The rapid evolution of vehicular communication has led to numerous new algorithms and applications based on this technology. Neglecting issues arising from wireless communication, such as the loss of information and delays, can result in problems such as reduced performance or compromised safety. However, while simulating V2X demands significant computational resources, it proves unsuitable for complex testing setups, including mixed-reality testing. This paper enhances V2X simulation by relying on an ecosystem based on SUMO, OMNeT++, Veins, and INET simulation tools. The proposed novel method introduces mesoscopic simulation in Vehicular Ad-hoc Networks to increase simulation performance to a level where real-time behavior is achievable. Meanwhile, it can also be beneficial in the acceleration of regular simulations. The presented solution introduces Meso nodes that are capable of aggregating communication across an entire traffic area, facilitated by a neural network function approximator. Results showed substantial performance gain while simulation accuracy was preserved.

As connectivity of road traffic grows, so does the concern for its reliability and security. On the other hand, real-world testing of large-scale vehicular ad-hoc networks would be extremely costly. This paper presents a mixed-reality proof-of-concept for evaluating the communication of real vehicles using standard V2X messages while simulating the rest of the traffic. Detailed simulation of large-scale communication networks is also a computationally complex task. To be able to achieve the desired real-time simulation performance for mixed-reality testing, a so-called mesoscopic communication node was introduced, fusing the communication of the simulated vehicles into a single entity. This single entity realizes a sensor spoofing on other communicating vehicles. To verify the feasibility of the proposed approach, real-worlds tests were carried out involving two cars equipped with V2X devices and a third device transmitting standard messages from the simulation to the real vehicles in an "informed" denial-of-service-like way. Test results show that the mesoscopic simulation can retain real-time performance while replicating the communication of over 100 vehicles. Real vehicles can extract these virtual messages and packet drops remain in a plausible range.

The present paper's focus is the V2X (Vehicle-to-Everything) communication between traffic light controller and road vehicles. After a brief review on the state-of-the-art V2X communication technologies and their feasibility for practical implementation, a brief description of their potential is discussed providing a working solution for SPaT/MAP (Signal Phase and Timing and MAP as intersection geometry) standard based communication, specifically for automotive proving ground usage. The main outcome of the proposed solution is a full y flexible traffic control system applying industrial PLC while ensuring a standardized V2X protocol. As a demonstrative HiL (Hardware-in-the-Loop) example, exploiting the elements of the traffic management system at the ZalaZONE Automotive Proving Ground, a GLOSA (Green Light Optimal Speed Advisory) scenario is presented with the developed SPaT/MAP V2X communication realizing a global optimum traffic control solution. The more, the whole framework is designed in a way that it is capable of using mixed reality components for testing purposes and realizing a digital twin for traffic lights. The method of virtualization for V2X communication is introduced using a co-simulation framework of three common software tools (SUMO, Veins, and OMNeT++).

Simulating automotive functions that rely on interaction with other vehicles (e.g., perception-based control or algorithms relying on inter-vehicular communication) created a demand for traffic simulation in the automotive field as well. Large-scale traffic simulation can be used to generate long, synthetic drive-cycles for EGO vehicles with realistic traffic. An EGO vehicle is defined as the vehicle the scenario revolves around, presumably running a control algorithm to be tested. On the other hand, simulating an entire district or city with thousands of vehicles present is superfluous and comes with a heavy computational burden while only the vicinity of the EGO vehicle is relevant. On the other hand, major traffic patterns can that could still influence the nearby traffic (e.g., traffic disruptions farther away) but can be simulated with lesser accuracy. Thus, simulation accuracy far from the EGO vehicle can be traded for simulation speed. This paper achieves this trade-off by co-simulating SUMO in microscopic and mesoscopic modes using Libsumo API. Microsimulated traffic is continuously spawned in an EGO-centered sub-network based on traffic states in the mesoscopic simulation. Simulation results in large urban scenarios suggest that the behavior of the EGO vehicle in terms of velocity distribution, headway distribution, and lane changes accurately matches pure microsimulation while simulation speed increased by $3-10$ times. This result assumes linear time complexity control algorithms with respect to the vehicle number and a single EGO vehicle. Reducing the number of microsimulated vehicles with co-simulation yields even larger simulation speed gains for more computationally complex algorithms. The aggregate (macroscopic) traffic parameters match for both the micro-, meso-, and co-simulated cases. Thus coupling the two simulators does not distort the mesoscopic simulation.

