Cooperative Intersection with Misperception in Partially Connected and Automated Traffic

Li, Chenghao; Hu, Zhiqun; Lu, Zhaoming; Wen, Xiangming

doi:10.3390/s21155003

Cited by 3 publications

(2 citation statements)

References 38 publications

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“…The integration of information technologies and protocols enables CAVs to exchange information seamlessly with each other. In a CAV platoon, the control system operates the vehicle using locally sensed information and information shared among vehicles [ 36 ]. A car-following model can be used to describe the vehicle’s longitudinal dynamics.…”

Section: Methodsmentioning

confidence: 99%

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Exploring Safety–Stability Tradeoffs in Cooperative CAV Platoon Controls with Bidirectional Impacts

Wei,

2024

Sensors

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Advanced sensing technologies and communication capabilities of Connected and Autonomous Vehicles (CAVs) empower them to capture the dynamics of surrounding vehicles, including speeds and positions of those behind, enabling judicious responsive maneuvers. The acquired dynamics information of vehicles spurred the development of various cooperative platoon controls, particularly designed to enhance platoon stability with reduced spacing for reliable roadway capacity increase. These controls leverage abundant information transmitted through various communication topologies. Despite these advancements, the impact of different vehicle dynamics information on platoon safety remains underexplored, as current research predominantly focuses on stability analysis. This knowledge gap highlights the critical need for further investigation into how diverse vehicle dynamics information influences platoon safety. To address this gap, this research introduces a novel framework based on the concept of phase shift, aiming to scrutinize the tradeoffs between the safety and stability of CAV platoons formed upon bidirectional information flow topology. Our investigation focuses on platoon controls built upon bidirectional information flow topologies using diverse dynamics information of vehicles. Our research findings emphasize that the integration of various types of information into CAV platoon controls does not universally yield benefits. Specifically, incorporating spacing information can enhance both platoon safety and string stability. In contrast, velocity difference information can improve either safety or string stability, but not both simultaneously. These findings offer valuable insights into the formulation of CAV platoon control principles built upon diverse communication topologies. This research contributes a nuanced understanding of the intricate interplay between safety and stability in CAV platoons, emphasizing the importance of information dynamics in shaping effective control strategies.

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Section: Methodsmentioning

confidence: 99%

“…For the spacing bidirectional IDM, by applying the partial differential equations in Equation (35) to Equation (10), we obtain Equation (36).…”

Section: Non-linear Car-following Modelmentioning

confidence: 99%

Exploring Safety–Stability Tradeoffs in Cooperative CAV Platoon Controls with Bidirectional Impacts

Wei,

2024

Sensors

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Cooperative Decision-Making for Mixed Traffic at an Unsignalized Intersection Based on Multi-Agent Reinforcement Learning

et al. 2023

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Despite rapid advances in vehicle intelligence and connectivity, there is still a significant period in mixed traffic where connected, automated vehicles and human-driven vehicles coexist. The behavioral uncertainty of human-driven vehicles makes decision-making a challenging task in an unsignalized intersection scenario. In this paper, a decentralized multi-agent proximal policy optimization (MAPPO) based on an attention representations algorithm (Attn-MAPPO) was developed to make joint decisions at an intersection to avoid collisions and cross the intersection effectively. To implement this framework, by exploiting the shared information, the system was modeled as a model-free, fully cooperative, multi-agent system. The vehicle employed an attention module to extract the most valuable information from its neighbors. Based on the observation and traffic rules, a joint policy was identified to work more cooperatively based on the trajectory prediction of all the vehicles. To facilitate the collaboration between the vehicles, a weighted reward assignment scheme was proposed to focus more on the vehicles approaching intersections. The results presented the advantages of the Attn-MAPPO framework and validated the effectiveness of the designed reward function. Ultimately, the comparative experiments were conducted to demonstrate that the proposed approach was more adaptive and generalized than the heuristic rule-based model, which revealed its great potential for reinforcement learning in the decision-making of autonomous driving.

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Simulation Analysis of Capacity Evaluation of Bus Stops under Connected and Automated Vehicles Environment

Yang

Xue

2023

Applied Sciences

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The application of connected and automated vehicles (CAVs) technology has changed the operation characteristics of vehicles. Investigating the traffic capacity of bus stops under a CAVs environment can allocate traffic flow more reasonably, which is effective in alleviating traffic congestion. Therefore, this paper proposes a method that can be used to evaluate the traffic capacity of bus stops under a CAVs environment. First, two evaluation indexes, failure duration time (FD) and forced lane-changing rate (FLR) are proposed. Second, the simulation scheme with ten scenarios is determined, and simulation experiments are conducted. Then, the relationships between FD, FLR, and traffic flow under different penetration rates of CAVs are analyzed. Finally, the relationship models between FD, FLR, and traffic capacity are fitted to verify their validity for traffic capacity analysis. Additionally, a predictive model is proposed for estimating capacity under a CAVs environment using indicators from HV traffic flow. Results indicate that: (i) FD and FLR both positively correlate with capacity, and perform well in capacity evaluation of bus stops; (ii) FD and FLR can be utilized to predict the capacity under a CAVs environment; (iii) the higher the penetration rate of CAVs, the smaller the impact of the bus failure phenomenon and forced lane change on traffic flow.

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Cooperative Intersection with Misperception in Partially Connected and Automated Traffic

Cited by 3 publications

References 38 publications

Exploring Safety–Stability Tradeoffs in Cooperative CAV Platoon Controls with Bidirectional Impacts

Exploring Safety–Stability Tradeoffs in Cooperative CAV Platoon Controls with Bidirectional Impacts

Cooperative Decision-Making for Mixed Traffic at an Unsignalized Intersection Based on Multi-Agent Reinforcement Learning

Simulation Analysis of Capacity Evaluation of Bus Stops under Connected and Automated Vehicles Environment

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