Blockchain-based Secure and Intelligent Sensing Scheme for Autonomous Vehicles Activity Tracking Beyond 5G Networks

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

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)

Our research primarily focuses on developing mechanisms to intelligently route autonomous vehicles in an urban area while managing to reduce traffic congestion. We propose an Intelligent Transportation System for Autonomous vehicles based on Multi-Agent paradigm. These agents observe traffic conditions over a period of time. Historical traffic information along with information of factors that affects traffic(like weather conditions) are used to develop a regression based learning algorithm that predicts time required to traverse a route for a given time-slot, which can be used to find out shortest path for a given source-destination pair, before beginning to travel. This estimated traverse time for every lane is communicated to the vehicles. Based on this information, the vehicles find out k shortest paths. Reservation based protocols are used to obtain a single path such that the overall congestion is minimized and the destination is reached in the shortest possible time. The shortest path obtained is then traversed with the help the vehicle control system with optimized fuel efficiency, ensuring vehicle stability and obstacle avoidance. To achieve this path trajectory, we used nonlinear Model Predictive control (MPC) approach based on bicycle model of vehicle dynamics. The main advantage of MPC is the fact that it allows the current time-slot to be optimized, while taking future time-slots into account. We further investigate the linear MPC strategies for Co-operative adaptive Cruise control which directly minimizes the fuel consumption.

Traffic congestion is a major problem in metropolitan areas, which inevitably leads to substantial social and economic impacts. In the Connected Autonomous Vehicles (CAVs) context, Intelligent Transportation System (ITS) addresses routing techniques for building an efficient transportation system in an urban environment. In order to improve traffic management, CAVs use real-time traffic data to disseminate faster routes for vehicles. Meanwhile, Cloud Computing is used to manage the traffic congestion situation, but it is not a suitable option for low-latency requirements of autonomous vehicles. Fog-based approaches dealing with traffic congestion found in the literature do not consider the use of caching for a routing scheme. Therefore, we propose a reliable caching mechanism for autonomous vehicle path planning based on Fog Computing, which is called ReCall. ReCall caches real-time traffic information from different regions to dynamically perform route recommendations. The results have shown that ReCall is able to reduce travel time and emissions.

The performance of autonomous vehicle systems is currently limited by their inability to accurately analyze their surrounding environment. In order to function in a dynamic real world environment, an autonomous vehicle system must be capable of interpreting terrain based upon predetermined mission goals. This paper describes a concept of knowledge-based terrain analysis currently being developed to support the information needs of an autonomous helicopter system. The terrain analysis system consists of five integrated processing stages. Each process is discussed in detail and supported by a number of mission oriented examples. Int roduction One of the most unique system concepts being explored today is that of the development of totally autonomous land, sea, and air vehicles [1 - 3]. For military applications, autonomous vehicles provide a mechanism for removing humans from modern day battlefields while not impacting the tactical capabilities of the battle force. Civilian applications for such a device entail the investigation of hazardous areas (e.g. nuclear reactors and toxic waste dumps), search over large areas (e.g. downed aircraft or lost campers) and monitoring of large facilities (e.g. robot security police). As military funding for these systems is currently at an alltime high, a variety of autonomous system concepts are continually being presented in the technical literature. The primary limitation of an autonomous vehicle still lies in its inability to accurately analyze surrounding terrain information in order to generate tactical plans of action. This paper describes activities being performed in the Artificial Intelligence Branch of the Georgia Tech Research Institute on knowledge-based terrain analysis, particularly in support of the autonomous helicopter program.System OverviewThe knowledge-based tactical terrain analysis system consists of five separate processing phases: [1] image segmentation, [2] region classification, [3] visual model, [4] route planning, [5] threat location and coverage. Each processing phase is briefly discussed in the following subsections with actual image results given where appropriate.

The rapid advancement of autonomous vehicle systems (AVS) has introduced complex challenges to road safety. While some studies have investigated the contribution of factors influencing AV-involved crashes, few have focused on the impact of vehicle-specific factors within AVS on crash outcomes, a focus that gains importance due to the absence of a human driver. To address this gap, the advanced machine learning algorithm, LightGBM (v4.4.0), is employed to quantify the potential effects of vehicle factors on crash severity and collision types based on the Autonomous Vehicle Operation Incident Dataset (AVOID). The joint effects of different vehicle factors and the interactive effects of vehicle factors and environmental factors are studied. Compared with other frequently utilized machine learning techniques, LightGBM demonstrates superior performance. Furthermore, the SHapley Additive exPlanation (SHAP) approach is employed to interpret the results of LightGBM. The analysis of crash severity revealed the importance of investigating the vehicle characteristics of AVs. Operator type is the most predictive factor. For road types, highways and streets show a positive association with the model’s prediction of serious crashes. Crashes involving vulnerable road users can be attributed to different factors. The road type is the most significant factor, followed by precrash speed and mileage. This study identifies key predictive associations for the development of safer AV systems and provides data-driven insights to support regulatory strategies for autonomous driving technologies.

