Supplementary data for the paper "Generating Realistic Traffic Scenarios: A Deep Learning Approach Using Generative Adversarial Networks (GANs)"

Traffic simulations are crucial for testing systems and human behaviour in transportation research. This study investigates the potential efficacy of Unsupervised Recycle Generative Adversarial Networks (Recycle–GANs) in generating realistic traffic videos by transforming daytime scenes into nighttime environments and vice-versa. By leveraging Unsupervised Recycle-GANs, we bridge the gap between data availability during day and night traffic scenarios, enhancing the robustness and applicability of deep learning algorithms for real-world applications. GPT-4V was provided with two sets of six different frames from each day and night time from the generated videos and queried whether the scenes were artificially created based on lightning, shadow behaviour, perspective, scale, texture, detail and presence of edge artefacts. The analysis of GPT-4V output did not reveal evidence of artificial manipulation, which supports the credibility and authenticity of the generated scenes. Furthermore, the generated transition videos were evaluated by 15 participants who rated their realism on a scale of 1 to 10, achieving a mean score of 7.21. Two persons identified the videos as deep-fake generated without pointing out what was fake in the video; they did mention that the traffic was generated.

交通仿真在交通研究领域的系统测试与人类行为分析中至关重要。本研究探究了无监督循环生成对抗网络（Unsupervised Recycle Generative Adversarial Networks，Recycle-GANs）在生成逼真交通视频方面的潜在效能，该网络可实现日间场景与夜间环境的双向风格转换。借助无监督循环生成对抗网络，本研究填补了昼夜交通场景数据可用性之间的缺口，进而提升了深度学习算法在实际应用中的鲁棒性与适用性。我们为GPT-4V提供了生成视频中昼夜场景各6组不同帧画面，并要求其基于光照、阴影表现、透视关系、尺度、纹理、细节以及边缘伪影的存在情况，判断该场景是否为人工合成。对GPT-4V输出结果的分析未发现人工操控的痕迹，这佐证了所生成场景的可信度与真实性。此外，15名参与者对生成的转换视频进行了评估，以1至10分的量表为其逼真度打分，最终平均得分为7.21。有2名参与者将视频判定为深度伪造生成内容，但未指出视频中具体的伪造之处，不过他们均提及视频中的交通场景为合成生成。