A review on artificial intelligence for autonomous vehicle behavior prediction in mixed traffic environment / Syasya Nadhirah Hamedon, Juliana Johari and Fazlina Ahmat Ruslan

• Main Authors: Hamedon, Syasya Nadhirah, Johari, Juliana, Ahmat Ruslan, Fazlina
• Format: Article
• Language: English
• Published: UiTM Press 2024
• Subjects: Back propagation (Artificial intelligence); Engineering mathematics. Engineering analysis
• Online Access: https://ir.uitm.edu.my/id/eprint/105778/1/105778.pdf; https://ir.uitm.edu.my/id/eprint/105778/

This paper presents a comprehensive review of Artificial Intelligence (AI) techniques applied to autonomous vehicle (AV) behavior prediction in mixed traffic environments. The rapid advancement of AV technology, driven by AI, necessitates accurate prediction of surrounding vehicle behaviors for safe and efficient operation. The paper explores various machine learning and deep learning approaches, including Support Vector Machines, Random Forests, Convolutional Neural Networks, Long Short-Term Memory Networks, Graph Neural Networks, and Reinforcement Learning. These techniques demonstrate significant improvements in predicting and adapting to diverse road user behaviors, ultimately enhancing road safety. By analyzing the capabilities and limitations of these AI-powered solutions, this review aims to inform current applications and future advancements in AI-driven road safety.