A comprehensive survey on cooperative intersection management for heterogeneous connected vehicles

Nowadays, with the advancement of technology, world is trending toward high mobility and dynamics. In this context, intersection management (IM) as one of the most crucial elements of the transportation sector demands high attention. Today, road entities including infrastructures, vulnerable road users (VRUs) such as motorcycles, moped, scooters, pedestrians, bicycles, and other types of vehicles such as trucks, buses, cars, emergency vehicles, and railway vehicles like trains or trams are able to communicate cooperatively using vehicle-to-everything (V2X) communications and provide traffic safety, efficiency, infotainment and ecological improvements. In this paper, we take into account different types of intersections in terms of signalized, semi-autonomous (hybrid) and autonomous intersections and conduct a comprehensive survey on various intersection management methods for heterogeneous connected vehicles (CVs). We consider heterogeneous classes of vehicles such as road and rail vehicles as well as VRUs including bicycles, scooters and motorcycles. All kinds of intersection goals, modeling, coordination architectures, scheduling policies are thoroughly discussed. Signalized and semi-autonomous intersections are assessed with respect to these parameters. We especially focus on autonomous intersection management (AIM) and categorize this section based on four major goals involving safety, efficiency, infotainment and environment. Each intersection goal provides an in-depth investigation on the corresponding literature from the aforementioned perspectives. Moreover, robustness and resiliency of IM are explored from diverse points of view encompassing sensors, information management and sharing, planning universal scheme, heterogeneous collaboration, vehicle classification, quality measurement, external factors, intersection types, localization faults, communication anomalies and channel optimization, synchronization, vehicle dynamics and model mismatch, model uncertainties, recovery, security and privacy

Design and evaluation of safety-critical applications based on inter-vehicle communication

Inter-vehicle communication has a potential to improve road traffic safety and efficiency. Technical feasibility of communication between vehicles has been extensively studied, but due to the scarcity of application-level research, communication\u27s impact on the road traffic is still unclear. This thesis addresses this uncertainty by designing and evaluating two fail-safe applications, namely, Rear-End Collision Avoidance and Virtual Traffic Lights

Towards Learning Feasible Hierarchical Decision-Making Policies in Urban Autonomous Driving

Modern learning-based algorithms, powered by advanced deep structured neural nets, have multifacetedly facilitated automated driving platforms, spanning from scene characterization and perception to low-level control and state estimation schemes. Nonetheless, urban autonomous driving is regarded as a challenging application for machine learning (ML) and artificial intelligence (AI) since the learnt driving policies must handle complex multi-agent driving scenarios with indeterministic intentions of road participants. In the case of unsignalized intersections, automating the decision-making process at these safety-critical environments entails comprehending numerous layers of abstractions associated with learning robust driving behaviors to allow the vehicle to drive safely and efficiently. Based on our in-depth investigation, we discern that an efficient, yet safe, decision-making scheme for navigating real-world unsignalized intersections does not exist yet. The state-of-the-art schemes lacked practicality to handle real-life complex scenarios as they utilize Low-fidelity vehicle dynamic models which makes them incapable of simulating the real dynamic motion in real-life driving applications. In addition, the conservative behavior of autonomous vehicles, which often overreact to threats which have low likelihood, degrades the overall driving quality and jeopardizes safety. Hence, enhancing driving behavior is essential to attain agile, yet safe, traversing maneuvers in such multi-agent environments. Therefore, the main goal of conducting this PhD research is to develop high-fidelity learning-based frameworks to enhance the autonomous decision-making process at these safety-critical environments. We focus this PhD dissertation on three correlated and complementary research challenges. In our first research challenge, we conduct an in-depth and comprehensive survey on the state-of-the-art learning-based decision-making schemes with the objective of identifying the main shortcomings and potential research avenues. Based on the research directions concluded, we propose, in Problem II and Problem III, novel learning-based frameworks with the objective of enhancing safety and efficiency at different decision-making levels. In Problem II, we develop a novel sensor-independent state estimation for a safety-critical system in urban driving using deep learning techniques. A neural inference model is developed and trained via deep-learning training techniques to obtain accurate state estimates using indirect measurements of vehicle dynamic states and powertrain states. In Problem III, we propose a novel hierarchical reinforcement learning-based decision-making architecture for learning left-turn policies at four-way unsignalized intersections with feasibility guarantees. The proposed technique involves an integration of two main decision-making layers; a high-level learning-based behavioral planning layer which adopts soft actor-critic principles to learn high-level, non-conservative yet safe, driving behaviors, and a motion planning layer that uses low-level Model Predictive Control (MPC) principles to ensure feasibility of the two-dimensional left-turn maneuver. The high-level layer generates reference signals of velocity and yaw angle for the ego vehicle taking into account safety and collision avoidance with the intersection vehicles, whereas the low-level planning layer solves an optimization problem to track these reference commands considering several vehicle dynamic constraints and ride comfort

