluence 2019, 2019, pp. 134-140.