Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/22200
Title: A Preliminary Investigation of an Autonomous Vehicle Validation Infrastructure for Smart Cities
Authors: Deliparaschos, Kyriakos M. 
Santha, Gergely 
Zanotti Fragonara, Luca 
Petrunin, Ivan 
Zolotas, Argyrios C. 
Tsourdos, Antonios 
Major Field of Science: Engineering and Technology
Field Category: Electrical Engineering - Electronic Engineering - Information Engineering
Keywords: Autonomous vehicle;Sensor fusion;Artificial intelligence (AI);Smart city
Issue Date: 22-Sep-2020
Source: International Conference Mechatronic Systems and Materials (MSM), 2020, 1-3 July, Bialystok, Poland
Conference: International Conference Mechatronic Systems and Materials 
Abstract: The research and development of autonomous vehicle has entered the era of commercialization. While the vehicle self-driving technology has been growing rapidly, the validation for autonomous vehicle in terms of driving model, human factor model and traffic model is still maturing. Most of previous infrastructures are mainly focused on validation of those three models separately resorting either on real driving test at physical infrastructure or software simulation in virtualized infrastructure. However, neither the real driving test can cover all possible scenarios of autonomous driving and human factors, nor the virtualized software simulation can generate a feasible model for practical on/off-road driving. Furthermore, future autonomous transport in smart cities requires comprehensive validation. In order for autonomous vehicles to meet the autonomous transport in such complex traffic environment, an integrated testing and simulation infrastructure has been built targeting the systematic validation for autonomous vehicles: the Multi-User Environment for Autonomous Vehicle Innovation (MUEAVI). A preliminary investigation of a new autonomous vehicle validation infrastructure that can serve a multitude of research projects for smart city is presented.
URI: https://hdl.handle.net/20.500.14279/22200
DOI: 10.1109/MSM49833.2020.9201644
Rights: © IEEE
Attribution-NonCommercial-NoDerivatives 4.0 International
Type: Conference Papers
Affiliation : Cranfield University 
Publication Type: Peer Reviewed
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation

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