The development of intelligent sensors has garnered widespread attention in autonomous driving. Although they have made significant progress, current vehicular sensing systems are still limited to basic perceptual intelligence and lack deeper cognitive capabilities for comprehensive scene understanding. The emerging LLMs (Large Language Models) and agent technologies provide a promising solution to address these issues. This letter proposes a novel SensingAgent framework for building next-generation vehicular sensing systems toward new AI (Autonomous Intelligence and Agentic Intelligence). It adopts a cloud-edge-end architecture, leveraging multi-agent collaboration to revolutionize the sensing paradigm. Additionally, we introduce DAO (Decentralized Autonomous Organization) into sensing systems and propose a new concept of SAO (Sensor Autonomous Organization). It utilizes smart contracts to ensure trustworthy operations across the sensing industry chain. This letter presents a report on the Distributed/Decentralized Hybrid Workshop on Foundation/Infrastructure Intelligence (DHW-FII), providing new insights into the future of intelligent sensing systems.
In the absence of external interference, independent multi-agent systems evolve over time, leading to a transition from an ordered to a chaotic state within the internal dynamics of the scene. To assist in maintaining the stability of dynamic systems, this paper explores the design and composition of the scenario. A category of altruistic agents with selfless attributes is introduced into the multi-agent environment, and the decentralized algorithm I-DDPG is employed to train these altruistic agents to perform tasks with altruistic tendencies. Weakly connected self-organization is used to enable cooperation among altruistic agents in addressing internal issues. The study demonstrates a positive correlation between the overall system stability and individual information entropy, as well as the method of obtaining individual altruistic rewards. To investigate the connection between overall scene stability and self-organization among internal individuals, a traffic scenario is constructed with both human-driven and autonomous vehicles. Autonomous vehicles (AVs) represent a type of altruistic agent, and experiments are conducted to explore the relationship between scene stability and internal self-organization among individuals. The study concludes that using I-DDPG improves the learning efficiency of agents compared to DDPG and demonstrates a higher safety level in stress tests. Post-training, agents utilizing weakly connected self-organization in scenarios involving merging and exiting obtain higher altruistic rewards. This approach aids human-driven vehicles in achieving their intended goals, contributing to an overall improvement in the stability of dynamic systems.
With the development of smart cities, video surveillance has become more prevalent in urban areas. The rapid growth of data brings challenges to video processing and analysis. Multi-object tracking (MOT), one of the most fundamental tasks in computer vision, has a wide range of applications and development prospects. MOT aims to locate multiple objects and maintain their unique identities by analyzing the video frame by frame. Most existing MOT frameworks are deployed in centralized systems, which are convenient for management but have problems such as weak algorithm adaptability, limited system scalability, and poor data security. In this paper, we propose a distributed MOT algorithm based on multi-agent reinforcement learning (DMARL-Tracker), which formulates MOT as a Markov decision process (MDP). Each object adjusts its tracking strategy during interactions with the environment. The benchmark results on MOT17 and MOT20 prove that our proposed algorithm achieves state-of-the-art (SOTA) performance. Based on this, we further integrate DMARL-Tracker into the blockchain and propose a blockchain-based collaborative MOT framework. All nodes collaborate and share information through the blockchain, achieving adaptation in different complex scenarios while ensuring data security. The simulation results show that our framework achieves good performance in terms of tracking and resource consumption.
The notion of an intelligent transportation system (ITS) aims to boost the performance of transportation networks, which has gained more and more traction in both academic and commercial circles. ITS is a constantly evolving vision that combines cutting-edge transportation approaches with new information, communication, computers, and other technology. ITS should discover consequence routes to enhance the sustainability, safety, and trustworthiness of the entire transportation system utilizing emerging technologies. In this paper, a sustainable safety management framework for connected vehicles is proposed by integrating blockchain. It introduces smart transportation equipment called an AI-enabled vehicle smart device (AVSD) for vehicular communications. AVSD can reduce energy consumption by decreasing the computational costs in vehicular communications. Smart contracts are used to identify vehicles automatically and establish secure communication among vehicles and emergency service stations (ESSs) like hospitals, police stations, and fire stations. The experiment results show that the proposed framework provides a communication environment for sustainable safety and security using the introduced smart transportation device. The proposed blockchain-enabled sustainable safety management framework has the potential to improve safety and sustainability in the transportation industry by creating a secure, decentralized, and transparent platform for managing safety data and promoting safe and sustainable driving behaviors.
