Efficient intersection management remains a critical challenge for Connected and Autonomous Vehicles (CAVs), especially under dynamic traffic conditions that require balancing safety, throughput, and responsiveness. Existing cooperative protocols, such as virtual platooning and rule-based scheduling, offer decentralized control but typically rely on fixed synchronization points and static sequencing rules, limiting their adaptability in real-time environments. In this study, we propose PSO-DCPVP, a novel hybrid framework that integrates Particle Swarm Optimization (PSO) within the Distributed Clearing Policy for Virtual Platooning (DCPVP). The key innovation lies in dynamically computing mobile synchronization points for each vehicle based on local traffic states, enabling more flexible and context-aware platoon coordination. We evaluate the framework in a custom multi-agent simulation environment under three traffic scenarios: low, moderate, and high demand over a 600 s simulation horizon. Results demonstrate that PSO-DCPVP significantly increases intersection throughput, exceeding 2.1 pcu/s in congested settings while reducing average delay to below 0.03 s. Compared to baseline strategies such as FIFS and DCPVP, PSO-DCPVP demonstrates strong potential for real-world deployment in intelligent transportation systems.

The integration of autonomous vehicles (AVs) into traffic road requires careful consideration of their ability to adapt to other road users. Among road users, pedestrians are particularly vulnerable and are likely to interact with AVs. While most studies have focused on pedestrian behavior in interactions with human-driven vehicles, the effects of AV presence and Vehicle-to-Pedestrian (V2P) communication on pedestrian behavior remain largely unexplored. This article explores the impact of these factors, as well as the influence of the learning process induced by the introduction of AVs into traffic, on pedestrian behavior. To this end, we analyze data from our experimental campaign involving 100 participants in a virtual reality (VR) environment designed to address this issue. This study provides a comprehensive analysis of pedestrian behavior, constituting a significant advancement in the understanding of pedestrian-autonomous vehicle interactions and contributing to the integration of AV.

As vehicle systems become increasingly connected and intelligent, insurance providers are turning to machine learning techniques to personalize billing based on individual driving behavior. This shift raises important questions about how to balance predictive performance with user privacy. In this paper, we present PrivFedProfiling, a decentralized privacy-preserving learning framework designed for use-based insurance (UBI) systems. Our method leverages Federated Learning (FL) to collaboratively train behavior models across distributed driver devices without transferring raw data. To further strengthen privacy, we integrate Differential Privacy (DP) and Homomorphic Encryption (HE) within the training process, protecting sensitive patterns in shared model updates. The proposed approach uses a Multilayer Perceptron (MLP) architecture and is validated using synthetic driving behavior data generated from the SUMO simulator. It offers a realistic yet controllable environment for testing. Results indicate that our method maintains high model accuracy while ensuring strong privacy guarantees, making it suitable for real-world deployment.

Pedestrians, as vulnerable road users, pose safety challenges for autonomous vehicles (AVs). Their behavior, often unpredictable and subject to change, complicates AV-pedestrian interactions. To address this uncertainty, AV s can enhance safety by communicating their planned trajectories to pedestrians. In this research, we explore the interaction between pedestrians and autonomous vehicles within an industrial environment, focusing on how communicative behavior from the vehicles influences pedestrians' physiology. We investigate the possibility of mea-suring biosignals while participants wear a VR headset and experiment a pedestrian crossing. Our preliminary study reveals subtle variations in delta rhythms when users immersed in VR simulations interact with AV s that either provide or withhold additional information.

This paper aims to contribute to the challenging issue of one microscopic simulation round for dynamic traffic assignment. It relays on the selfish behaviour of the vehicle agent that benefits from a more accurate estimation of its travel time. The main novelty is that, rather than considering the average travel times in the network links according to the present vehicles, the vehicle must first know when it can cross the nodes located at both extremities of the road. This is achieved through a negotiation between the vehicle agent and the node agents to book the crossing time. In order to assess this new paradigm, this paper compares it with well-known approaches in an elementary network. The result invites us to extend the approach to more general cases. A discussion of the opportunities and limitations of the approach extension is provided in this paper. One of the notable opportunities is that the proposed approach has a great potential to improve energy efficiency by exploring mobile navigation applications and connected autonomous vehicles.

Intersections are major bottlenecks for road traffic, as well as the origin of many accidents. Efficient management of traffic at intersections is required to ensure both safety and efficiency. Yet, the traditional solutions (static signs, traffic lights) are limited in their efficiency as they consider the flow of vehicles and not the vehicles at the microscopic level. By using inter-vehicular communication of connected vehicles, recent works have shown the possibility to have a great increase in the number of evacuated vehicles thanks to the possibility to give an individual right-of-way directly to each vehicle. In this context of intersections of cooperative vehicles, the scheduling of this right-of-way in order to maximize the throughput of the intersection is still a challenging task, with regard to the hybrid and dynamic aspects of the problem. In this paper, we propose an approach based on Deep Reinforcement Learning (DRL) to efficiently distribute the right-of-way to each vehicle. A Markov Decision Process model of intersections of cooperative vehicles, enabling the application of DRL, is proposed. The performance of the DRL-based scheduling is then compared with classic traffic lights, and with two state-of-the-art cooperative scheduling policies, showing the benefits of the approach (increase of the flow, reduction of CO2 emissions).

