Radio Resource Management and Path Planning in Intelligent Transportation Systems via Reinforcement Learning for Environmental Sustainability

TL;DR

This paper proposes a reinforcement learning-based framework for radio resource management and path planning in connected vehicle systems, aiming to improve environmental sustainability by reducing travel time and energy consumption.

Contribution

It introduces a novel approach to optimize radio resource management to minimize age of information, enhancing path planning accuracy in intelligent transportation systems.

Findings

Lower AoI leads to more accurate road capacity estimation.

The framework reduces travel times and improves volume over capacity metrics.

Simulation results show promising performance improvements.

Abstract

Efficient and dynamic path planning has become an important topic for urban areas with larger density of connected vehicles (CV) which results in reduction of travel time and directly contributes to environmental sustainability through reducing energy consumption. CVs exploit the cellular wireless vehicle-to-everything (C-V2X) communication technology to disseminate the vehicle-to-infrastructure (V2I) messages to the Base-station (BS) to improve situation awareness on urban roads. In this paper, we investigate radio resource management (RRM) in such a framework to minimize the age of information (AoI) so as to enhance path planning results. We use the fact that V2I messages with lower AoI value result in less error in estimating the road capacity and more accurate path planning. Through simulations, we compare road travel times and volume over capacity (V/C) against different levels of AoI and demonstrate the promising performance of the proposed framework.