Industry 5.0 integrates human ability with machines to satisfy the increasing demands of automation. Autonomous Vehicles (AVs) are vital in Industry 5.0 due to their high mobility and intelligent decision-making. Data collected from AVs using Road Side Units (RSUs) aid in enhanced delivery, automated ride-sharing and minimized latency travel. The AVs are reticent to exchange information with other vehicles to ensure data privacy. As there is no trusted environment for data exchange, the AV data are vulnerable to cyber infiltration due to the widespread use of software and the activation of wireless connections. Identifying the source of data that has been shared without authorization is challenging. In this paper, we exploit Machine Learning (ML) with an Intrusion Detection System (IDS) that incorporates Stochastic Gradient Descent (SGD) for detecting intrusions in assistance with blockchain for an enhanced trust evaluation in a 5G-V2X Internet of Vehicles (IoV) environment. A detailed analysis demonstrates that the proposed Blockchain assisted IDS (BIDS) is efficient and secures 98% accuracy compared with the other state-of-the-art solutions.
Although vehicular ad hoc networks (VANETs) significantly enhance traffic convenience, the propagation of erroneous information by malicious vehicles remains a challenging issue. To maintain message reliability, it is crucial to establish a trust management model that can promptly detect malicious vehicles and identify false messages. This article presents a novel trust management model based on blockchain, machine learning, and active detection technology. In the proposed model, we designed a trust evaluation scheme to evaluate the credibility by calculating the direct and indirect trust of the vehicle. To achieve this goal, we use active detection technology to detect indirect trust in vehicles, and then store it in the blockchain. The direct trust of the vehicle is calculated using a Bayesian classifier. The use of active detection technology speeds up the process of filtering out malicious vehicles. Machine learning technology simplifies the complex iterations involved in computing the trust value. Finally, the use of blockchain ensures the consistency and tamper-proofing of the trusted data. The simulation outcomes demonstrate that our approach outperforms the present trust management models.
Autonomous vehicles (AVs), defined as vehicles capable of navigation and decision-making independent of human intervention, represent a revolutionary advancement in transportation technology. These vehicles operate by synthesizing an array of sophisticated technologies, including sensors, cameras, GPS, radar, light imaging detection and ranging (LiDAR), and advanced computing systems. These components work in concert to accurately perceive the vehicle’s environment, ensuring the capacity to make optimal decisions in real-time. At the heart of AV functionality lies the ability to facilitate intercommunication between vehicles and with critical road infrastructure—a characteristic that, while central to their efficacy, also renders them susceptible to cyber threats. The potential infiltration of these communication channels poses a severe threat, enabling the possibility of personal information theft or the introduction of malicious software that could compromise vehicle safety. This paper offers a comprehensive exploration of the current state of AV technology, particularly examining the intersection of autonomous vehicles and emotional intelligence. We delve into an extensive analysis of recent research on safety lapses and security vulnerabilities in autonomous vehicles, placing specific emphasis on the different types of cyber attacks to which they are susceptible. We further explore the various security solutions that have been proposed and implemented to address these threats. The discussion not only provides an overview of the existing challenges but also presents a pathway toward future research directions. This includes potential advancements in the AV field, the continued refinement of safety measures, and the development of more robust, resilient security mechanisms. Ultimately, this paper seeks to contribute to a deeper understanding of the safety and security landscape of autonomous vehicles, fostering discourse on the intricate balance between technological advancement and security in this rapidly evolving field.
Bad weather or environmental factors, particularly in remote mountain areas, may result in unsafe driving conditions and consequently road traffic accidents. As the deployment of large-scale sensing nodes for reporting road conditions is too expensive, the crowdsourcing method or reporting by sensors in vehicles themselves will be easier to deploy and more practical. However, those participant sensing methods impose some difficulties, such as fake information, reporter misbehavior, and timeliness. Thus, we propose a tri-blockchain-based Internet of Vehicles system, called TriBoDeS, to facilitate real-time information detection and sharing. It is designed to guarantee concurrency and security to dynamically store, manage, and share information uploaded by vehicles with great efficiency. Such information will be announced on the blockchain under the autonomous identification of vehicles in low-trust conditions. In order to ensure the software’s security, TriBoDeS can monitor the software’s state, detect identified malicious activities, and respond accordingly. To ensure data security, a role-based management mechanism is introduced to achieve fine-grained control over permissions, and confidence rules are established to guarantee the authenticity of the data. To demonstrate the applicability of the proposed scheme, we evaluate its performance (e.g., computing and communication overheads) and security (e.g., resiliency against common attacks) over a consortium blockchain. The experimental results demonstrate that, under the conditions of a sufficient number of vehicles, the TriBoDeS system is capable of real-time information sharing while ensuring the security of user information. Compared to conventional single-chain systems, the TriBoDeS system achieves a 2.75-time improvement in efficiency.
