Fully autonomous intersections, where traffic lights are not needed, are a long-term goal in urban traffic planning. In these types of intersections, the priority rules and the order in which vehicles can cross the intersection are determined by wireless communication. Eliminating the human factor can lead to fewer incidents; however, to achieve this, the system must operate perfectly. In the case of autonomous intersections, communication between vehicles and infrastructure must be flawless, as must the execution of the desired maneuvers (for example, avoiding emergency situations). The aim of this research is to analyze autonomous vehicles, intersections, control methods, and how failures in different components of the specified infrastructure can lead to safety and security issues. Firstly, it is necessary to categorize the different types and elements of autonomous intersections and control methods. This includes analyzing the concepts and components of centralized and decentralized intersections, the components of self-driving vehicles, communication protocols, and the topics of safety, security, and cybersecurity in autonomous systems. Potential failures are identified using the Failure Mode and Effect Analysis (FMEA) method, which categorizes and evaluates the impact of different failures in the intersection system. This analysis provides an overview of potential failures and pinpoints components that pose the highest risk. In addition, this paper conducts a simulation-based analysis to assess the severity of some critical failures. Providing comprehensive solutions to such failures is beyond the scope of this work.

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.

Estimating traffic flow states at unmeasured urban locations provides a cost-efficient solution for many ITS applications. In this work, a geostatistical framework, kriging is extended in such a way that it can both estimate and predict traffic volume and speed at various unobserved locations, in real-time. In the paper, different distance metrics for kriging are evaluated. Then, a new, data-driven one is formulated, capturing the similarity of measurement sites. Then, with multidimensional scaling the distances are transformed into a hyperspace, where the kriging algorithm can be used. As a next step, temporal dependency is injected into the estimator via extending the hyperspace with an extra dimension, enabling for short horizon traffic flow prediction. Additionally, a temporal correction is proposed to compensate for minor changes in traffic flow patterns. Numerical results suggest that the spatio-temporal prediction can make more accurate predictions compared to other distance metric-based kriging algorithms. Additionally, compared to deep learning, the results are on par while the algorithm is more resilient against traffic pattern changes.

Road traffic simulation is becoming more important as the complexity and multi-modality of traffic grow with the evolution of novel transportation and automotive technologies. There are several ways to mimic traffic dynamics. However, realistic modeling traffic has a clear trade-off between accuracy and simulation processing time. Obviously, the more detailed traffic dynamics are sought, the more calculation is needed. The paper offers a compromise solution introducing a novel mesoscopic traffic simulation framework applicable on an arbitrary network level. The mathematical background of the simulation is provided as the extension of the shockwave profile model (SPM), distinguishing three continuous traffic states on a link. Extension of the model is done by means of a weighted directed graph where the dynamic edge weights are the queue lengths on each link. The model can handle both signalized and unsignalized road links. An important opportunity of the proposed network SPM approach is the analysis of traffic jam propagation and bottleneck detection. The presented mesoscopic simulator is validated based on both synthetic and real-world traffic networks using fleet traffic data. The simulated queue length in the mesoscopic simulation accurately matches the microscopic traffic simulation results.

Converged networks require new service protection functionalities to ensure reliable and robust communications. This paper provides insights into the evolution of deterministic communication and the importance of service protection in converged networks. We examine the per-packet-based replication/elimination service protection mechanism, ongoing standardization efforts, and potential areas for further development. We also explore the practical implementation of the service protection technique in real network scenarios, including its impact on operation and maintenance. The main contributions of the paper are: (1) new cloud-specific extensions to the service protection functionality, (2) introduction of redundancy domains to avoid replication/elimination-related failure propagation, (3) new service protection specific extensions to the network operation toolset, and (4) new diagnostic capabilities to monitor the healthiness of the service protection functionalities. Additionally, we evaluate the effectiveness and operation of newly proposed service protection extensions based on experimental results from a real 5G testbed.

Autonomous Mobile Robots (AMRs) and mobile manipulators are becoming increasingly popular in industrial use cases, especially for flexible lineless assembly systems. These use cases require wireless communication with high reliability and jitter-minimized bounded latency. This paper describes an industrial use case, where edge-controlled AMRs collaboratively perform various screw installation tasks on a truck chassis. We integrate Time Sensitive Networking (TSN) features such as time synchronization and IEEE 802.1CB based Frame Replication and Elimination for Reliability (FRER) scheme with 5G wireless communication to realize this use case. Moreover, we apply hold and forward buffer (HFB) mechanism to minimize communication jitters. This paper highlights the performance benefits of the aforementioned TSN features for 5G communication in the edge-controlled AMR use case. Our empirical evaluation with over-the-air performance results obtained on an industrial shopfloor brings significant insights on using 5G for edge-controlled robotic use cases.