Conference PaperPDF Available

Design and Implementation Issues in Autonomous Vehicles- A Comparative Review

Authors:
  • School of ICT
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2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM) | 978-1-7281-9491-2/21/$31.00 ©2021 IEEE | DOI: 10.1109/ICCAKM50778.2021.9357706
Authorized licensed use limited to: Lovely Professional University - Phagwara. Downloaded on February 26,2021 at 05:46:29 UTC from IEEE Xplore. Restrictions apply.
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... With the increase in the size and complexity of autonomous vehicles, there is a need to develop and validate them in simulated environments to reduce development and testing costs [12]. Simulation environments cannot perfectly replicate the nuances of real-world sensor characteristics, leading to differences between the simulated and actual vehicle environments [13]. Therefore, adapting software to an actual vehicle environment is imperative when transitioning autonomous driving software developed in simulation environments to real vehicles. ...
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Autonomous driving has achieved a significant milestone in research and development over the last decade. There is increasing interest in the field as the deployment of self-operating vehicles on roads promises safer and more ecologically friendly transportation systems. With the rise of computationally powerful artificial intelligence (AI) techniques, autonomous vehicles can sense their environment with high precision, make safe real-time decisions, and operate more reliably without human interventions. However, intelligent decision-making in autonomous cars is not generally understandable by humans in the current state of the art, and such deficiency hinders this technology from being socially acceptable. Hence, aside from making safe real-time decisions, the AI systems of autonomous vehicles also need to explain how these decisions are constructed in order to be regulatory compliant across many jurisdictions. Our study sheds a comprehensive light on developing explainable artificial intelligence (XAI) approaches for autonomous vehicles. In particular, we make the following contributions. First, we provide a thorough overview of the present gaps with respect to explanations in the state-of-the-art autonomous vehicle industry. We then show the taxonomy of explanations and explanation receivers in this field. Thirdly, we propose a framework for an architecture of end-to-end autonomous driving systems and justify the role of XAI in both debugging and regulating such systems. Finally, as future research directions, we provide a field guide on XAI approaches for autonomous driving that can improve operational safety and transparency towards achieving public approval by regulators, manufacturers, and all engaged stakeholders.
... Automation in the next-generation mobile networks would also presume that a strategic position requires advanced agents to organize the infrastructure and maintain it. e authors of [6,7] discussed the importance of Artificial Intelligence (AI) and Machine Learning (ML) for improving vehicles and agents connectivity and quality of service. Ideally, these will be motivated by algorithms from AI [8]. ...
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