5G for Vehicular Use Cases: Analysis of Technical Requirements, Value Propositions and Outlook

The fifth generation (5G) of wireless networks promises to meet the stringent requirements of vehicular use cases that cannot be supported by previous technologies. However, the stakeholders of the automotive industry (e.g., car manufacturers and road operators) are still skeptical about the capability of the telecom industry to take the lead in a market that has been dominated by dedicated intelligent transport systems (ITS) deployments. In this context, this paper constructs a framework where the potential of 5G to support different vehicular use cases is thoroughly examined under a common format from both the technical and business perspectives. From the technical standpoint, a storyboard description is developed to explain when and how different use case scenarios may come into play (i.e., pre-conditions, service flows and post-conditions). Then, a methodology to trial each scenario is developed including a functional architecture, an analysis of the technical requirements and a set of target test cases. From the business viewpoint, an initial analysis of the qualitative value perspectives is conducted considering the stakeholders, identifying the pain points of the existing solutions, and highlighting the added value of 5G in overcoming them. The future evolution of the considered use cases is finally discussed

Proactive Risk Navigation System for Real-World Urban Intersections

We consider the problem of intelligently navigating through complex traffic. Urban situations are defined by the underlying map structure and special regulatory objects of e.g. a stop line or crosswalk. Thereon dynamic vehicles (cars, bicycles, etc.) move forward, while trying to keep accident risks low. Especially at intersections, the combination and interaction of traffic elements is diverse and human drivers need to focus on specific elements which are critical for their behavior. To support the analysis, we present in this paper the so-called Risk Navigation System (RNS). RNS leverages a graph-based local dynamic map with Time-To-X indicators for extracting upcoming sharp curves, intersection zones and possible vehicle-to-object collision points. In real car recordings, recommended velocity profiles to avoid risks are visualized within a 2D environment. By focusing on communicating not only the positional but also the temporal relation, RNS potentially helps to enhance awareness and prediction capabilities of the user

ICT Infrastructure for Cooperative, Connected and Automated Transport in Transition Areas

One of the challenges of automated road transport is to manage the coexistence of conventional and highly automated vehicles, in order to ensure an uninterrupted level of safety and efficiency. Vehicles driving at a higher automation level may have to change to a lower level of automation in a certain area under certain circumstances and certain (e.g. road and weather) conditions. The paper targets the transition phases between different levels of automation. It will review related research, introduce a concept to investigate automation level changes, present some recent research results, i.e. assessing key performance indicators for both analysing driver behaviour and traffic management in light of autonomous vehicles, an initial simulation architecture, and address further research topics on investigation of the traffic management in such areas (called "Transition Areas") when the automation level changes, and development of traffic management procedures and protocols to enable smooth coexistence of automated, cooperative, connected vehicles and conventional vehicles, especially in an urban environment

Open Platforms for Connected Vehicles

L'abstract è presente nell'allegato / the abstract is in the attachmen

Automating HAZOP studies using D-higraphs

In this paper, we present the use of D-higraphs to perform HAZOP studies. D-higraphs is a formalism that includes in a single model the functional as well as the structural (ontological) components of any given system. A tool to perform a semi-automatic guided HAZOP study on a process plant is presented. The diagnostic system uses an expert system to predict the behavior modeled using D-higraphs. This work is applied to the study of an industrial case and its results are compared with other similar approaches proposed in previous studies. The analysis shows that the proposed methodology fits its purpose enabling causal reasoning that explains causes and consequences derived from deviations, it also fills some of the gaps and drawbacks existing in previous reported HAZOP assistant tools

Performance analysis of V2X technologies 802.11p and LTE-PC5

The concept of Digital Twin (DT) has been broadly adopted in the Industry 4.0, in the healthcare and in the Smart Cities. It represents a digital model of the reality where it is possible to test and evaluate different actions before implementing them into the real world. In the context of Smart City, the digital copy of the city includes the representation of the road infrastructure, vehicles, pedestrians, .... Its main objectives are to ease the road maintenance, to provide the means for mobility simulations, and to create traffic information management systems. This prNowadays, Vehicular-to-Everything (V2X) communications are becoming an essential element to improve safe driving conditions and autonomous driving. This thesis presents a comparison of two V2X communication technologies: IEEE 802.11p, and Cellular-V2X. The objective of this study is to evaluate the performance of both technologies in terms of the Medium Acces Control (MAC) layer, especially in a congested environment. Therefore, we analyze the different schemes used on these technologies to access shared channel resources and avoid interferences. The study is conducted using several simulation tools: SUMO which allows us to create personalized scenarios, and OMNeT++ used to simulate the network and transmit all the V2X messages between the vehicles. With SUMO we created a highway scenario that can support a high density of vehicles. And OMNeT++ is used to change the main simulation parameters, and obtain results such as all the packets received and sent through the network. Finally, we defined some performance metrics to analyze the results and observe how the technologies react over a congested scenario, with high densities of vehicles

