Vehicular Data Research Articles

On Vehicular Data Aggregation in Federated Learning

Vehicular federated learning systems will be beneficial to predicting traffic events in future intelligent cities. However, they might leak private information upon model updates. Hence, an honest but curious server could infer private information, such as the route of a vehicle. In this study, we elaborate on the nature of such privacy leakage caused by gradient sharing. With a simulated scenario, we focus on determining who is in danger of privacy threats and how successful a route inference attack can be. Results indicate that vanilla federated learning exposes intra-city and commuter traffic to successful location inference attacks. We also found that an adversarial aggregator server successfully infers the moving time of vehicles traveling during low-traffic periods.

An analytical study Of Vehicular Effects on Sound in Residential, Silent And Mixed zones Of Sylhet

Noise is a form of environmental pollution that influences our life and comfort. Because of growing populations and increasing urbanization, noise is more common and is getting worse. Sylhet is one of the Bangladesh's most rapid growth cities. In recent years, it has experienced unprecedented population increase. During the past decades, there has been a noticeable growth in the number of individuals moving to Sylhet City from other regions of the nation, particularly for jobs and education. As the lifestyle and facilities of this city increase compared to the suburbs and countryside areas, nowadays vast amounts of individuals are interested in living in the Sylhet city. As the population is increasing, the number of transport facilities (volume of vehicles) are also increasing. To be more specific, the number of private cars ownership is increasing day by day. As a result, now the sound level is increasing much more than the standard level. Throughout the working days, between 6 am and 12 am, noise levels were monitored at 15 significant places (residential, silent and mixed zones) across the city. A sound meter was used to conduct the study. Vehicular data were converted to passenger car unit (PCU) conversion factor. Geometric design standard manual (LGED, 2005) was used to calculate PCU by passenger car factor or passenger car equivalent. “Environmental conservation rules-1997” was considered as basic to analyze the data. The study was conducted to quantify the amount of noise pollution that happens in the city's mixed, residential, and silent zones. It also revealed the impact of vehicular noise. It was found that most of the time the sound level at every site remained consistently much above the allowable limit and vehicular noise played a significant role in every case. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 10(1), 2023 P 10-16

Investigation on cellular LTE C-V2X network serving vehicular data traffic in realistic urban scenarios

The need for reliable vehicular communication is an essential precondition of future Cooperative Intelligent Transportation Systems (C-ITS). The C-ITS applications and their diverse requirements necessitate a hybrid approach that combines ad hoc and cellular networks to support seamless and robust connectivity. While the research showed tremendous potential, the successful adaptation of the hybrid approach depends on the optimal understanding of its impact on network and protocol performance in an actual reference scenario. This work, which has practical implications for the field, focuses on creating a realistic representation of the dual-link-enabled Hybrid Vehicular Network (HVN) in the simulator. Several enhancements were implemented into a network simulator, including multiple Radio Access Technologies (multi-RAT) support, models for path loss predictions, static subscribers, background interference, and SINR throughput mapping within a multi-cell environment. We then presented a parameterized data traffic steering algorithm that enables switching between Dedicated Short-Range Communication (DSRC) and Long Term Evolution (LTE) RAT based on channel congestion and cell load conditions. The simulation result shows the high potential of a dual-link-enabled hybrid approach and its implications on both networks under realistic vehicular networking conditions. The investigations reveal that an intelligent decision to steer traffic can target balanced connectivity and highly reliable vehicular communication at a reduced network load.

A privacy-preserving data aggregation system based on blockchain in VANET

In the realm of vehicular ad hoc networks (VANETs), data aggregation plays a pivotal role in bringing together data from multiple vehicles for further processing and sharing. Erroneous data feedback can significantly impact vehicle operations, control, and overall safety, necessitating the assurance of security in vehicular data aggregation. Addressing the security risks and challenges inherent in data aggregation within VANETs, this paper introduces a blockchain-based scheme for secure and anonymous data aggregation. The proposed scheme integrates cloud computing with blockchain technology, presenting a novel blockchain-based data aggregation system that robustly supports efficient and secure data collection in VANETs. Leveraging key escrow resilience mechanisms, the solution ensures the security of system keys, preventing the security problems caused by keys generated by third parties alone in the past. Furthermore, through secondary data aggregation, fine-grained data aggregation is achieved, providing effective support for cloud services in VANETs. The effectiveness of the proposed scheme is confirmed through security analysis and performance evaluations, demonstrating superior computational and communication efficiency compared existing alternatives.