The big data of Internet of Vehicles contributes to the development of intelligent transportation. Privacy protection in vehicular ad hoc networks (VANETs) is the core factor to improve user and vehicle participation. This article proposes a novel blockchain-based dynamic extensible privacy protection and message authentication scheme for VANETs. It minimizes the computation cost of message authentication based on an elliptic curve and message batch verification. Based on the Chinese remainder theorem, this scheme protects transmitted message security by adaptively and dynamically responding to vehicles and roadside units accessing the VANET. It offers a smart contract-based forensics and tracing solution from the accident vehicle. In addition, strict security proof and analysis that the scheme meets the security requirements for the VANET. It evaluates the efficiency of the scheme, and the results show its practicality.
Hugues Blache, Pierre-Antoine Laharotte, Nour‐Eddin El Faouzi
The deployment of Automated and Connected Vehicles (ACV) into traffic requires certifications and validations guaranteeing high levels of safety, security and reliability. The underlying objective is to gain public acceptance by proving that automation systems might bring out a safer mobility. While plenty of methods to certify these systems are populating the literature, the scenario-based approach stands out by reducing the quantity of required Field tests to validate any new system at stake. In this study, we refine the scenario-based approach by proposing a proof of concept (PoC) for scenario reduction using criticality metrics. For this PoC, we weave a relationship between the a priori criticality of abstract functional scenarios and the words used to generate them. Once, the criticality of a subset of scenarios is qualified based on open field data (HighD), the Latent Dirichlet Allocation (LDA) clustering approach is used to generate topics and feature the relationship between observed criticality and semantics words applied to functional scenarios. The criticality degree of semantics words is used to predict the a priori criticality of unobserved functional scenarios.
The vehicular ad hoc networks (VANETs) offer additional opportunities for the development of intelligent transportation. Network-connected vehicles can help drivers adjust their driving environment and improve overall road safety by accessing information such as maps, signal status and road conditions. Due to the open nature of the communication channel, it is necessary for the receiving vehicle to verify the source and integrity of the message. Most of the existing researches are based on PKI certificate authentication model. However, there are some problems in this model: the distribution, storage and revocation of identity certificates need to consume a lot of vehicle resources, and the centralized structural framework cannot form an effective supervision of node operation. In order to solve the above problems, this paper proposes an identity authentication mechanism based on blockchain and zero-knowledge proof. In the blockchain structure composed of fog nodes, vehicles only need to store the generation seeds of communication pseudonyms to complete the communication within the coverage of fog nodes, which can effectively reduce the resource overhead caused by pseudonym distribution and revocation. Security analysis shows that our scheme can meet the security requirements of VANETs, and performance analysis shows that our scheme is better than existing schemes.
The wave of modernization around us has put the automotive industry on the brink of a paradigm shift. Leveraging the ever-evolving technologies, vehicles are steadily transitioning towards automated driving to constitute an integral part of the intelligent transportation system (ITS). The term autonomous vehicle has become ubiquitous in our lives, owing to the extensive research and development that frequently make headlines. Nonetheless, the flourishing of AVs hinges on many factors due to the extremely stringent demands for safety, security, and reliability. Cutting-edge technologies play critical roles in tackling complicated issues. Assimilating trailblazing technologies such as the Internet of Things (IoT), edge intelligence (EI), 5G, and Blockchain into the AV architecture will unlock the potential of an efficient and sustainable transportation system. This paper provides a comprehensive review of the state-of-the-art in the literature on the impact and implementation of the aforementioned technologies into AV architectures, along with the challenges faced by each of them. We also provide insights into the technological offshoots concerning their seamless integration to fulfill the requirements of AVs. Finally, the paper sheds light on future research directions and opportunities that will spur further developments. Exploring the integration of key enabling technologies in a single work will serve as a valuable reference for the community interested in the relevant issues surrounding AV research.
