提出协同路线优化框架，让车队行驶更省油、更少疲劳。
Joint Travel Route Optimization Framework for Platooning
- 中心化策略统筹车队路线，协同规划降低整体成本。
- 相比单辆行车，长途行程平均节省14%油耗与疲劳成本。
- 适合智能交通系统研究者及自动驾驶车队设计人员。
编队驾驶是一种先进的驾驶技术，通过不同长度的车辆车队提升道路安全、减少驾驶员疲劳并提高燃油效率。先进的自动驾驶辅助系统推动了这一创新。近年来，编队驾驶强调在集中式与分布式架构中通过车联网技术实现协作。本研究提出一种协同路线规划优化框架，旨在通过系统级的集中式编队形成策略促进编队驾驶的普及，该策略被视为从个体驾驶过渡到完全协作驾驶的中间阶段。研究还构建了包含燃油消耗、驾驶员疲劳和行程时间的旅行成本指标，并考虑连续驾驶时长的法规限制。在基于网络图的框架下，采用Dijkstra和A*最短路径算法评估性能。结果表明，所提架构在长途行程中相较个体路线规划平均成本降低14%。
原文摘要 · Abstract (English)
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.
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