Application of conditional generative adversarial network to multi-step car-following modeling

Area of Science:

• Intelligent Transportation Systems
• Machine Learning in Automotive Engineering
• Autonomous Driving Control

Background:

• Car-following models are crucial for the longitudinal control of connected and autonomous vehicles (CAVs).
• Existing models may not fully capture the complexity and variability of human driving behavior.
• Generative Adversarial Networks (GANs) show promise in modeling realistic data distributions.

Purpose of the Study:

• To apply a conditional Generative Adversarial Network (CGAN) for advanced car-following modeling in CAVs.
• To develop a generator with a sequence-to-sequence structure that mimics human driving decision-making.
• To evaluate the CGAN model's performance against traditional supervised and mathematical models.

Main Methods:

• Utilized a conditional Generative Adversarial Network (CGAN) with a sequence-to-sequence generator architecture.
• Trained and validated the CGAN model using an empirical car-following dataset.
• Compared the CGAN model's trajectory reproduction accuracy against supervised learning and mathematical models.
• Conducted numerical simulations, particularly focusing on mixed traffic flow conditions.

Main Results:

• The CGAN model demonstrated superior performance in trajectory reproduction compared to supervised and mathematical models.
• The model effectively imitates human driving behavior, capturing nuanced decision-making processes.
• Simulations indicated that CGAN-based CAVs significantly improve the stability and efficiency of mixed traffic flow.

Conclusions:

• Conditional GANs offer a powerful approach for realistic car-following modeling in autonomous driving.
• The proposed CGAN model enhances the imitation of human driving behavior, leading to better traffic flow dynamics.
• Integrating CGAN-based CAVs into traffic systems can lead to substantial improvements in overall traffic efficiency and stability.