Abu Jafar Md Muzahid | Searcharxiv

arXiv2024

Survey on Human-Vehicle Interactions and AI Collaboration for Optimal Decision-Making in Automated Driving

The capabilities of automated vehicles are advancing rapidly, yet achieving full autonomy remains a significant challenge, requiring ongoing human cognition in decision-making processes. Incorporating human cognition into control algorithms has become increasingly important, as researchers work to develop strategies that minimize conflicts between human drivers and AI systems. Despite notable progress, many challenges persist, underscoring the need for further innovation and refinement in this field. This review covers recent progress in human-vehicle interaction (HVI) and AI collaboration for vehicle control. First, we start by looking at how HVI has evolved, pointing out key developments and identifying persistent problems. Second, we discuss the existing techniques, including methods for integrating human intuition and cognition into decision-making processes and developing systems that can mimic human behavior to enable optimal driving strategies and achieve safer and more efficient transportation. This review aims to contribute to the development of more effective and adaptive automated driving systems by enhancing human-AI collaboration.

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arXiv2022

Impact Learning: A Learning Method from Features Impact and Competition

Machine learning is the study of computer algorithms that can automatically improve based on data and experience. Machine learning algorithms build a model from sample data, called training data, to make predictions or judgments without being explicitly programmed to do so. A variety of wellknown machine learning algorithms have been developed for use in the field of computer science to analyze data. This paper introduced a new machine learning algorithm called impact learning. Impact learning is a supervised learning algorithm that can be consolidated in both classification and regression problems. It can furthermore manifest its superiority in analyzing competitive data. This algorithm is remarkable for learning from the competitive situation and the competition comes from the effects of autonomous features. It is prepared by the impacts of the highlights from the intrinsic rate of natural increase (RNI). We, moreover, manifest the prevalence of the impact learning over the conventional machine learning algorithm.

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