20.500.12556/DKUM-92399 Advancing sustainable mobility: artificial intelligence approaches for autonomous vehicle trajectories in roundabouts This study develops and evaluates advanced predictive models for the trajectory planning of autonomous vehicles (AVs) in roundabouts, with the aim of significantly contributing to sustainable urban mobility. Starting from the “MRoundabout” speed model, several Artificial Intelligence (AI) and Machine Learning (ML) techniques, including Linear Regression (LR), Random Forest (RF), Support Vector Regression (SVR), Gradient Boosting Regression (GBR), and Neural Networks (NNs), were applied to accurately emulate human driving behavior and optimize AV trajectories. The results indicate that neural networks achieved the best predictive performance, with R2 values of up to 0.88 for speed prediction, 0.98 for acceleration, and 0.94 for differential distance, significantly outperforming traditional models. GBR and SVR provided moderate improvements over LR but encountered difficulties predicting acceleration and distance variables. AI-driven tools, such as ChatGPT-4, facilitated data pre-processing, model tuning, and interpretation, reducing computational time and enhancing workflow efficiency. A key contribution of this research lies in demonstrating the potential of AI-based trajectory planning to enhance AV navigation, fostering smoother, safer, and more sustainable mobility. The proposed approaches contribute to reduced energy consumption, lower emissions, and decreased traffic congestion, effectively addressing challenges related to urban sustainability. Future research will incorporate real traffic interactions to further refine the adaptability and robustness of the model. sustainable mobility autonomous vehicles machine learning roundabouts artificial intelligence ChatGPT trajnostna mobilnost avtonomna vozila strojno učenje krožišča umetna inteligenca true false true MDPI Angleški jezik Slovenski jezik https://www.mdpi.com/journal/sustainability © 2025 by the authors Članek v reviji 2025-04-04 14:59:04 2025-04-04 14:59:04 2026-01-26 12:03:56 0000-00-00 00:00:00 2025 0 0 str. 1-35 iss. 7, [article no.] 2988 Vol. 17 March 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2025-02-26 2025-03-26 2025-03-27 656.1:004.8 231511555 10.3390/su17072988 2071-1050 sustainability-17-02988-v2.pdf sustainability-17-02988-v2.pdf 1 DAB0E399E35D6C50578D095BDFCA126D b86e12b4eac8c055d420a40368207ae1b0055fb0c9b7acd85aee2dff9c0c8be5 d2c36949-1154-11f0-b51c-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=189997 https://www.mdpi.com/2071-1050/17/7/2988 0 da7c0d06-1154-11f0-b51c-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=189998 Fakulteta za gradbeništvo, prometno inženirstvo in arhitekturo 0 0 0