Towards Sustainable Distributed Computing: Integrating Energy Costs and Benefits for Optimal Growth in Traffic Autonomy

Towards Sustainable Distributed Computing: Integrating Energy Costs and Benefits for Optimal Growth in Traffic Autonomy
Junyue Jiang, Xianglong Wang, Daniel M. Kammen, Hao Frank Yang

Abstract
Computing-enabled Automated Traffic Systems (ATS) are rapidly gaining popularity for autonomous vehicles and smart infrastructure, leading to increased power consumption and energy costs. This paper first demonstrates that the computational demands of ATS substantially elevate energy consumption, driven by the intensive sensing and computing functions powered by deep neural networks. By integrating state-level energy portfolios and policies, we develop a new accounting framework and dynamic model to predict the carbon emissions trajectory from 2025 to 2100 for California and Ohio to formulate both short- and long-term proactive emission mitigation strategies.