AoI-Guaranteed Dynamic Route Planning for Connected Vehicles
TL;DR
This paper presents a novel dual- factor approach that integrates travel time estimation and radio resource availability into an innovative route-planning scheme for connected vehicles (CVs) and shows that AGDRP outperforms the baseline scheme, which solely focuses on travel time optimization.
Abstract
The advancement of Intelligent Transportation Sys- tems (ITS) has been significantly driven by progress in radio communication technology. Dynamic route planning, a key com- ponent of ITS, traditionally focuses on metrics such as route capacity and travel time. This paper presents a novel dual- factor approach that integrates travel time estimation and radio resource availability into an innovative route-planning scheme for connected vehicles (CVs). To address this dual-objective route planning challenge, we employ Deep Reinforcement Learning (DRL). Our approach, called AoI-Guaranteed Dynamic Route Planning (AGDRP), effectively balances travel time and Age of Information (AoI), enhancing route planning performance through adaptive learning over time. Simulation results demon- strate that AGDRP outperforms the baseline scheme, which solely focuses on travel time optimization. In fact, we show that incor- porating AoI minimization significantly enhances route planning performance beyond conventional travel-time-based approaches.