Improving Vehicular ad hoc Network Protocols to Support Safety Applications in Realistic Scenarios

La convergencia de las telecomunicaciones, la informática, la tecnología inalámbrica y los sistemas de transporte, va a facilitar que nuestras carreteras y autopistas nos sirvan tanto como plataforma de transporte, como de comunicaciones. Estos cambios van a revolucionar completamente cómo y cuándo vamos a acceder a determinados servicios, comunicarnos, viajar, entretenernos, y navegar, en un futuro muy cercano. Las redes vehiculares ad hoc (vehicular ad hoc networks VANETs) son redes de comunicación inalámbricas que no requieren de ningún tipo de infraestructura, y que permiten la comunicación y conducción cooperativa entre los vehículos en la carretera. Los vehículos actúan como nodos de comunicación y transmisores, formando redes dinámicas junto a otros vehículos cercanos en entornos urbanos y autopistas. Las características especiales de las redes vehiculares favorecen el desarrollo de servicios y aplicaciones atractivas y desafiantes. En esta tesis nos centramos en las aplicaciones relacionadas con la seguridad. Específicamente, desarrollamos y evaluamos un novedoso protocol que mejora la seguridad en las carreteras. Nuestra propuesta combina el uso de información de la localización de los vehículos y las características del mapa del escenario, para mejorar la diseminación de los mensajes de alerta. En las aplicaciones de seguridad para redes vehiculares, nuestra propuesta permite reducir el problema de las tormentas de difusión, mientras que se mantiene una alta efectividad en la diseminación de los mensajes hacia los vehículos cercanos. Debido a que desplegar y evaluar redes VANET supone un gran coste y una tarea dura, la metodología basada en la simulación se muestra como una metodología alternativa a la implementación real. A diferencia de otros trabajos previos, con el fin de evaluar nuestra propuesta en un entorno realista, en nuestras simulaciones tenemos muy en cuenta tanto la movilidad de los vehículos, como la transmisión de radio en entornos urbanos, especialmente cuando los edificios interfieren en la propagación de la señal de radio. Con este propósito, desarrollamos herramientas para la simulación de VANETs más precisas y realistas, mejorando tanto la modelización de la propagación de radio, como la movilidad de los vehículos, obteniendo una solución que permite integrar mapas reales en el entorno de simulación. Finalmente, evaluamos las prestaciones de nuestro protocolo propuesto haciendo uso de nuestra plataforma de simulación mejorada, evidenciando la importancia del uso de un entorno de simulación adecuado para conseguir resultados más realistas y poder obtener conclusiones más significativas.Martínez Domínguez, FJ. (2010). Improving Vehicular ad hoc Network Protocols to Support Safety Applications in Realistic Scenarios [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/9195Palanci

Achieving dynamic road traffic management by distributed risk estimation in vehicular networks

In this thesis I develop a model for a dynamic and fine-grained approach to traffic management based around the concept of a risk limit: an acceptable or allowable level of accident risk which vehicles must not exceed. Using a vehicular network to exchange risk data, vehicles calculate their current level of accident risk and determine their behaviour in a distributed fashion in order to meet this limit. I conduct experimental investigations to determine the effectiveness of this model, showing that it is possible to achieve gains in road system utility in terms of average vehicle speed and overall throughput whilst maintaining the accident rate. I also extend this model to include risk-aware link choice and social link choice, in which vehicles make routing decisions based on both their own utility and the utility of following vehicles. I develop a coupled risk estimation algorithm in which vehicles use not only their own risk calculations but also estimates received from neighbouring vehicles in order to arrive at a final risk value. I then analyse the performance of this algorithm in terms of its convergence rate and bandwidth usage and examine how to manage the particular characteristics of a vehicular ad-hoc network, such as its dynamic topology and high node mobility. I then implement a variable-rate beaconing scheme to provide a trade-off between risk estimate error and network resource usage

Wireless Networking for Vehicle to Infrastructure Communication and Automatic Incident Detection

Vehicular wireless communication has recently generated wide interest in the area of wireless network research. Automatic Incident Detection (AID), which is the recent focus of research direction in Intelligent Transportation System (ITS), aims to increase road safety. These advances in technology enable traffic systems to use data collected from vehicles on the road to detect incidents. We develop an automatic incident detection method that has a significant active road safety application for alerting drivers about incidents and congestion. Our method for detecting traffic incidents in a highway scenario is based on the use of distance and time for changing lanes along with the vehicle speed change over time. Numerical results obtained from simulating our automatic incident detection technique suggest that our incident detection rate is higher than that of other techniques such as integrated technique. probabilistic technique and California Algorithm. We also propose a technique to maximize the number of vehicles aware of Road Side Units (RSUs) in order to enhance the accuracy of our AID technique. In our proposed Method. IEEE 802.11 standard is used at RSUs with multiple antennas to assign each lane a specific channel. To validate our proposed approach. we present both analytical and simulation scenarios. The empirical values which are obtained from both analytical and simulation results have been compared to show their consistency. Results indicate that the IEEE 802.11 standard with its beaconing mechanism can be successfully used for Vehicle to Infrastructure (V2I) communications

