Scalable Cellular V2X Solutions: Large-Scale Deployment Challenges of Connected Vehicle Safety Networks

Vehicle-to-Everything (V2X) communication is expected to accomplish a long-standing goal of the Connected and Autonomous Vehicle (CAV) community to bring connected vehicles to roads on a large scale. A major challenge, and perhaps the biggest hurdle on the path towards this goal is the scalability issues associated with it, especially when vehicular safety is concerned. As a major stakeholder, 3rd Generation Partnership Project (3GPP) based Cellular V2X (C-V2X) community has long been trying to research on whether vehicular networks are able to support the safety-critical applications in high-density vehicular scenarios. This paper attempts to answer this by first presenting an overview on the scalability challenges faced by 3GPP Release 14 Long Term Evolution C-V2X (LTE-V2X) using the PC5 sidelink interface for low and heavy-density traffic scenarios. Next, it demonstrates a series of solutions that address network congestion, packet losses and other scalability issues associated with LTE-V2X to enable this communication technology for commercial deployment. In addition, a brief survey is provided into 3GPP Release 16 5G New Radio V2X (NR-V2X) that utilizes the NR sidelink interface and works as an evolution of C-V2X towards better performance for V2X communications including new enhanced V2X (eV2X) scenarios that possess ultra-low-latency and high-reliability requirements

AROW: A V2X-based Automated Right-of-Way Algorithm for Distributed Cooperative Intersection Management

Safe and efficient intersection management is critical for an improved driving experience. As per several studies, an increasing number of crashes and fatalities occur every year at intersections. Most crashes are a consequence of a lack of situational awareness and ambiguity over intersection crossing priority. In this regard, research in Cooperative Intersection Management (CIM) is considered highly significant since it can utilize Vehicle-to-Everything (V2X) communication among Connected and Autonomous Vehicles (CAVs). CAVs can transceive basic and/or advanced safety information, thereby improving situational awareness at intersections. Although numerous studies have been performed on CIM, most of them are reliant on the presence of a Road-Side Unit (RSU) that can act as a centralized intersection manager and assign intersection crossing priorities. In the absence of RSU, there are some distributed CIM methods that only rely on communication among CAVs for situational awareness, however, none of them are specifically focused towards Stop Controlled-Intersection (SCI) with the aim of mitigating ambiguity among CAVs. Thus, we propose an Automated Right-of-Way (AROW) algorithm based on distributed CIM that is capable of reducing ambiguity and handling any level of noncompliance by CAVs. The algorithm is validated with extensive experiments for its functionality and robustness, and it outperforms the current solutions

Prediction-aware and Reinforcement Learning based Altruistic Cooperative Driving

Autonomous vehicle (AV) navigation in the presence of Human-driven vehicles (HVs) is challenging, as HVs continuously update their policies in response to AVs. In order to navigate safely in the presence of complex AV-HV social interactions, the AVs must learn to predict these changes. Humans are capable of navigating such challenging social interaction settings because of their intrinsic knowledge about other agents behaviors and use that to forecast what might happen in the future. Inspired by humans, we provide our AVs the capability of anticipating future states and leveraging prediction in a cooperative reinforcement learning (RL) decision-making framework, to improve safety and robustness. In this paper, we propose an integration of two essential and earlier-presented components of AVs: social navigation and prediction. We formulate the AV decision-making process as a RL problem and seek to obtain optimal policies that produce socially beneficial results utilizing a prediction-aware planning and social-aware optimization RL framework. We also propose a Hybrid Predictive Network (HPN) that anticipates future observations. The HPN is used in a multi-step prediction chain to compute a window of predicted future observations to be used by the value function network (VFN). Finally, a safe VFN is trained to optimize a social utility using a sequence of previous and predicted observations, and a safety prioritizer is used to leverage the interpretable kinematic predictions to mask the unsafe actions, constraining the RL policy. We compare our prediction-aware AV to state-of-the-art solutions and demonstrate performance improvements in terms of efficiency and safety in multiple simulated scenarios

A Survey on IoT-Enabled Smart Grids: Emerging, Applications, Challenges, and Outlook

Swift population growth and rising demand for energy in the 21st century have resulted in considerable efforts to make the electrical grid more intelligent and responsive to accommodate consumers’ needs better while enhancing the reliability and efficiency of modern power systems. Internet of Things (IoT) has appeared as one of the enabling technologies for smart energy grids by delivering abundant cutting-edge solutions in various domains, including critical infrastructures. As IoT-enabled devices continue to flourish, one of the major challenges is security issues, since IoT devices are connected through the Internet, thus making the smart grids vulnerable to a diverse range of cyberattacks. Given the possible cascading consequences of shutting down a power system, a cyberattack on a smart grid would have disastrous implications for the stability of all grid-connected infrastructures. Most of the gadgets in our homes, workplaces, hospitals, and on trains require electricity to run. Therefore, the entire grid is subject to cyberattacks when a single device is hacked. Such attacks on power supplies may bring entire cities to a standstill, resulting in massive economic losses. As a result, security is an important element to address before the large-scale deployment of IoT-based devices in energy systems. In this report, first, we review the architecture and infrastructure of IoT-enabled smart grids; then, we focus on major challenges and security issues regarding their implementation. Lastly, as the main outcome of this study, we highlight the advanced solutions and technologies that can help IoT-enabled smart grids be more resilient and secure in overcoming existing cyber and physical attacks. In this regard, in the future, the broad implementation of cutting-edge secure and data transmission systems based on blockchain techniques is necessary to safeguard the entire electrical grid against cyber-physical adversaries

Facile Synthesis of NH-Free 5-(Hetero)Aryl-Pyrrole-2-Carboxylates by Catalytic C–H Borylation and Suzuki Coupling

A convenient two-step preparation of NH-free 5-aryl-pyrrole-2-carboxylates is described. The synthetic route consists of catalytic borylation of commercially available pyrrole-2-carboxylate ester followed by Suzuki coupling without going through pyrrole N–H protection and deprotection steps. The resulting 5-aryl substituted pyrrole-2-carboxylates were synthesized in good- to excellent yields. This synthetic route can tolerate a variety of functional groups including those with acidic protons on the aryl bromide coupling partner. This methodology is also applicable for cross-coupling with heteroaryl bromides to yield pyrrole-thiophene, pyrrole-pyridine, and 2,3’-bi-pyrrole based bi-heteroaryls

Fe-POM/ attapulgite composite materials: efficient catalysts for plastic pyrolysis

This article describes the catalytic cracking of low-density polyethylene over attapulgite clay and iron substituted tungstophosphate/attapulgite clay (Fe-POM/attapulgite) composite materials to evaluate their suitability and performance for recycling of plastic waste into liquid fuel. The prepared catalysts enhanced the yield of liquid fuel (hydrocarbons) produced in cracking process. A maximum yield of 82% liquid oil fraction with a negligible amount of coke was obtained for 50% Fe-POM/attapulgite composite. Whereas, only 68% liquid oil fractions with a large amount of solid black residue was produced in case of non-catalytic pyrolysis. Moreover, Fe-POM/attapulgite clay composites showed higher selectivity towards lower hydrocarbons (C5–C12) with aliphatic hydrocarbons as major fractions. These synthesised composite catalysts significantly lowered the pyrolysis temperature from 375°C to 310°C. Hence, recovery of valuable fuel oil from polyethylene using these synthesised catalysts suggested their applicability for energy production from plastic waste at industrial level as well as for effective environment pollution control