The technology of Connected and Autonomous Vehicles (CAV) is a hot topic of transportation systems, especially regarding platooning and the interaction with other road users. Considering traffic safety, many studies have been devoted to the exchange of information among various road users, such as CAVs and pedestrians. In a platooning scenario, when a pedestrian is detected by a CAV, the leader CAV shares the information with its followers to provide a safe and courteous environment thanks to its connectivity. However, the possibility to improve traffic efficiency while meeting the safety requirements has rarely been addressed in current research. Yet, in industrial areas, where automated vehicles and pedestrians frequently interact, combining safety and efficiency is crucial. The present paper addresses this challenge by first analyzing the intersection of CAVs and pedestrians in no-traffic-signal scenarios. The optimal state is proposed to reduce the time loss. Then, the paper uses a reinforcement learning-based method to make CAVs arrive at the optimal state, to improve traffic efficiency. The experimental results based on virtual reality show that the proposed method increases traffic efficiency while ensuring traffic safety.

In recent years, Connected and Automated Vehicle (CAV) related research has progressed considerably. This paper proposes an approach for finding the best itinerary to CAVs into a road network. If we consider only a single CAV, the problem turns into the shortest path problem according to the real-time traffic information. When it comes to a road network served by several CAVs, such as in Personal Rapid Transit (PRT), the problem of the traffic assignment equilibrium is raised. However, the known algorithms for solving such problems are greedy in terms of computation time and memory. In order to solve the problem, this paper introduces a new distributed approach, using multi-agent systems. We call the approach Mixed Node Reservation (MNR). Each CAV agent computes its itinerary by using an accurate estimation of its travel time through its connectivity to node agents of the network. To this end, two new key concepts are introduced. The first one is the node reservation. This allows to adjust the travel time between two nodes, by considering the acceleration time as well as the time spent into the next intersection. The second key concept is the spawned virtual CAVs. This aims to accurately estimate the future lost time generated by CAVs which are not yet in the network. Other approaches of the literature are simulated for comparisons. The experiment results show that MNR allows CAVs to share the network much more efficiently.

Connected and autonomous vehicles (CAV) is the development trend in the field of transportation systems. Recent studies show that the resources sharing between pedestrians and CAV is a big challenge. Considering traffic safety and efficiency at that sharing point not only requires a collision avoidance system but also more communicative behaviors of the CAV. More precisely, pedestrian needs to understand the intention of the incoming CAV whether it will cross first or not according to its speed profile. This paper uses the optimal trajectory control to provide CAV with a communicative behavior. A scenario where CAV and pedestrian cooperate together to cross a conflict zone is studied. A communicative CAV behavior is designed through an objective function. Hamiltonian analysis is used to derive the optimal control for the CAV. Based on Oculus virtual reality platform, the proposed approach is tested and the behavior of cars and pedestrians are studied. The tests show that this approach provides CAVs with a kind of automatic courtesy.

Recently, new research activities have emerged for controlling traffic. Since future vehicles will travel autonomously and communicate with their surrounding environment, they will then be able to negotiate the right-of-way at intersections [cooperative intersection management (CIM)] as well as reserve their itinerary (road reservation). Both concepts, i.e., CIM and road reservation, are very promising for relieving traffic congestion. The scope of this paper is to prevent deadlock under real-time conditions, by taking advantage of these two concepts. This paper presents an appropriate graph to model the network of intersections and a sufficient condition for obtaining deadlock-free traffic with a specified route from their origin to their destination. In addition, it proposes a hierarchical approach in which a network server, intersection servers, and vehicles contribute to improving the traffic condition. Simulations were performed on a network of 25 interconnected intersections, as well as, on a real urban network to prove the effectiveness of the model. The results are presented and discussed.

The recent ability of some vehicles to handle autonomously the lane change maneuvers, and the progressive equipment of roads and vehicles with ITS-G5 units motivate this paper to consider the case of road narrowing that requires a lane change because one lane is occupied by road works for maintenance, incidents and so on. This paper extends the approaches of cooperative speed synchronization at intersections. Because of the complexity of the overall system, it considers each automatic lane change as a mobile (unfixed) intersection in which vehicles synchronize their velocities. The wireless communication allows each vehicle to increase its field of view to negotiate its merging with the other equipped vehicles. Hence, the proposed approach introduces a kind of automatic “courtesy” between equipped vehicles. The paper defines the intersection point between each pair of vehicles and the suited protocol to safely reach the new lane. The protocol can be handled by the new work item (NWI) that has been created at ETSI to realize platooning and cooperative adaptive cruise control. Besides enhancing safety, the simulation results show that the main advantage of the approach is the energy saving by smoothing the traffic.

In this paper, we propose new controls for a simple intersection based on new information and communication system for intelligent vehicles. These controls are performed to take into account all vehicle arrivals individually. Hence, we consider that there is no traffic light planned by the city and vehicles negotiate their time of access between them or through an intelligent device embedded in the intersection. The intersection is modelled as a resource shared between vehicles of roads. Intersection control becomes to determine the best access order to the intersection for all vehicles which are approaching it. The objective is to evacuate all vehicles as soon as possible. This paper shows that negotiation between vehicles by means of well adapted approaches is efficient to improve traffic control at a simple intersection.

This paper proposes a model for invariant resource sharing problems in dioid algebra. A strong motivation for investigating the issue is the absence of a general systematic technique which can be used to tackle these problems.$(\min, +)$constraints have been developed to handle resource sharing in Discrete-Event Dynamic Systems. In particular, the part that can be modeled by a Timed Event Graph induce$(\min, +)$-linear equations which are constrained by the resource availability. The proposed algebraic model has been proved to describe the actual behavior of the systems dealt with. This paper will show two examples of systems that are modeled and controlled by means of this approach.