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- Title
Game-Based Flexible Merging Decision Method for Mixed Traffic of Connected Autonomous Vehicles and Manual Driving Vehicles on Urban Freeways.
- Authors
Du, Zhibin; Xie, Hui; Zhai, Pengyu; Yuan, Shoutong; Li, Yupeng; Wang, Jiao; Wang, Jiangbo; Liu, Kai
- Abstract
Connected Autonomous Vehicles (CAVs) have the potential to revolutionize traffic systems by autonomously handling complex maneuvers such as freeway ramp merging. However, the unpredictability of manual-driven vehicles (MDVs) poses a significant challenge. This study introduces a novel decision-making approach that incorporates the uncertainty of MDVs' driving styles, aiming to enhance merging efficiency and safety. By framing the CAV-MDV interaction as an incomplete information static game, we categorize MDVs' behaviors using a Gaussian Mixture Model–Support Vector Machine (GMM-SVM) method. The identified driving styles are then integrated into the flexible merging decision process, leveraging the concept of pure-strategy Nash equilibrium to determine optimal merging points and timing. A deep reinforcement learning algorithm is employed to refine CAVs' control decisions, ensuring efficient right-of-way acquisition. Simulations at both micro and macro levels validate the method's effectiveness, demonstrating improved merging success rates and overall traffic efficiency without compromising safety. The research contributes to the field by offering a sophisticated merging strategy that respects real-world driving behavior complexity, with potential for practical applications in urban traffic scenarios.
- Subjects
REINFORCEMENT learning; DEEP reinforcement learning; MACHINE learning; MOTOR vehicle driving; NASH equilibrium
- Publication
Applied Sciences (2076-3417), 2024, Vol 14, Issue 16, p7375
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app14167375