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

TL;DR

This paper presents an integrated decision-making framework for vehicle platoon reorganization in emergency scenarios, combining reinforcement learning, coalition game theory, and graph theory to improve safety and efficiency.

Contribution

It introduces a novel multi-layer framework with a risk assessment model, cooperative decision-making, and a new platoon disposition index for faster reorganization.

Findings

Reduces collision rate significantly.

Decreases platoon reorganization time.

Improves overall driving efficiency.

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.