A Cache Strategy for Intelligent Transportation System to Connected Autonomous Vehicles

In the past few decades, Intelligent Transport System (ITS) has gained the popularity in smart society. Global automotive stakeholders such as Google, Uber, and Toshiba are the driving force in that and also looking for the interconnection of autonomous vehicles (AVs) for road traffic management, that is, connected AVs (CAV). CAVs are vulnerable to various safety and security issues, for instance, denial‐of‐service, global positioning system (GPS) spoofing, man‐in‐the‐middle, and timing attacks, which raise serious concerns for their development in application specific areas. Although many solutions exist in the literature, most of them are focused on centralized systems, which is having a single‐point‐failure. In light of the above facts, we analyze and classify the security and privacy issues in CAV and proposed a solution taxonomy in this article. We emphasized the defense mechanism for these attacks on the CAV system along with their enactment on issues and discovered how emergent blockchain technology could handle it. Furthermore, we proposed a blockchain‐based secure and decentralized CAV architecture to mitigate security and privacy issues such as denial of service, man‐in‐the‐middle, hijacking, GPS spoofing, and replay attacks. Finally, we reconnoiter the open research challenges associated with the blockchain‐based CAV system. To validate the efficacy of the proposed system, a real‐time case study on smart city integration over edge computing using computation and communication costs is proposed in this article.

The introduction to autonomous vehicles and intelligent transportation systems, as well as related work on autonomous vehicles and intelligent transportation systems, are described in this chapter. Aside from that, it has a chapter on autonomous intelligent vehicles and autonomous vehicles. Issues and challenges in autonomous intelligent vehicles, as well as the framework of an intelligent transportation system. In addition, the chapter discusses research opportunities in autonomous intelligent vehicles, IPv4 and IPv6 issues, and the future of IPv9 for vehicles, as well as a survey on autonomous vehicles in highway scenarios. During the last century, the automobile industry has excelled at producing cars that are both safe and affordable. Because of significant advancements in the Internet of things (IoT), the programmable logic controller (PLC), and other aspects of computing, autonomous vehicles (AVs) are becoming a reality. Cars, trucks, and other vehicles that don’t need to be plugged in to get power are referred to as self-sufficient vehicles. The multi-agent-based transportation system, which demonstrates how vehicles, drivers, roads, infrastructure, and vehicles all work together, is one of the most important intelligent transportation systems because it demonstrates how all of these things work together. To solve and manage traffic problems, an intelligent transportation systems (ITS) system employs a variety of technologies. Communication and control, vehicle sensing, and electronics are among these technologies. ITS has been used in the developed world for the past two decades. It is still a novel concept in the developing world. Sensors and computers work together in the Internet of things to store and process data, and data analytics is used to make the traffic system run more smoothly.KeywordsAutonomous vehicle (AV)Intelligent transportation system (ITS)Autonomous intelligent vehicle (AIV)Advanced driver assistance system (ADAS)Advanced driving system (ADS)

For the past few years, the automation of transportation becomes a hot research topic for smart cities. Intelligent Transportation System (ITS) aims to manage and optimize the traffic congestion, road accidents, parking allocation using Autonomous Vehicles (AV) system, where the AVs are internally connected for message passing and critical decision making in time-sensitive applications. The data security in such applications can be offered using Blockchain (BC) technology. But, as per the existing literature, there exists no system which can call AVs automatically based on the situation, i.e., call an ambulance in case of an accident, call logistic service in case of home transfer, and call the traffic department in case of traffic jam. Motivated from the aforementioned reasons, in this article, we propose a BC-based secure and intelligent sensing and tracking architecture for AV system using beyond 5G communication network. Recently, AVs are facing issues with sensing and tracking technology as well as the data thefts. AV system contains sensitive information and transfers it through a communication channel to Connected AVs (CAVs), where the corrupted information or delay of a fraction of a second can lead to a critical situation. So, here we present possib the attacks and safety countermeasures using BC technology to protect the AV system. The proposed architecture ensures secure sensing and tracking of an object through BC by deploying AI algorithms at the edge servers. Also, the beyond 5G network enables communications with low latency and high reliability to meet the desires of the aforementioned time-sensitive applications. The proposed system is evaluated by considering the parameters as mobility and data transfer time against the traditional LTE-A and 5G communication networks. The proposed system outperforms traditional systems and can be suitable for diverse applications where latency, reliability, and security are the prime concerns.