Security, Privacy, and Decentralized Trust Management in VANETs: A Review of Current Research and Future Directions

Vehicular Ad Hoc Networks (VANETs) are powerful platforms for vehicular data services and applications. The increasing number of vehicles has made the vehicular network diverse, dynamic, and large-scale, making it difficult to meet the 5G network’s demanding requirements. Decentralized systems are interesting and provide attractive services because they are publicly available (transparency), have an append-only ledger (robust integrity protection), remove single points of failure, and enable distributed key management and communication in a peer-to-peer network. Researchers dedicated substantial efforts to advancing vehicle communications, however conventional cryptographic mechanisms are insufficient which enabled us to look at decentralized technologies. Therefore, we revisit decentralized approaches with VANETs. Endpoint devices hold a wallet which may incorporate threshold key management methods like MPC wallets, HD Wallets, or multi-party threshold ECDSA/EdDSA/BLS. We also discuss trust management approaches and demonstrate how decentralization can improve integrity, security, privacy, and resilience to single points of failure. We also conduct a comprehensive review, comparing them with current requirements, and the latest authentication and secure communication architectures, which require the involvement of trusted but non-transparent authorities in certificate issuance/revocation. We highlight the limitations of these schemes from PKI deployment and recommend future research, particularly in the realm of quantum cryptography.

Online Processing of Vehicular Data on the Edge Through an Unsupervised TinyML Regression Technique

The Internet of Things (IoT) has made it possible to include everyday objects in a connected network, allowing them to intelligently process data and respond to their environment. Thus, it is expected that those objects will gain an intelligent understanding of their environment and be able to process data more efficiently than before. Particularly, such edge computing paradigm has allowed the execution of inference methods on resource-constrained devices such as microcontrollers, significantly changing the way IoT applications have evolved in recent years. However, although this scenario has supported the development of Tiny Machine Learning (TinyML) approaches on such devices, there are still some challenges that require further investigation when optimizing data streaming on the edge. Therefore, this article proposes a new unsupervised TinyML regression technique based on the typicality and eccentricity of the samples to be processed. Moreover, the proposed technique also exploits a Recursive Least Squares (RLS) filter approach. Combining all these features, the proposed method uses similarities between samples to identify patterns when processing data streams, predicting outcomes based on these patterns. The results obtained through the extensive experimentation utilizing vehicular data streams were highly encouraging. The proposed algorithm was meticulously compared with the RLS algorithm and Convolutional Neural Networks (CNN). It exhibited significantly superior performance, with mean squared errors that were 4.68 and 12.02 times lower, respectively, compared to the aforementioned techniques.

Improving Driving Style in Connected Vehicles via Predicting Road Surface, Traffic, and Driving Style

This paper investigates the application of ensemble learning in improving the accuracy and reliability of predictions in connected vehicle systems, focusing on driving style, road surface quality, and traffic conditions. Our study’s central methodology is the voting classifier ensemble method, which integrates predictions from multiple machine learning models to improve overall predictive performance. Specifically, the ensemble method combines insights from random forest, decision tree, and K-nearest neighbors models, leveraging their individual strengths while compensating for their weaknesses. This approach resulted in high accuracy rates of 94.67% for driving style, 99.10% for road surface, and 98.80% for traffic predictions, demonstrating the robustness of the ensemble technique. Additionally, our research emphasizes the importance of model explanation ability, employing the tree interpreter tool to provide detailed insights into how different features influence predictions. This paper proposes a model based on the algorithm GLOSA for sharing data between connected vehicles and the algorithm CTCRA for sending road information to navigation application users. Based on prediction results using ensemble learning and similarity in driving styles, road surface conditions, and traffic conditions, an ensemble learning approach is used. This not only contributes to the predictions’ transparency and trustworthiness but also highlights the practical implications of ensemble learning in improving real-time decision-making and vehicle safety in intelligent transportation systems. The findings underscore the significant potential of advanced ensemble methods for addressing complex challenges in vehicular data analysis.

An Efficient Fault-Tolerant Vehicular Data Forwarding Scheme for Timely and Reliable Data Transmission

With the increasing demands of various applications on vehicles, such as road condition sensing, traffic management, location-based services, and others both academic researchers and automotive industries pay much attention to Vehicular Networks. Due to the rapid change of network topology and intermittent connection, it is a big challenge to satisfy the service requests from a large number of vehicular users. Further, network faults disrupt communication among vehicles, which affects the reliability and timely data delivery in vehicular networks. In this paper, for timely and reliable data transmission, an efficient data forwarding scheme for vehicular networks is proposed. Also, the paper addresses three types of network faults along with the data forwarding issue by selecting an optimal route with a higher probability of connectivity and lower experienced delay. The proposed scheme detects the faults dynamically and takes necessary actions to recover from the fault. Extensive simulation has been performed to evaluate the proposed scheme. Compared to the existing AODV- D approach and SRCP scheme, the proposed scheme attains better performance in terms of packet delivery ratio, end-to-end delay, and control overhead.

