Legal and ethical implications of applications based on agreement technologies: the case of auction-based road intersections

Agreement Technologies refer to a novel paradigm for the construction of distributed intelligent systems, where autonomous software agents negotiate to reach agreements on behalf of their human users. Smart Cities are a key application domain for Agreement Technologies. While several proofs of concept and prototypes exist, such systems are still far from ready for being deployed in the real-world. In this paper we focus on a novel method for managing elements of smart road infrastructures of the future, namely the case of auction-based road intersections. We show that, even though the key technological elements for such methods are already available, there are multiple non-technical issues that need to be tackled before they can be applied in practice. For this purpose, we analyse legal and ethical implications of auction-based road intersections in the context of international regulations and from the standpoint of the Spanish legislation. From this exercise, we extract a set of required modifications, of both technical and legal nature, which need to be addressed so as to pave the way for the potential real-world deployment of such systems in a future that may not be too far away

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

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

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Road transportation is fundamental for the movement of individuals and goods, also contributing to economic development. A significant contributor to urban road congestion is poor intersection control using conventional traffic signals. In this work, we present a decentralized multi-agent system mechanism for road intersection management for connected autonomous vehicles, including the coordination of platoon formations. We propose a reservation-based mechanism able to maximize the overall vehicle throughput at intersections. The study introduces i) auctions as an alternative to the First-Come-First-Serve policy for assigning reservations to vehicles and ii) a method for resolving disputes between conflicting reservations. The results demonstrate the benefits of using platooning for improving throughput and the average delay in intersection control. The distributed nature of the approach increases scalability by shifting the majority of the computing burden from the intersection manager to the driving agents.info:eu-repo/semantics/publishedVersio