  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.
