Generative Artificial Intelligence Technologies and Applications for Road Environment Understanding

Dear Colleagues,

This Special Issue explores the latest advancements and applications of generative AI technologies to enhance understanding road environments. Generative AI models have demonstrated remarkable capabilities in synthesizing realistic data, enabling novel solutions for tasks such as scene generation, object detection, and semantic segmentation in road environments. The topics will cover theoretical foundations, methodological developments, and the practical applications of generative AI for road scene analysis, 3D environment modeling, traffic flow prediction, and other related areas. The Special Issue will provide a platform for disseminating innovative ideas, fostering interdisciplinary collaborations, and driving progress in this rapidly evolving field. The topics include but are not limited to the following:

1. Generative adversarial networks for road scene synthesis and augmentation;
2. Variational autoencoders for road environment modeling and reconstruction;
3. Diffusion models for road scene generation and editing;
4. Generative models for 3D road environment reconstruction;
5. Conditional generative models for road object detection and segmentation;
6. Generative models for traffic flow prediction and simulation;
7. Adversarial training techniques for road environment understanding;
8. Interpretability and explainability of generative models in road scene analysis;
9. Multimodal generative models for road environments (e.g., combining vision and LiDAR data);
10. Generative models for data augmentation and domain adaptation in road scene understanding;
11. Generative models for road sign and lane marking synthesis;
12. Applications of generative AI in autonomous driving, intelligent transportation systems, and road safety.

Prof. Dr. Rung-Ching Chen
Prof. Dr. Long-Sheng Chen
Guest Editors

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• deep learning
• synthetic data generation
• road environments
• scene understanding
• intelligent transportation systems
• autonomous driving
• object detection
• semantic segmentation
• generative adversarial networks (GANs)
• variational autoencoders (VAEs)