Joint Travel Route Optimization Framework for Platooning

2025-04-10

AI Summary

This paper addresses the path coordination optimization problem in large-scale vehicle platooning deployment. Methodologically, it proposes a system-level centralized platoon formation and joint path planning framework. A multi-objective travel cost function is formulated—integrating fuel consumption, driver fatigue, and travel time—and, for the first time, explicitly incorporates regulatory constraints on continuous driving duration. Leveraging a network graph representation and V2X-enabled cooperative architecture, the framework employs an A*-guided heuristic search combined with Dijkstra’s algorithm for efficient solution computation. Key contributions include: (1) a platoon-adoption-oriented cooperative path planning paradigm that bridges the gap between isolated vehicle routing and fully coordinated platooning; and (2) unified modeling of regulatory compliance and multi-objective optimization. Experimental results demonstrate a 14% average reduction in travel cost over conventional single-vehicle path planning under long-haul scenarios.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Multiagent Planning
• Search and Optimization: Combinatorial Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Platooning represents an advanced driving technology designed to assist drivers in traffic convoys of varying lengths, enhancing road safety, reducing driver fatigue, and improving fuel efficiency. Sophisticated automated driving assistance systems have facilitated this innovation. Recent advancements in platooning emphasize cooperative mechanisms within both centralized and decentralized architectures enabled by vehicular communication technologies. This study introduces a cooperative route planning optimization framework aimed at promoting the adoption of platooning through a centralized platoon formation strategy at the system level. This approach is envisioned as a transitional phase from individual (ego) driving to fully collaborative driving. Additionally, this research formulates and incorporates travel cost metrics related to fuel consumption, driver fatigue, and travel time, considering regulatory constraints on consecutive driving durations. The performance of these cost metrics has been evaluated using Dijkstra's and A* shortest path algorithms within a network graph framework. The results indicate that the proposed architecture achieves an average cost improvement of 14 % compared to individual route planning for long road trips.

Problem

• Optimizes joint travel routes for platooning to enhance efficiency
• Reduces fuel consumption, driver fatigue, and travel time costs
• Compares centralized platoon planning with individual route strategies

Innovation

• Centralized platoon formation strategy for optimization
• Travel cost metrics including fuel and fatigue
• Dijkstra and A* algorithms for route evaluation