Mobility mining for time-dependent urban network modeling

170 p.Mobility planning, monitoring and analysis in such a complex ecosystem as a city are very challenging.Our contributions are expected to be a small step forward towards a more integrated vision of mobilitymanagement. The main hypothesis behind this thesis is that the transportation offer and the mobilitydemand are greatly coupled, and thus, both need to be thoroughly and consistently represented in a digitalmanner so as to enable good quality data-driven advanced analysis. Data-driven analytics solutions relyon measurements. However, sensors do only provide a measure of movements that have already occurred(and associated magnitudes, such as vehicles per hour). For a movement to happen there are two mainrequirements: i) the demand (the need or interest) and ii) the offer (the feasibility and resources). Inaddition, for good measurement, the sensor needs to be located at an adequate location and be able tocollect data at the right moment. All this information needs to be digitalised accordingly in order to applyadvanced data analytic methods and take advantage of good digital transportation resource representation.Our main contributions, focused on mobility data mining over urban transportation networks, can besummarised in three groups. The first group consists of a comprehensive description of a digitalmultimodal transport infrastructure representation from global and local perspectives. The second groupis oriented towards matching diverse sensor data onto the transportation network representation,including a quantitative analysis of map-matching algorithms. The final group of contributions covers theprediction of short-term demand based on various measures of urban mobility

Mobility mining for time-dependent urban network modeling

170 p.Mobility planning, monitoring and analysis in such a complex ecosystem as a city are very challenging.Our contributions are expected to be a small step forward towards a more integrated vision of mobilitymanagement. The main hypothesis behind this thesis is that the transportation offer and the mobilitydemand are greatly coupled, and thus, both need to be thoroughly and consistently represented in a digitalmanner so as to enable good quality data-driven advanced analysis. Data-driven analytics solutions relyon measurements. However, sensors do only provide a measure of movements that have already occurred(and associated magnitudes, such as vehicles per hour). For a movement to happen there are two mainrequirements: i) the demand (the need or interest) and ii) the offer (the feasibility and resources). Inaddition, for good measurement, the sensor needs to be located at an adequate location and be able tocollect data at the right moment. All this information needs to be digitalised accordingly in order to applyadvanced data analytic methods and take advantage of good digital transportation resource representation.Our main contributions, focused on mobility data mining over urban transportation networks, can besummarised in three groups. The first group consists of a comprehensive description of a digitalmultimodal transport infrastructure representation from global and local perspectives. The second groupis oriented towards matching diverse sensor data onto the transportation network representation,including a quantitative analysis of map-matching algorithms. The final group of contributions covers theprediction of short-term demand based on various measures of urban mobility

Design of an adaptive congestion control protocol for reliable vehicle safety communication

[no abstract

二分決定図と空間行動粒度に基づくローカルダイナミックマップを実装可能にする手法に関する研究

Autonomous vehicles (AVs) have been increasing rapidly on the road in recent years. However, the safety of AVs is of significant concern, which we must ensure. AVs use sensor information to achieve autonomy, but sensors such as cameras and lidar have limitations, and vehicles cannot rely on them entirely for safe navigation. To assist AVs with static information, high-definition maps (HD maps) can facilitate the complex static details of the surrounding for safe autonomy. However, we can model complex static information using HD maps for navigation; detecting and maintaining the traffic participant’s dynamic information using sensors of the ego vehicle alone is still a significant concern for safe navigation. In such a situation of sensing limitations, Cooperative Intelligent Transport Systems (C-ITS) is one approach to facilitate vehicle navigation through sharing information between the traffic participants. The C-ITS approach has various Intelligent transportation system (ITS) station units, namely Personal, Vehicle, Road-side and Central ITS station units. A Local Dynamic Map (LDM) is a critical component in any ITS station’s facilities layer. LDM is one way to maintain static and dynamic information of the traffic participants in a consistent geometrical way. It is a necessary facility in C-ITS to share sensor information between participating traffic agents. Moreover, it maintains information about the objects that are either part of the traffic or influenced by it. The International Organization for Standardization (ISO) and European Telecommunications Standards Institute (ETSI) have also made standardization efforts. Since its inception in the SAFESPOT project, implementations of LDM have been mostly four-layer data organizations. Where Layer 1 and Layer 2 maintain static information and transient static information. Then, Layer 3 and Layer 4 contain transient dynamic and highly dynamic data. Depending upon the requirement, the LDM community realized memory-based or database-based LDM. We utilized the decision diagram to enhance the safety aspect of the traffic participants in the memory/ database-based LDM setup. We utilized Shared Binary Decision Diagram (SBDD) and Geohash granular properties to detect the near-miss situation, i.e. when vehicles come very close. However, besides DynaMap, there is also a common understanding since the SAFESPOT project introduced LDM to use the database and supported query language to retrieve data from the LDM. Hence, most implementations use different databases and query languages to execute it. Although, the LDM community has explored LDM depending on the database variants. Nevertheless, remarkably less emphasis has been given to the type of data stored in the LDM. This thesis attempted to fill this gap in the LDM to enhance the moving vehicle’s safety aspect. We proposed a novel method of data representation for vehicle future geographical occupancy information using a binary decision diagram (BDD). We show that sharing BDD-based information is consistent with the C-ITS nature of the data sharing since the algebraic operation between the exchanged BDDs can confirm the possibility of future interaction. We calculated potential future occupancy using Kamm’s circle, shown in the ROS-based simulator and modified the mid-point circle generation algorithm to find the BDD representing the set of Geohash enclosing the Kamm’s circle. We also reported data insertion and collision avoidance check time of the linked list-based BDD on PostgreSQL database-based LDM.九州工業大学博士学位論文 学位記番号：生工博甲第449号 学位授与年月日：令和4年9月26日1 Introduction|2 Literature Review|3 Methodology|4 Results|5 Discussion|6 Summary九州工業大学令和4年