Zuverlässigkeitsbewertung von Fahrzeug-zu-Fahrzeug Kommunikation

V2V communication enables a plethora of cooperative applications aimed at reducing road hazard situations as well as enhancing traffic efficiency and individual driving comfort, expanding therewith the boundaries of Advanced Driver Assistance Systems (ADAS). These applications will be supported by IEEE 802.11p, a standard operating in the 5.9GHz frequency band and adapted for the highly dynamic vehicular environment. The focus of this work is V2V safety applications, which have already gained a major attention from the industry, academia, as well as standardization bodies. Being a subject of wireless communication the performance of V2V applications directly depends on the communication link quality and the packet distribution pattern. Therefore, the main purpose of this thesis is to develop an effective communication link reliability assessment method and analyze to what extent V2V communication is feasible to satisfy the reliability requirements of safety applications. Furthermore, we investigate the effectiveness of the proposed assessment method when applied for real-time communication link reliability prediction. In particular, in this work we establish the link between classical network performance metrics and specific application reliability requirements and derive a set of advanced assessment metrics. Afterwards, we investigate through these metrics how different environmental factors affect application reliability based on the measurement data, which was obtained in elaborated real-world measurement campaigns and in different non-line-of-sight scenarios. Using the suggested metrics further in this work we additionally analyze the achievable application reliability of the V2V safety applications in congested network scenarios through the simulation study. Based on these results we also define the most favorable combinations of the network parameters to support reliable operation of these applications. Finally, in this thesis we examine to what extent the suggested metrics are suitable for applications while operating in real time. We develop and implement two frameworks for prediction of the communication link reliability, based on the data that was obtained over the 4.5 months of the simTD project field trials. Furthermore, we apply both frameworks to other measurement data, which was obtained outside the simTD project and assess the effectiveness of both frameworks under independent realistic conditions.Car2Car-Kommunikation ermöglicht eine Vielzahl von kooperativen Anwendungen, welche auf die Unfallverminderung, Verbesserung der Verkehrseffizienz sowie den individuellen Fahrkomfort abzielen und damit die Grenzen von aktiven Fahrerassistenzsystemen erweitern. Im Fokus dieser Dissertation stehen Car2Car-Sicherheitsanwendungen, denen heutzutage bereits große Aufmerksamkeit von Seiten der Industrie, Forschung und diversen Normierungsgremien geschenkt wird. Da alle diese Anwendungen auf drahtloser Kommunikation basieren, ist ihre Leistungsfähigkeit direkt von der Qualität der Kommunikationsverbindung sowie dem Paketverteilungsmuster abhängig. Daher liegt der Hauptfokus dieser Arbeit in der Entwicklung effektiver Methoden zur Bewertung der Kommunikationszuverlässigkeit und der Analyse, inwieweit Car2Car-Kommunikation im Allgemeinen die Anforderungen von Sicherheitsanwendungen erfüllt. Darüber hinaus untersucht diese Doktorarbeit die Effektivität der hier vorgeschlagenen Bewertungsmethoden in Bezug auf die Vorhersage der Kommunikationszuverlässigkeit in Echtzeit-Szenarien. Im Speziellen verbindet diese Arbeit die Welt der klassischen Netzwerkperformance-Metriken mit Car2Car-Anwendungsspezifischen Zuverlässigkeitsanforderungen und stellt als Ergebnis eine Reihe effektiver Bewertungskennzahlen vor. Mithilfe der vorgeschlagenen Metriken wird des Weiteren untersucht, inwieweit verschiedene Umweltfaktoren die Anwendungszuverlässigkeit beeinflussen können. Diese Untersuchung basiert auf Messdaten, die in ausführlichen Feldversuchen in verschiedenen Non-Line-of-Sight-Szenarien gewonnen wurden. Im nächsten Schritt analysiert diese Doktorarbeit die erreichbare Zuverlässigkeit der Car2Car-Sicherheitsanwendungen in Netzwerküberlastungsszenarien anhand einer Simulationsstudie. Als Ergebnis werden die spezifischen Kombinationen der verschiedenen Netzwerkparameter definiert, die einen zuverlässigen Betrieb der Car2Car-Sicherheitsanwendungen gewährleisten können. Zum Abschluss untersucht diese Dissertation, inwieweit die vorgeschlagenen Metriken für die im Echtzeit-Modus funktionierenden Anwendungen geeignet sind. Darüber hinaus werden zwei Frameworks entwickelt und implementiert, welche die Zuverlässigkeit der Kommunikationsverbindung prädizieren. Dies geschieht basierend auf Daten, die während der 4.5 Monate dauernden Feldversuche im Rahmen des simTD Projektes gewonnen wurden. Beide Frameworks werden am Ende anhand unabhängiger Messdaten auf ihre Funktionalität unter realistischen Bedingungen getestet