Ma Lijing, Qu Shiru, Song Lijun, Zhang Zhiteng, Ren Jie
School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
Entropy (Basel). 2023 Jul 12;25(7):1050. doi: 10.3390/e25071050.
This paper proposes a novel hybrid car-following model: the physics-informed conditional generative adversarial network (PICGAN), designed to enhance multi-step car-following modeling in mixed traffic flow scenarios. This hybrid model leverages the strengths of both physics-based and deep-learning-based models. By taking advantage of the inherent structure of GAN, the PICGAN eliminates the need for an explicit weighting parameter typically used in the combination of traditional physics-based and data-driven models. The effectiveness of the proposed model is substantiated through case studies using the NGSIM I-80 dataset. These studies demonstrate the model's superior trajectory reproduction, suggesting its potential as a strong contender to replace conventional models in trajectory prediction tasks. Furthermore, the deployment of PICGAN significantly enhances the stability and efficiency in mixed traffic flow environments. Given its reliable and stable results, the PICGAN framework contributes substantially to the development of efficient longitudinal control strategies for connected autonomous vehicles (CAVs) in real-world mixed traffic conditions.
基于物理知识的条件生成对抗网络（PICGAN），旨在增强混合交通流场景下的多步跟车建模。这种混合模型利用了基于物理的模型和基于深度学习的模型的优势。通过利用生成对抗网络的固有结构，PICGAN无需传统基于物理的模型和数据驱动模型相结合时通常使用的显式加权参数。通过使用NGSIM I-80数据集的案例研究证实了所提出模型的有效性。这些研究表明该模型具有卓越的轨迹再现能力，表明其在轨迹预测任务中作为替代传统模型的有力竞争者的潜力。此外，PICGAN的部署显著提高了混合交通流环境中的稳定性和效率。鉴于其可靠且稳定的结果，PICGAN框架为在实际混合交通条件下为联网自动驾驶车辆（CAV）开发高效的纵向控制策略做出了重大贡献。
