With ongoing development of autonomous driving systems and increasing desire for deployment, researchers continue to seek reliable approaches for ADS systems. The virtual simulation test (VST) has become a prominent approach for testing autonomous driving systems (ADS) and advanced driver assistance systems (ADAS) due to its advantages of fast execution, low cost, and high repeatability. However, the success of these simulation-based experiments heavily relies on the realism of the testing scenarios. It is needed to create more flexible and high-fidelity testing scenarios in VST in order to increase the safety and reliabilityof ADS and ADAS.To address this challenge, this paper introduces the "Transfusor" model, which leverages the transformer and diffusor models (two cutting-edge deep learning generative technologies). The primary objective of the Transfusor model is to generate highly realistic and controllable human-like lane-changing trajectories in highway scenarios. Extensive experiments were carried out, and the results demonstrate that the proposed model effectively learns the spatiotemporal characteristics of humans' lane-changing behaviors and successfully generates trajectories that closely mimic real-world human driving. As such, the proposed model can play a critical role of creating more flexible and high-fidelity testing scenarios in the VST, ultimately leading to safer and more reliable ADS and ADAS.

翻译：随着自动驾驶系统的持续发展及对部署需求的日益增长，研究人员不断寻求可靠的自动驾驶系统（ADS）方法。虚拟仿真测试（VST）凭借执行速度快、成本低、可重复性高等优势，已成为测试自动驾驶系统（ADS）和高级驾驶辅助系统（ADAS）的重要方法。然而，这些基于仿真的实验的成功在很大程度上取决于测试场景的真实性。为在VST中创建更灵活、高保真的测试场景，以提升ADS和ADAS的安全性与可靠性，本文提出了“Transfusor”模型，该模型融合了Transformer和扩散模型（两种前沿的深度学习生成技术）。Transfusor模型的主要目标是在高速公路场景中生成高度逼真且可控的类人换道轨迹。通过大量实验，结果表明该模型有效学习了人类换道行为的时空特征，并成功生成了高度模拟真实人类驾驶行为的轨迹。因此，所提出模型可在VST中扮演关键角色，用于创建更灵活、高保真的测试场景，最终实现更安全、更可靠的ADS和ADAS系统。