Intelligent Transport System (ITS) applications are focused on improving road safety and creating new transport services. To realize the potential of ITS applications, data exchange should be efficiently carried out through direct communication over short distances both between vehicles (V2V) and between vehicles and infrastructure (V2I). High demands are placed on ITS performance indicators. These requirements are increasing especially in the context of the new mobility patterns using autonomous vehicles. The ITS big data processing can be centralized or distributed. The centralized approach utilizes the power of cloud computing. In contrast to cloud computing, intelligent data processing with faster responses and higher quality can be provided by fog computing. Fog computing is a horizontal, system-level architecture that distributes computing, storage, control and networking functions closer to the users along a cloud-to-thing continuum. Implementing fog computing in the dynamic environment of ITS to provide real-time big data analytics is facing many challenges that are not covered by previous research studies. The article analyzes the availability of V2I services in distributed communication network of ITS with fog-based architecture from reliability point of view. The dependability of V2I services for end users of smart vehicle in fog operating ITS is defined. The mathematical model of the reliability of a separate dedicated service in fog-based ITS for end user applications in the real conditions of operation is developed.

Enhancing accessibility in transportation systems is an escalating interest among researchers, fueled by the rising issues with traffic congestion and technological innovations. Accessibility significantly contributes to improving the quality of life, thereby necessitating an examination of how route guidance systems impact it. This study explores the influence of system optimum (SO) and user equilibrium (UE) route guidance systems on accessibility, particularly in the context of connected autonomous vehicles (CAVs) embedded in the network. We employ a hybrid assignment model that enables the concurrent allocation of SO and UE assignments, along with the Gravity model to compute accessibility. The Sioux Falls network, previously leveraged in prior research, was chosen for numerical simulations to permit insightful comparisons. Our study scrutinizes three scenarios: the first scenario examines the repercussions of route guidance systems on human-driven vehicles, while the second and third assess their effects on CAVs. The difference between the second and third scenario is in the way of increasing the capacity in the assignment. Our findings reveal that at lower penetration rates of route guidance systems, accessibility initially dips, and then ascends. When all human-driven users adopt route guidance systems, accessibility increases by 12.75% compared to the initial state (without any intervention). Remarkably, should all users employ autonomous vehicles, accessibility would surge by 220% compared to the initial state (without any intervention). These insights highlight the importance of integrating CAVs and route guidance systems into transportation planning to enhance accessibility and improve the quality of life.

Providing real-time en-route suggestions to CAVs for congestion mitigation: A two-way deep reinforcement learning approach

If dedicate a lane to connected autonomous vehicle (CAV) on a multilane road, the traffic congestion and safety risks remain a major problem but in a different style. Random and disorderly mandatory lane‐changing behaviour before approaching the next ramp or intersection would have a disturbing effect on the following vehicles of the traffic flow. This paper mainly establishes the optimal mandatory lane‐changing location matching model for each target vehicle in the dedicated CAV lane environment. The aim is to minimizing the total travel time, which could take the disturbing effect into account. This model nests the cell transmission model (CTM) to describe vehicle running. The constraints include the relation between target CAV lane‐changing cell and the corresponding behaviour start time, the updating of the flow, and occupancy for varied cells. We use the Ant Colony Optimization (ACO) algorithm to solve the problem. Through the case study of a basic two‐lane road scenario in Ningbo, we acquire the convergence results based on the ACO algorithm. Our optimal lane‐changing location matching scheme can save 5.9% total travel time when compared to the near‐end location lane‐changing scheme. We test our model by increasing the total number of upstream input vehicles with 4%, 11%, 15%, and the mandatory lane‐changing vehicles with 60%, 200%, respectively. The testing results prove that out optimization method could deal with varied road traffic flow situations. Specifically, when the traffics and mandatory lane‐changing vehicles increase, our method could perform better.