The arrival of autonomous vehicles (AVs) promises many great benefits, including increased safety and reduced energy consumption, pollution, and congestion. However, these engines have many security and privacy issues that could undermine the expected benefits if not addressed. AVs will provide new opportunities for hackers to carry out malicious attacks, posing a great threat to the future of mobility and data protection. The research trend in this field indicates that combining Blockchain and AI could bring strong protection for AVs against malicious attacks. Blockchain and AI have different working paradigms, but when merged, they can empower each other, and solve many security and privacy issues of AVs. AI can optimise the construction of the Blockchain to make it more efficient, secure and energy-saving, where Blockchain provides data immutability and trust mechanism for AI-based solutions and makes them more transparent, trustful, and explainable. Although some research is being conducted on this area, the topic of applying Blockchain and AI for securing AVs is not deeply investigated. In this paper, we explore the possible application of an amalgamation of Blockchain and AI solutions for securing AVs. We first introduce a classification of security and privacy threats that may arise from the application of AVs. Then, we provide an overview of recent literature regarding Blockchain and AI usage for securing AVs. Finally, we highlight limitations and challenges that may face the integration of Blockchain and AI with AVs based on our systemic review and suggest potential future directions for research in this field.
Seyed Mohammad Hashemi, Seyed Ali Hashemi, Ruxandra Mihaela Botez, Georges Ghazi
View Video Presentation: https://doi.org/10.2514/6.2023-2190.vid This paper presents a methodology for designing a highly reliable Air Traffic Management and Control (ATMC) methodology using Neural Networks and Peer-to-Peer (P2P) blockchain. A novel data-driven algorithm is designed for Aircraft Trajectory Prediction (ATP) based on Autoencoder architecture. The Autoencoder is used due to its excellent fault-tolerant ability when input data provided by the GPS is deficient. After conflict detection, P2P Blockchain is utilized for securely decentralized decision-making. The meta-controller composed of the Autoencoder and P2P blockchain performed the ATMC task very well. The validation studies were done relying on a comprehensive database of trajectories constructed using our UAS-S4 Ehécatl. The ATP accuracy was evaluated for a variety of data failures, and the performance index confirmed the Autoencoder’s excellent efficiency. Aircraft were considered in several local encounter scenarios, and their trajectories were securely managed and controlled using our designed Smart Contract developed on the Ethereum platform. Toward decentralized processing, the edge computing approach improved the P2P Blockchain performance in terms of computational complexity and processing time in real-time operations.
Vehicular Ad-hoc Network (VANET) is a modern concept of transportation that was formulated by extending Mobile Ad-hoc Networks (MANETs). VANET presents diverse opportunities to modernize transportation to enhance safety, security, and privacy. Direct communication raises various limitations, most importantly, the overhead ratio. The most prominent solution proposed is to divide these nodes into clusters. In this paper, we propose a clustering mechanism that provides security and maintains quality after the cluster formulation based on the pre-defined Quality-of-Service (QoS) parameters. To address potential attacks in the VANET environment, the proposed mechanism uses blockchain to encrypt the trust parameters’ computation. A particular trust degree of a vehicle is evaluated by the base station, encrypted with the blockchain approach, and transmitted toward roadside units (RSUs) for further utilization. The system’s performance is evaluated and compared with the existing approaches. The results show a significant improvement in terms of security and clustering quality.
Abstract Because of a large number of vehicles in Internet of Vehicle(IoV), distributed nodes and complex driving environment, data security and certification speed are easily affected. Blockchain enables different devices that do not trust each other to work together, maintain the general state in the process of information dissemination and sharing, and protect the privacy of devices. However, at present, the speed of vehicle certification in IoV is slow, and the use of idle resources is not considered. To address this problem, this paper provides a blockchain-based vehicle identity verification scheme by using a hybrid identity code verification method to ensure the nodes in the network securely share information. Meanwhile, a task processing algorithm based on time window is proposed to optimize the utilization of idle resources. In addition, the method is evaluated by simulation experiment, and the designed scheme can reduce malicious behavior of a registered vehicle in the network, and can shorten the processing task delay.
