Learn to Bet: Using Reinforcement Learning to Improve Vehicle Bids in Auction-Based Smart Intersections

Abstract

With the advent of IoT, cities will soon be populated by autonomous vehicles and managed by intelligent systems capable of actively interacting with city infrastructures and vehicles. In this work, we propose a model based on reinforcement learning that teaches to autonomous connected vehicles how to save resources while navigating in such an environment. In particular, we focus on budget savings in the context of auction-based intersection management systems. We trained several models with Deep Q-learning by varying traffic conditions to find the most performance-effective variant in terms of the trade-off between saved currency and trip times. Afterward, we compared the performance of our model with previously proposed and random strategies, even under adverse traffic conditions. Our model appears to be robust and manages to save a considerable amount of currency without significantly increasing the waiting time in traffic. For example, the learner bidder saves at least 20% of its budget with heavy traffic conditions and up to 74% in lighter traffic with respect to a standard bidder, and around three times the saving of a random bidder. The results and discussion suggest practical adoption of the proposal in a foreseen future real-life scenario.

Leveraging 5G Technology to Investigate Energy Consumption and CPU Load at the Edge in Vehicular Networks

“…Internet of Vehicles (IoV) technology is at the heart of this evolution, integrating communication, control, and calculation technologies to significantly enhance road utilization in urban transportation systems. This advancement contributes to heightened transportation efficiency, reduced traffic accidents, lower energy consumption, and the realization of sustainable development within the transportation industry [2][3][4][5][6][7][8][9].…”