Mobile Collaborative Learning Over Opportunistic Internet of Vehicles

Machine learning models are widely applied for vehicular applications, which are essential to future intelligent transportation system (ITS). Traditional model training methods commonly employ a client-server architecture to perform local training and global iterative aggregations, which can consume significant bandwidth resources that are often absent in vehicular networks, especially in high vehicle density scenarios. Modern vehicle users naturally can collaboratively train machine learning models as they are the data owner and have strong local computing power from the onboard units (OBU). In this paper, we propose a novel collaborative learning scheme for mobile vehicles that can utilize the opportunistic vehicle-to-roadside (V2R) communication to exploit the common priors of vehicular data without interaction with a centralized coordinator. Specifically, vehicles perform local training during the driving journey, and simply upload its local model to roadside unit (RSU) encountered on the way. RSU's model will be updated accordingly and sent back to the vehicle via the V2R communication. We have theoretically shown that RSUs' models can eventually converge without a backhaul connection. Extensive experiments upon various road configurations demonstrate that the proposed scheme can efficiently train models among vehicles without dedicated Internet access and scale well with both the road range and vehicle density.

Data poisoning attacks on traffic state estimation and prediction

Data has become ubiquitous nowadays in transportation, including vehicular data and infrastructure-generated data. The growing reliance on data poses potential cybersecurity issues to transportation systems, among which the so-called “data poisoning” attacks by adversaries are becoming increasingly critical. Such attacks aim to compromise a system’s performance by adding systematic and malicious noises, perturbations, or deviations to the dataset used by the system. Formal investigations of data poisoning attacks are essential for understanding the attacks and developing effective defense methods. This study develops a general data poisoning attack model for traffic state estimation and prediction (TSEP) that is a basic application in transportation. We first formulate data poisoning attacks as a general sensitivity analysis of parameterized optimization problems over parameter changes (i.e., data perturbations) and study the Lipschitz continuity property of the solution with the presence of general (equality and inequality) constraints. Then, we develop attack models that fit a broader spectrum of learning applications (such as TSEP) by extending existing models that only focus on learning problems with no or equality constraints (widely used in the cybersecurity field). Since the solution of such general problems is often continuous but not differentiable with data changes, we apply the generalized implicit function theorem to compute the semi-derivatives that express how the TSEP solution responds to data perturbations. The semi-derivatives enable us to evaluate TSEP models’ vulnerability (at each data point) and solve the proposed attack model. We demonstrate the generality and effectiveness of the proposed method on two TSEP models using mobile sensing data.

Improving Security in the Internet of Vehicles: A Blockchain-Based Data Sharing Scheme

To ensure the aggregation of a high-quality global model during the data-sharing process in the Internet of Vehicles (IoV), current approaches primarily utilize gradient detection to mitigate malicious or low-quality parameter updates. However, deploying gradient detection in plain text neglects adequate privacy protection for vehicular data. This paper proposes the IoV-BDSS, a novel data-sharing scheme that integrates blockchain and hybrid privacy technologies to protect private data in gradient detection. This paper utilizes Euclidean distance to filter the similarity between vehicles and gradients, followed by encrypting the filtered gradients using secret sharing. Moreover, this paper evaluates the contribution and credibility of participating nodes, further ensuring the secure storage of high-quality models on the blockchain. Experimental results demonstrate that our approach achieves data sharing while preserving privacy and accuracy. It also exhibits resilience against 30% poisoning attacks, with a test error rate remaining below 0.16. Furthermore, our scheme incurs a lower computational overhead and faster inference speed, markedly reducing experimental costs by approximately 26% compared to similar methods, rendering it suitable for highly dynamic IoV systems with unstable communication.

Elucidating Super-Compressible States in Inner Structure of Vehicular Flow

The article deals with the open questions in the theory of vehicular headway modeling. Specifically, the question of the existence of anomalous constellations in vehicular traffic micro-structure, in which the rate of fluctuations (measured by the stochastic compressibility) exceeds the fluctuation level of systems with non-interacting elements. The solution to this open problem is converted into the mathematical format working with the so-called balance particle systems, where seeking relevant relations is more straightforward and thus significantly easier. Presented research has shown that unit compressibility represents (despite popular opinion) the upper limit only for particle systems, in which there is no attractive interaction between the particles. In the article, the specific system is constructed in which the presence of an attractive force component will cause higher fluctuations than in the Poisson systems of non-interacting elements. This means that traffic constellations with higher compressibility (so-called super-compressible constellations) can be explained either by a discrepancy between the empirical traffic flow and the mathematical model used, or by the presence of attractive forces acting between individual vehicles. Using empirical vehicular data (measured on two parallel freeway lanes under reconstruction), we show that super-compressible states occur even though overtaking is prohibited. This means, therefore, that these super-compressible states arose without a doubt due to the mutual attraction of successive vehicles. In addition, the article shows that the presence of the aforementioned attractive forces appears predominantly in the fast lane, and only in situations where the traffic density is relatively low. At higher densities, the two freeway lanes are markedly synchronized, the opportunity for a sporty style of driving vanishes and the reason for changing lanes disappears. Under these circumstances, the attractive force component vanishes, which finally leads to the transition of the entire traffic system back to standard sub-compressible states.

