Towards Intelligent Adaptive Cruise Control: Integrating AI, Edge Computing, and V2V Communication for Urban Environments

A Scalable Framework for Safer and Smarter Urban Driving Automation

Abstract

Traditional Adaptive Cruise Control (ACC) systems offer significant safety and comfort benefits in highway scenarios but remain inadequate in the dynamic and unpredictable context of urban driving. This paper introduces an enhanced framework, termed Intelligent Adaptive Cruise Control (iACC), which integrates Artificial Intelligence (AI), Edge Computing, and Vehicle-to-Vehicle (V2V) communication to address urban mobility challenges. The iACC system utilizes reinforcement learning and predictive modeling for proactive decision-making, while edge computing ensures low-latency responses to environmental stimuli. V2V communication supports collaborative traffic behavior, facilitating smoother acceleration, safer navigation through pedestrian zones, and better adaptation to urban complexity. Simulation scenarios demonstrate the proposed system’s ability to outperform traditional ACC in response time, safety, and driver comfort. This work contributes to the advancement of urban autonomous mobility and presents a scalable foundation for future smart city transportation systems.