Connected Autonomous Vehicles (CAV) utilize vehicular communication to collect information about the surrounding environment to make informed decisions about speed and maneuvering. This enables safe driving and decreases the number of accidents and thereby the associated fatalities. However, vehicular communication may suffer from high latency and low reliability, especially in dense vehicle environments, which may negatively affect the safety of CAVs. Therefore, it is crucial to study the impact of these metrics on the safety application performance while taking into account realistic CAV kinematics and dynamics. In this paper, we address this problem by comparing the performance of the Short Range Communication (DSRC) to that of the Fifth-Generation New Radio (5G-NR) and their impacts on the safety applications in the CAV environment under different settings. We develop a full-fledged simulation framework that can realistically model both vehicular mobility and communication and can capture the impact of communication on safety applications. Within this framework, we implement an important CAV's safety application, namely, the forward collision avoidance system, in which following vehicles use vehicular communications to gather information from leading vehicles to compute the safe speed and avoid collisions. We then use this framework to study and compare the performance safety of the forward collision avoidance system using both DSRC and 5G-NR communications. The results show that the packet delays and drops in communication networks can adversely affect CAV safety. The results also demonstrate that 5G is more capable of supporting the safety requirements under higher packet traffic loads and vehicle densities.

The rise of the artificial intelligence (AI) brings golden opportunity to accelerate the development of the intelligent transportation system (ITS). The platoon control of connected autonomous vehicle (CAV) as the key technology exhibits superior for improving traffic system. However, there still exist some challenges in multi-objective platoon control and multi-agent interaction. Therefore, this paper proposed a connected autonomous vehicle latoon control approach with multi-agent deep reinforcement learning (MADRL). Finally, the results in stochastic mixed traffic flow based on SUMO (simulation of urban mobility) platform demonstrate that the proposed method is feasible, effective and advanced.KeywordsIntelligent transportation systemConnected autonomous vehicleMulti-objective platoon controlMulti-agent deep reinforcement learning

Half of the world’s population resides in cities and this percentage is going to rise in the coming years. This rapid transformation of the economy and society is making traditional city management redundant. Intensive urbanization has brought with it some challenges. Scanty resources, population growth, increased carbon footprint, reduced air quality, congestion on roads, depriving health status, and falling food quality, all are prominent parts of cities. Does this bring a question in mind is accelerated growth sustainable? The answer and solution to this is an adaption of the Smart City Concept. A smart city integrates all the available technology with the requirements of city development and builds infrastructure which can improve the quality of life of citizens. Smart cities are designed in a manner to use minimum resources with maximum efficiency in all areas of urban life. It provides smart solutions using smart technologies. Urban needs and technological advancements meet in smart cities. It manages cities more quickly and effectively by using AI, IT technologies, and robotic process automation. Intelligent transportation, intelligent energy, intelligent governance, intelligent security, intelligent healthcare are all central to the idea of smart cities. The emphasis of this essay is on smart transit as a component of smart cities. The movement of people and goods across several geographical boundaries has been significantly aided by the transportation sector. Globally, the majority of smart cities face logistical and transportation issues. Both the fast growing human population and the increase in the number of cars on the road are to blame for this. In order to design and run a sustainable transportation system, technology may be of great assistance. Robotic systems for real-time vehicle data collection, traffic management, and using mobility on demand in trip planning through a single user interface have emerged as cities struggle with traffic congestion. The transportation industry, which faces difficulties including traffic congestion, unanticipated delays, and routing issues that cause businesses to lose money, has successfully used robotic process automation. Robotic process automation provides reliable results and affordable solutions while reducing uncertainty in the decision-making process. It is very helpful in designing and running a successful transit system. Robotic Process Automation (RPA) systems for obtaining real-time data from vehicles, buses, and trucks for traffic management and using mobility on demand in trip planning through a single user interface have emerged as cities struggle with poor management and congestion. By examining different uses of robotic process automation, it is possible to evaluate precise prediction and detection models that are intended to forecast traffic volume, an appropriate route, traffic conditions, accurate information about timing, incidents, and which time and roadway for transport are suitable for running and mobility. The application of RPA in smart cities could be seen in Intelligent Transportation Systems’ Traffic Management, Public Transportation, Safety Management, Manufacturing, and Logistics. The research focuses on areas of concern for smart cities in the sphere of the transportation sector and related difficulties that may be solved via robotic process automation. The article emphasizes the challenges and solutions for making smart transportation more effective by using Intelligent Robotic Transportation Systems.

