Game Theory-Based Ramp Merging for Mixed Traffic With Unity-SUMO Co-Simulation

Liao, Xishun; Zhao, Xuanpeng; Wang, Ziran; Han, Kyungtae; Tiwari, Prashant; Barth, Matthew; Wu, Guoyuan

doi:10.1109/tsmc.2021.3131431

Cited by 34 publications

(8 citation statements)

References 33 publications

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“…For example, Liao et al propose a cooperative driving game for CAVs in which each vehicle decides its optimal speed to minimize energy consumption while avoiding collisions with other vehicles [259]. The game-theoretic approach ensures that each vehicle converges to a Nash equilibrium where all players optimize their objective functions simultaneously.…”

Section: B Critical Components Of Collaborative Control Techniquesmentioning

confidence: 99%

A Systematic Survey of Control Techniques and Applications in Connected and Automated Vehicles

Liu,

Hua,

Deng

et al. 2023

IEEE Internet Things J.

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Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and indepth look at the state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration.

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Section: B Critical Components Of Collaborative Control Techniquesmentioning

confidence: 99%

A Systematic Survey of Control Techniques and Applications in Connected and Automated Vehicles

Liu,

Hua,

Deng

et al. 2023

IEEE Internet Things J.

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“…One of the studies reviewed, analyzed data derived from numerical simulations conducted. Finally, it is highlighted that apart from driver profiling and driving pattern recognition studies, there are studies that make use of co-simulation platforms to support personalized driving research [15].…”

Section: A Main Findingsmentioning

confidence: 99%

“…It is clarified that this research focuses only on studies related to driver profiles and driving patterns and not generally on the broader concept of individual driving behavior. For more details on studies related to personalized driving behavior analysis, e.g., using reinforcement learning, readers could refer to [13], [14], [15]. Some of these studies have developed more advanced simulation and co-simulation platforms to analyze personalized behavior and support personalized driving research.…”

Section: Introductionmentioning

confidence: 99%

Driver Profile and Driving Pattern Recognition for Road Safety Assessment: Main Challenges and Future Directions

Tselentis

Papadimitriou

2023

IEEE Open J. Intell. Transp. Syst.

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This study reviews the Artificial Intelligence and Machine Learning approaches developed thus far for driver profile and driving pattern recognition, representing a set of macroscopic and microscopic behaviors respectively, to enhance the understanding of human factors in road safety, and therefore reduce the number of crashes. It provides a definition of the two scientific fields in terms of safety, and identifies the most efficient approaches used regarding methodology, data collection and driving metrics. Results show that K-means and Neural Networks are the most commonly used methodologies for driver profile identification, and Dynamic Time Warping for driving pattern detection. Most studies discovered driver profiles related to aggressiveness, considering mainly speed and acceleration as driving metrics. Based on the gaps and challenges identified, this paper provides a new framework for combining microscopic and macroscopic driving behavior analysis, instead of examining them separately as is the state-of-theart. Such combined results can potentially improve the development of traffic risk models, which could be exploited in applications that monitor drivers in real-time and provide feedback. These models will represent human behavior more accurately, which can eventually lead to the recognition of "optimal" human driving patterns that Automated Vehicles (AV) could 'mimic' to become safer.

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“…Ejercito et al conducted a systematic review that compared various simulation modeling platforms, including MATSim, SUMO, Aimsun, PTV Vissim, and GAMA [43]. Evacuation simulation has an advantage in continuously analyzing the evacuation process with high precision [44]. SUMO simulation offers a significant advantage due to its free license that allows SUMO to be used in the traffic simulation process.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Urban-Scale Evacuation Strategies Through Disaster Victim Aggregation Modification

Asriandi Ekaputra,

Kee,

Yee

2024

IEEE Access

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Game Theory-Based Ramp Merging for Mixed Traffic With Unity-SUMO Co-Simulation

Cited by 34 publications

References 33 publications

A Systematic Survey of Control Techniques and Applications in Connected and Automated Vehicles

A Systematic Survey of Control Techniques and Applications in Connected and Automated Vehicles

Driver Profile and Driving Pattern Recognition for Road Safety Assessment: Main Challenges and Future Directions

Optimizing Urban-Scale Evacuation Strategies Through Disaster Victim Aggregation Modification

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