Extraction and Generation of Cooperative Driving Scenarios by Developing a Diffusion-Transformer-Wavelet Model

Compared to autonomous driving, cooperative driving offers higher efficiency and safety. However, the interaction based cooperative driving makes its control system more complex. To ensure the functional safety of this technology, extensive scenarios testing is required. Due to the complexity of cooperative driving scenarios and the scarcity of cooperative driving data, testing and validating it becomes challenging. Additionally, existing data generation methods are limited in their ability to generate effective cooperative driving scenarios. To address these challenges, this study develops a novel Diffusion-Transformer-Wavelet (Diffwavelet) model for cooperative driving scenario generations. A rule-based method is firstly proposed to extract cooperative scenarios, which integrates a surrogate safe measure of Stopping Distance Index (SDI) with a Dynamic Proactive Behavior Identification method (DPBI). Furthermore, the Diffwavelet model is developed for cooperative scenario generations, overcoming the limitations of existing generative models in generating realistic cooperative driving scenarios. The Wavelet module is employed to capture both local and trend features of the data, enhancing the models ability to capture the characteristics of cooperative driving data. Results show that, through the proposed rule-based method, a large number of cooperative driving scenarios can be extracted effectively. The proposed Diffwavelet model outperforms existing techniques for scenario generations, such as TimeGAN and TimeDiffusion, in terms of data quality, particularly in preserving the interaction characteristics of different vehicle trajectories. The parameter distribution of the generated scenario data closely matches that of the original data and also enhances scenarios diversity.