CoDEA: A framework for extraction and augmentation of cooperative lane-changing scenarios from naturalistic driving...

As modern transportation systems face increasing complexity, with challenges such as increased vehicle volumes, limited road resources, and rising safety concerns, there is an urgent need for innovative solutions. Cooperative driving, which enables vehicles to share information and collaborate through communication technologies, presents a promising solution to enhance safety, reduce congestion, and improve mobility. However, the validation of cooperative driving systems is hindered by a critical scarcity of real-world data. To address this challenge, we introduce CoDEA (Cooperative Driving Extraction and Augmentation), a comprehensive three-stage pipeline designed to generate robust and realistic cooperative driving datasets. First, a systematic method is developed to extract cooperative lane-changing behaviors from large-scale Naturalistic Driving Data (NDD), ensuring that the extracted data captures the key kinematic and cooperative features of real-world scenarios. Next, to effectively generate realistic cooperative lane-changing scenarios, we enhance the DiffTraj framework by introducing our Interaction-Aware Context Encoding (IA-CE) module. This module allows the diffusion model to condition its generation process on the nuanced interactions between vehicles, leading to the creation of more realistic and diverse cooperative trajectories. Finally, the effectiveness of the generated trajectories is evaluated using computational metrics such as RMSE and MAE, and by comparing key feature distributions between real and generated trajectories. The results show a strong similarity between the generated data and real-world cooperative lane-changing patterns, while also introducing greater diversity in certain features. Ultimately, the proposed CoDEA approach lays a solid foundation for advancing cooperative lane change control algorithms by providing a robust dataset for both training and evaluation, effectively bridging the gap between real-world complexity and algorithm testing environments.