The rapid evolution of vehicle-to-vehicle and vehicle-to-infrastructure communication opens doors to various control algorithms, one particular domain being intersection control. Several researchers have proposed communication-based intersection control algorithms omitting traditional traffic lights with the promise of enhanced throughput and improved traffic safety. On the other hand, the majority of these algorithms ignore the uncertainties and delays of communication and overlook new cyberattack vectors that are opened by connected traffic. The present study employs a highly detailed simulation of vehicular communication to highlight the sensitivity of different autonomous intersection control algorithms (both centralized and decentralized) to communication-related imperfections. The paper investigates five different algorithms borrowed from the literature in a comparative way. This research focuses on traffic-related parameters such as average speed, occupancy, and network throughput while also analyzing communication-related parameters (e.g., packet loss, computational demand) and considering possible attack vectors. Simulation results suggest that even the simplest control logic is sensitive to communication failures, degrading intersection throughput below traditional intersection control or even compromising traffic safety. For centralized algorithms, in the presence of noise, average speeds drop significantly, suggesting reduced intersection throughput and even gridlock for the First Come First Serve algorithm. Decentralized algorithms can be heavily affected by incoming message orders, which can lead to dangerous situations. During the simulations, multiple collisions were registered for the Monte-Carlo Tree Search algorithm. More complex algorithms that rely on accurate prediction on vehicle trajectories are more sensitive to noise and even produce accidents in the simulation. In conclusion, regardless of control architecture, the evaluated algorithms require additional fallback solutions and redundancies to retain traffic safety.

Digital twins of road surfaces support multiple engineering applications. Remote sensing technologies provide information from the entire surface of the pavement by high accuracy point clouds. Pavement errors and differences from designed geometry can be detected and assessed using such datasets, while OpenCRG models derived from point clouds support transportation applications. High-resolution CRG (Curved Regular Grid) models enable analyzing vehicle suspension systems in vehicle dynamics simulation environments. Furthermore, such models also support creating the digital twins of vehicle suspensions and improve the development and research of models related to vehicle dynamics. The paper presents how the suspension digital twin was obtained applying a genetic algorithm and how it was assessed. The quality of raw data and that of the derived methods are analyzed in the case of multiple mapping technologies (terrestrial, mobile, and aerial laser scanning). CRG models were created from all datasets, and their applicability was investigated to support vehicle simulations with high accuracy demand. Other important vehicle-related use cases are also mentioned in the paper.

Autonomous intersection control received particular interest recently. Theoretically, such traffic light-free controls can greatly improve the throughput of intersections with autonomous vehicles. The objective of this paper is to show how even a simple intersection control algorithm is susceptible to communication imperfections through detailed modeling of the communication layers. Safety and sensitivity analyses were carried out using a distributed control architecture, where no central control units are present. The control relies only on data contained in standard Cooperative Awareness Messages. The vehicle ad-hoc network communication is simulated with multiple layers considering many critical factors like signal propagation, noise, channel load, and even faulty units. Simulation results suggest that autonomous intersection control is robust against minor faults or communication errors. On the other hand, significant communication breakdowns lead to collisions.

Usage of simulation techniques like Vehicle-in-the-Loop, Scenario-in-the-Loop, and other mixed-reality systems are becoming inevitable in autonomous vehicle development, particularly in testing and validation. These methods rely on using digital twins, realistic representations of real vehicles, and traffic in a carefully rebuilt virtual world. Recreating them precisely in a virtual ecosystem requires many parameters of real vehicles to follow their properties in a simulation. This is especially true for vehicle dynamics, where these parameters have high impact on the simulation results. The paper's objective is to provide a method that can help reverse engineering a real car's suspension characteristics with the help of a genetic algorithm. A detailed description of the method is presented, guiding the reader through the whole process, including the meta-heuristic function's settings and how it interfaces with IPG Carmaker. The paper also presents multiple measurements, which can be effortlessly recreated without expensive devices or the need to disassemble any vehicle parts. Measurements are reproduced in two separate simulation tools with special scenarios providing an efficient way to analyze and verify the results. The provided method creates vehicle suspension characteristics with adequate quality, opening up the possibility to use them in the creation of digital twins or creating virtual traffic with realistic vehicle dynamics for high-quality visualization. Results show satisfying accuracy when tested with OpenCRG.