| Title: | Advancing sustainable mobility: artificial intelligence approaches for autonomous vehicle trajectories in roundabouts |
|---|
| Authors: | ID Leonardi, Salvatore (Author)
ID Distefano, Natalia (Author)
ID Gruden, Chiara (Author) |
|---|
| Files: |  sustainability-17-02988-v2.pdf (9,00 MB)
MD5: DAB0E399E35D6C50578D095BDFCA126D
 https://www.mdpi.com/2071-1050/17/7/2988
|
|---|
| Language: | English |
|---|
| Work type: | Article |
|---|
| Typology: | 1.01 - Original Scientific Article |
|---|
| Organization: | FGPA - Faculty of Civil Engineering, Transportation Engineering and Architecture
|
|---|
| Abstract: | 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. |
|---|
| Keywords: | sustainable mobility, autonomous vehicles, machine learning, roundabouts, artificial intelligence, ChatGPT |
|---|
| Publication status: | Published |
|---|
| Publication version: | Version of Record |
|---|
| Submitted for review: | 26.02.2025 |
|---|
| Article acceptance date: | 26.03.2025 |
|---|
| Publication date: | 27.03.2025 |
|---|
| Publisher: | MDPI |
|---|
| Year of publishing: | 2025 |
|---|
| Number of pages: | str. 1-35 |
|---|
| Numbering: | Vol. 17, iss. 7, [article no.] 2988 |
|---|
| PID: | 20.500.12556/DKUM-92399  |
|---|
| UDC: | 656.1:004.8 |
|---|
| ISSN on article: | 2071-1050 |
|---|
| COBISS.SI-ID: | 231511555 |
|---|
| DOI: | 10.3390/su17072988 |
|---|
| Copyright: | © 2025 by the authors |
|---|
| Publication date in DKUM: | 04.04.2025 |
|---|
| Views: | 170 |
|---|
| Downloads: | 13 |
|---|
| Metadata: |    |
|---|
| Categories: | Misc.
|
|---|
|
:
|
Copy citation |
|---|
| | | | Average score: | (0 votes) |
|---|
| Your score: | Voting is allowed only for logged in users. |
|---|
| Share: |  |
|---|
Hover the mouse pointer over a document title to show the abstract or click
on the title to get all document metadata. |
