Path Planning Strategy Based on Principal Component Federation for Multi-Agent in Connected Vehicles

Path planning is an important mean to alleviate traffic congestion and reduce travel cost in the internet of vehicles. Existing path planning strategies primarily rely on shortest path or single-agent algorithms. However, they encounter challenges related to global dynamic coordination safety and resource constraints. Therefore, we propose a Multi-Agent dynamic Path Planning strategy based on the Principal component Federation (MA3PF) to address the these challenges. In this strategy, we introduce the Heuristic Multi-Agent Deep Policy Gradient algorithm (H-MADPG), which incorporates future traffic states and leverages shared agent experiences through migration learning. This approach ensures fast convergence and addresses resource limitations through a combination of centralized evaluation and distributed decision-making. Next, we propose the Adaptive Client-based Principal component Federation learning algorithm (ACPFed) for real-time prediction of future traffic flow. This algorithm utilizes principal component analysis to select model parameters and incorporates Bayesian optimization to dynamically determine client weights. These enhancements improve global coordination security and reduce communication overhead. Experimental results on real and simulated datasets demonstrate that our proposed MA3PF method outperforms existing agent algorithms, such as DARP and MAPF. It achieves superior route planning in complex environments, resulting in a reduction of travel distance by 21.90% and time loss by 39.41%. Additionally, MA3PF improves the real-time prediction accuracy of future traffic states while achieving a significant 70% reduction in communication cost. Note to Practitioners—This paper was motivated by the problem of holding multi-vehicle path planning. Existing path planning approaches only consider path planning for a single vehicle, which 1) fail to consider the challenges posed by resource constraints and data security in multi-vehicle planning 2) neglect the influence of future traffic states on path planning. This paper suggests a new dynamic path planning strategy, which incorporates real-time information about future traffic states, ensures data security, and reduces communication overhead using a specially designed ACPFed algorithm, simultaneously leveraging transfer learning and distributed decision-making to address resource constraints. In this paper, we present a mathematical characterization of the multi-vehicle path planning problem, incorporating future traffic states, and analyze the problem to derive an effective heuristic scheme MA3PF. The experimental results on real and simulated datasets demonstrate that the proposed MA3PF achieves significant improvements in terms of route distance and time loss compared to the traditional agent algorithm. In future research, we will explore more complex road environments and incorporate the effects of safety attacks on dynamic agent path planning.