According to the world health organization, there were 1.25 million road accident deaths globally in 2013. To decrease this count, it has been said that autonomous vehicles can play a major role. The autonomous vehicle uses different sensors such as LIDAR, RADAR, Ultrasonic sensors, and cameras for detection of lane lines, objects, traffic sign, traffic lights, and driving path. There are several successful projects on autonomous vehicles, but the main limitations are high costs and regulations. The camera can be a cost-effective solution as compare to LIDAR in all the basic detection needed for autonomous driving. The proper placement of sensors on the vehicle plays an important role in efficient design, working and reducing the cost of the overall autonomous vehicle system. This paper discusses the parameters considered while deciding the positioning of the camera, and the geometric calculation for the best positioning of a monocular camera on an autonomous vehicle.

Transitioning to a driverless city: Evaluating a hybrid system for autonomous and non-autonomous vehicles

Comparative study of different controllers in an autonomous vehicle system

Underwater imaging is being increasingly helpful for the autonomous robots to reconstruct and map the marine environments which is fundamental for searching for pipelines or wreckages in depth waters. In this context, the accuracy of the information obtained from the environment is of extremely importance. This work presents a study about the accuracy of a reconfigurable stereo vision system while determining a dense disparity estimation for underwater imaging. The idea is to explore the advantage of this kind of system for underwater autonomous vehicles (AUV) since varying parameters like the baseline and the pose of the cameras make possible to extract accurate 3D information at different distances between the AUV and the scene. Therefore, the impact of these parameters is analyzed using a metric error of the point cloud acquired by a stereoscopic system. Furthermore, results obtained directly from an underwater environment proved that a reconfigurable stereo system can have some advantages for autonomous vehicles since, in some trials, the error was reduced by 0.05m for distances between 1.125 and 2.675 m.

Multi-sensor fusion has been widely used by autonomous vehicles (AVs) to integrate the perception results from different sensing modalities including LiDAR, camera and radar. Despite the rapid development of multi-sensor fusion systems in autonomous driving, their vulnerability to malicious attacks have not been well studied. Although some prior works have studied the attacks against the perception systems of AVs, they only consider a single sensing modality or a camera-LiDAR fusion system, which can not attack the sensor fusion system based on LiDAR, camera, and radar. To fill this research gap, in this paper, we present the first study on the vulnerability of multi-sensor fusion systems that employ LiDAR, camera, and radar. Specifically, we propose a novel attack method that can simultaneously attack all three types of sensing modalities using a single type of adversarial object. The adversarial object can be easily fabricated at low cost, and the proposed attack can be easily performed with high stealthiness and flexibility in practice. Extensive experiments based on a real-world AV testbed show that the proposed attack can continuously hide a target vehicle from the perception system of a victim AV using only two small adversarial objects.

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.

As autonomous vehicles (AVs) become an integral part of modern transportation, their complex systems face increasing cybersecurity threats. This review examines the critical cybersecurity challenges posed by AVs, focusing on external and internal threats including hackers, cybercriminals, and software vulnerabilities Denial of service (DoS); including AVs that rely on sophisticated communications networks, sensor systems, and artificial intelligence (AI), are highly susceptible to cyberattacks such as counterfeiting and remote control abuse. The problem statement identifies this threat as a serious threat to vehicle safety and the broader transportation system, and highlights the need for robust cybersecurity measures. The purpose of this study is to classify cybersecurity vulnerabilities in AV systems, assess potential risks, and propose effective mitigation measures The study investigates technical vulnerabilities in software, communication systems, sensors, . AI algorithms, as well as systematic challenges and regulatory gaps in AV delivery. In response, the study provides comprehensive mitigation strategies, with policy recommendations to develop effective global cybersecurity standards and regulatory frameworks including encryption, intrusion detection systems , secure software updates, and integrating post-quantum cryptography to address future threats from quantum computer programming. The results highlight the need for a multilevel cybersecurity strategy that incorporates both technical and legal solutions. The findings suggest that a holistic approach is needed to secure AV systems, addressing not only implementation can significantly reduce the risk of cyberattacks, and ensure that autonomous vehicles operate safely and reliably in a highly connected world.

Concerns regarding cybersecurity, safety, privacy, and public acceptance are raised by the integration of autonomous vehicles (AVs) into sophisticated transportation networks. The purpose of this study is to investigate and evaluate the various perspectives that surround AVs. Evaluating public acceptability entails determining degrees of confidence and concerns about AV technology. The ability of AVs to handle challenging traffic situations while maintaining pedestrian and passenger safety is a safety concern. Concerns about privacy mostly concern how personal information is gathered, used, and safeguarded in AV systems. Implications for cybersecurity also highlight how susceptible antivirus software is to possible hackers or system breaches. This research contributes to a thorough knowledge of the potential and problems associated with the broad adoption of autonomous cars within futuristic transportation ecosystems by looking at these factors.