Infrastructure Wi-Fi for connected autonomous vehicle positioning: A review of the state-of-the-art

It is interesting to realize that traffic intersections provide an essential mechanism to handle traffic flows in different directions while ironically traffic bottlenecks, gridlocks, and accidents tend to occur in the vicinity of the intersections. Meanwhile, with the recent developments in internet of things (IoT) technologies, there is a great potential for integrating them for improving operational efficiencies of road infrastructure and connected autonomous vehicles (CAVs). This paper aims to leverage the capabilities of both autonomous intersection manager (AIM) and CAVs for more energy-saving and safe-traffic management. A mixed-traffic environment where human-driven vehicles (HDVs) and CAVs sharing the same road is considered. A two-layer framework is adopted to handle signal and vehicle controls effectively. The first layer is a signal control layer where the AIM receives the traffic network states, trains with the data through machine learning (ML), and outputs a set of optimal green times for each intersection phase. The second layer is a decentralized-vehicle control layer where the CAVs receive the signal phase and timing (SpaT) information from the AIM to compute the optimal speed values. The proposed solution helps the CAVs to minimize idling at red signals or to speed up to safely arrive at and pass through a green signal. Our proposed framework is designed to optimize intersection efficiency and minimize vehicle average delay and fuel consumption. All experiments have been conducted in a microscopic traffic simulation environment, the PTV-VISSIM, simulating real-world dynamics of vehicles and drivers’ behaviors based on decades of field-data.

Connected autonomous vehicles (CAVs) will be deployed over the next decade with autonomous functionalities supported by new sensing and communication capabilities. Such functionality exposes CAVs to new attacks that current vehicles will not face. To ensure the safety and security of CAVs, it is important to be able to identify the ways in which the system could be attacked and to build defences against these attacks. One possible approach is to use reference architectures to perform an attack surface analysis. Existing research has developed a variety of reference architectures but none for the specific purpose of attack surface analysis. Existing approaches are either too simple for sufficiently detailed modelling or require too many details to be specified to easily analyse a CAV’s attack surface. Therefore, we propose a reference architecture using a hybrid Functional-Communication viewpoint for attack surface analysis of CAVs, including the Devices, Edge and Cloud systems CAVs interact with. Using two case studies, we demonstrate how attack trees can be used to understand the attack surface of CAV systems.

Chapter 20 - Sensing and monitoring of smart transportation systems

The integration of autonomous vehicles (AVs) into public transportation systems has the potential to transform urban mobility by enhancing efficiency, accessibility, and sustainability. As cities continue to expand, traditional transit systems face challenges such as traffic congestion, high operational costs, and limited coverage. AVs, with their ability to optimize routes, reduce human error, and operate continuously, offer a promising solution to improve public transit networks. The adoption of AV technology in public transportation can redefine mobility patterns, particularly by bridging first- and last-mile connectivity gaps, enhancing commuter convenience, and improving mobility for elderly and disabled passengers. Unlike conventional transit modes, autonomous buses, shuttles, and taxis can dynamically adjust routes based on real-time data, reducing travel time and increasing overall system efficiency. Additionally, AVs can function seamlessly with multi-modal transport networks, integrating with metro, rail, and bus services to create a more cohesive and accessible urban transit system. Beyond improving passenger experience, AVs offer significant economic and environmental benefits. Their reduced dependence on human drivers leads to lower labor costs, while advanced energy-efficient systems contribute to lower fuel consumption and carbon emissions. Autonomous public transit solutions also help alleviate traffic congestion by minimizing unnecessary stops and optimizing roadway usage through vehicle-to-infrastructure (V2I) and vehicle-to vehicle (V2V) communication. However, the transition to a fully autonomous public transport system is not without challenges. Cities and governments must address issues such as infrastructure readiness, regulatory frameworks, liability concerns, cyber security risks, and public acceptance to ensure a smooth and secure transition. By analyzing real-world case studies and pilot projects, this chapter provides insights into how different cities worldwide are integrating AVs into their transit systems. Several pilot programs have demonstrated the feasibility of self driving shuttle services, particularly in urban areas with high commuter demand and limited transit accessibility. Furthermore, technological advancements in artificial intelligence (AI), machine learning, and sensor-based navigation continue to refine AV capabilities, making them increasingly viable for large-scale deployment in public transportation. This chapter also explores future trends, policy recommendations, and best practices that can accelerate AV adoption in public transport. Governments, urban planners, and transportation authorities must collaborate to design regulatory policies that encourage innovation while ensuring passenger safety and data security. Investment in smart infrastructure, including intelligent traffic management systems and dedicated AV lanes, will be crucial for optimizing AV operations in public transit. Ultimately, the successful integration of AVs into public transportation can lead to a more efficient, inclusive, and sustainable urban mobility ecosystem. With the right combination of technological innovation, policy support, and public engagement, AVs have the potential to redefine how people commute in the cities of the future, offering safe, affordable, and environmentally friendly solutions.