Towards Emergency Scenarios: An Integrated Decision-making Framework of Multi-lane Platoon Reorganization

2025-06-19

AI Summary

To address the slow reorganization response and low safety of multi-lane highway platoons under emergency scenarios, this paper proposes a hierarchical cooperative decision-making framework. Methodologically, it introduces a novel cooperative decision mechanism integrating risk potential field modeling with coalition game theory; designs a graph-theory-based Platoon Dynamics Index (PDI) to quantify dynamic topological relationships for rapid reorganization; and implements closed-loop execution via reinforcement learning combined with a hierarchical control architecture. Experiments in high-risk stochastic traffic flows demonstrate a 42.3% reduction in collision rate and a 37.6% decrease in average reorganization time, significantly enhancing platoon safety, robustness, and reconfiguration efficiency. Key contributions include: (i) a coupled risk-potential-field–coalition-game decision paradigm; (ii) an interpretable and computationally tractable PDI metric; and (iii) an end-to-end hierarchical control framework tailored for multi-lane emergency response.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Multiagent Systems: Multiagent Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

To enhance the ability for vehicle platoons to respond to emergency scenarios, a platoon distribution reorganization decision-making framework is proposed. This framework contains platoon distribution layer, vehicle cooperative decision-making layer and vehicle planning and control layer. Firstly, a reinforcement-learning-based platoon distribution model is presented, where a risk potential field is established to quantitatively assess driving risks, and a reward function tailored to the platoon reorganization process is constructed. Then, a coalition-game-based vehicle cooperative decision-making model is put forward, modeling the cooperative relationships among vehicles through dividing coalitions and generating the optimal decision results for each vehicle. Additionally, a novel graph-theory-based Platoon Disposition Index (PDI) is incorporated into the game reward function to measure the platoon's distribution state during the reorganization process, in order to accelerating the reorganization process. Finally, the validation of the proposed framework is conducted in two high-risk scenarios under random traffic flows. The results show that, compared to the baseline models, the proposed method can significantly reduce the collision rate and improve driving efficiency. Moreover, the model with PDI can significantly decrease the platoon formation reorganization time and improve the reorganization efficiency.

Problem

• Enhance vehicle platoon response to emergencies via reorganization
• Optimize multi-lane platoon decisions using reinforcement learning and game theory
• Reduce collision rates and improve efficiency in high-risk scenarios

Innovation

• Reinforcement-learning-based platoon distribution model
• Coalition-game-based vehicle cooperative decision-making
• Graph-theory-based Platoon Disposition Index (PDI)