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Applications of Heuristics and Artificial Intelligence in Autonomous Vehicles
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Abstract
In recent years a marked increase in the development of autonomous vehicles has facilitated by the advancements in the applications of heuristics and artificial intelligence (AI). Specifically, heuristic algorithms facilitate adaptive decision-making under diverse and constantly evolving environmental conditions, while AI systems enhance vehicle performance through learning from prior experiences, thereby promoting safety and efficiency. Notwithstanding these technological breakthroughs, ethical and legal challenges persist as significant obstacles to public trust and widespread adoption, including the allocation of liability in the event of accidents. This study aims to address three core research questions: firstly, the contribution of heuristics and AI to adaptive decision-making and path planning in autonomous vehicles; Furthermore, the ethical and safety challenges that arise from their real-world deployment. Finally, how real-world case studies, such as those involving Waymo and Uber, can inform the development of safer and more accountable autonomous systems. The significance of this research lies in its comprehensive examination of both the technical effectiveness and the ethical considerations of AI-powered autonomy. The paper's primary method is a literature review and case analysis, which it uses to highlight current achievements, and offer insight into the regulatory and technological improvements needed to ensure the responsible and widely accepted integration of autonomous vehicles into modern society.
Keywords
Heuristics, Autonomous Vehicles, Artificial Intelligence, Path Planning, A*
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Cite this article
Li,J. (2025). Applications of Heuristics and Artificial Intelligence in Autonomous Vehicles. Applied and Computational Engineering,154,83-89.
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of CONF-SEML 2025 Symposium: Machine Learning Theory and Applications
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