In the smart culture where everything is going intelligently, there is a need to administer the intelligent transport system and improve the traffic congestion, accidents on road accidents, and above all suitable parking allocation. It is to be noted that intelligent vehicles are equipped with the internet that enables all types of communication with the surroundings. Automotive service providers like Uber and Ola are the inspiration behind Blockchain Technology and Autonomous Vehicles. In some countries, they provide automated autonomous vehicles for smooth travel management and are known as linked or connected autonomous vehicles. Although such services are susceptible to attacks like men-in-the-middle attacks, parodying of a global positioning system, DoS attacks, sniffing, and many similar attacks, those are creating hurdles in the development of fully automated vehicles wherein the parallel autonomous industry is growing so fast as self-driving cars. Most of the studies concentrate on a central system with a single point of failure. The paper discussed about blockchain and autonomous vehicles, architecture, security, and challenges.
Number of vehicles on the road is increasing day by day and as a result, the problems caused by these vehicles are also increasing. Hence, to overcome these problems related to road safety, Vehicular Ad-hoc Network (VANET) is being used which plays a major role in solving these safety issues. VANET is a network created to transmit the data using road-side entities and vehicles. To make data transmission secure and storing data such that every user can have access to it in a secured way, the use of Blockchain is preferred. Blockchain is a distributed ledger technology that can fulfill this requirement. There are many cryptographic techniques available that can be used for carrying out data transmission in VANET. This paper discusses and analyses various VANET system ideas for which researchers have carried out implementation. Still, there are many research challenges for carrying out secure transmission in VANET. This paper also proposes an approach to store the vehicular data securely by using Blockchain technology.
The rapid evolution of vehicles leads to future vehicular technologies which improve transportation systems. One prominent technology is platooning networks where a vehicle, called platoon head, leads to a group of vehicles. Platoons not only improve the vehicle driving experience and safety but also reduce traffic congestion. Despite all these benefits, the authentication of vehicles for platoon networks is a critical problem in order to share and get access control to other vehicles. Moreover, due to the platoon networks’ complexity and high dynamic formation, it requires an efficient and robust authentication mechanism between vehicles. In this paper, we propose a blockchain-based authentication protocol for platoons using the concept of the platoon as a service. Our approach not only proposes an efficient protocol that reduces the computation and communication for the authentication but also improves the security with the use of a distributed authentication (Auth-I) data ledger across the network and a token-based platoon authentication (Auth-II), which is suitable for practical implementations in areas with different traffic density.
Connected and Autonomous Vehicles (CAVs) are an emerging solution to the issues of safe and sustainable transportation systems in the future. One major transport technology for CAVs is Cooperative Adaptive Cruise Control (CACC), for which unsignalized autonomous intersection crossing is a growing use case. CACC relies heavily on inter-vehicular communication and is thus vulnerable to message forgery and jamming attacks. Most solutions for CACC focus exclusively on enhancing efficiency or security but do not offer an integrated framework for achieving both on a large scale. In this paper, we propose a Blockchain-integrated Multi-Agent Deep Reinforcement Learning (Block-MADRL) architecture for enhancing the efficiency of CACC while cooperatively detecting attacks, reducing the fuel efficiency of identified attackers and securely notifying the overall network. Our approach uses multi-agent deep reinforcement learning to find fuel and throughput optimizing solutions for CACC and a cooperative verification mechanism based on Extended Isolation Forest (EIF) for attack detection. Attacker data is securely stored in a Road Side Unit (RSU) level blockchain, and we design a low-latency, high throughput consensus protocol for speedy and secure data dissemination. Simulation results indicate over 29.5% better lane throughput with our approach during acceleration forgery attack, up to 23% induced reduction in fuel efficiency of malicious vehicles, 17.6% higher blockchain throughput through our consensus protocol and over 8% improvement in attack detection rate compared to the state-of-the-art.
Connected and autonomous vehicle (CAV) technologies are among the most heavily researched automotive technologies. Under the categories of transportation automation levels as defined by Society of Automobile Engineers International, this chapter provides a comprehensive overview of connected vehicle (CV) and autonomous vehicle (AV) technologies, as well as other automation-based vehicles such as cooperative vehicles and autonomous shuttles. To better understand the CV's collective functionality, the existing CV-supported systems are categorized into CV-aided safety systems, mobility systems, and environmental systems. A specific technology for CAV systems, the distributed ledger technology (DLT), is presented; it has emerged as a potentially revolutionary approach across a variety of industries including transportation, finance, supply chain management and logistics, and energy. An analysis is included of the potential to effectively apply CAV technologies and associated systems to achieve the envisioned future of transportation.