Secure Data Sharing over Vehicular Networks Based on Multi-sharding Blockchain

Internet of Vehicles (IoV) has become an indispensable technology to bridge vehicles, persons, and infrastructures and is promising to make our cities smarter and more connected. It enables vehicles to exchange vehicular data (e.g., GPS, sensors, and brakes) with different entities nearby. However, sharing these vehicular data over the air raises concerns about identity privacy leakage. Besides, the centralized architecture adopted in existing IoV systems is fragile to single point-of-failure and malicious attacks. With the emergence of blockchain technology, there is the chance to solve these problems due to its features of being tamper-proof, traceability, and decentralization. In this article, we propose a privacy-preserving vehicular data sharing framework based on blockchain. In particular, we design an anonymous and auditable data sharing scheme using Zero-Knowledge Proof (ZKP) technology so as to protect the identity privacy of vehicles while preserving the vehicular data auditability for Trusted Authorities (TAs). In response to high mobility of vehicles, we design an efficient multi-sharding protocol to decrease blockchain communication costs without compromising the blockchain security. We implement a prototype of our framework and conduct extensive experiments and simulations on it. Evaluation and analysis results indicate that our framework can not only strengthen system security and data privacy but also reduce communication complexity by \(O(\frac{n\sqrt {m}}{m^2})\) times compared to existing sharding protocols.

Advanced Intrusion Detection in Vehicular Networks: Empowering Security through Hybrid Off-loading Techniques and Enhanced Radial Bias Neural Network

Over the last several decades, the implementation of ITS has shown to be the most efficient and successful strategy for expanding the variety of current transportation networks. Vehicle-based offloading of data going to be essential for forthcoming networking innovations like D2D and 5G due to the substantial contribution it makes to efficiently using network capability while wasting minimal power. Information transmissions that would normally need a cellular network's infrastructure may instead be made using alternative networking mechanisms including Bluetooth, WiFi, and opportunistic communications. Data offloading has the ability to significantly increase the efficiency with which network resources are used. The offloading of data from vehicles has a considerable impact on the strain on cellular networks. It helps the network achieve higher throughput by facilitating the simultaneous reception of data by a large number of users. First, we must establish that the problem of Vehicular data offloading is an NP-hard target set selection (TSS) issue before we can even begin to characterize it. Using a combination of Hybrid PSO and GWO, TSS selects a small group of nodes to do the redundant data exchange (Particle Swarm Optimization with Gray Wolf Optimization). Collaboration between individuals and ISPs to identify effective aim sets may provide useful insights. If malicious users are present in the target group, they may slow down network activity by spoofing or by reducing the network's offloading capacity. It is possible that the whole network's performance would suffer as a direct result of these malicious users. In this study, we suggest a hybrid approach to communication for specifying the intended audience. We take use of the characteristics of opinion dynamics amongst users to get around the issue of overlapping community detection. Trust-based metrics inferred from users' activities are used to ensure the safety of the target set. In order to call 911, the suggested work additionally incorporates a method of sorting and classifying the offload limitations through Radial Bias Neural Network (RBNN). The following may be determined with the use of the proposed work's performance indicators: precision, entropy, and delay.

DeepSecDrive: An explainable deep learning framework for real-time detection of cyberattack in in-vehicle networks

Autonomous driving (AD) technologies are becoming increasingly popular, promising significant enhancements in road safety and efficiency. However, the susceptibility of autonomous vehicles to cyberattacks highlights the critical need for protecting the security and dependability of in-vehicular networks (IVNs). This study proposes a lightweight, efficient, and explainable deep learning framework, DeepSecDrive, for early detection of security threats against IVN. DeepSecDrive presents robust feature extractor units designed with deformable convolutions and a lightweight non-local network (LNLN). The former promotes dynamic change of receptive field to enhance extraction of complex features from the traffics of in-vehicle networks, while the latter enables learning contextual representations and non-local relationships in vehicular data. DeepSecDrive integrates Shapley Additive exPlanations to construct multi-level interpretability units, providing local and global explanations for model decisions in terms of feature contributions to the overall performance. Proof-of-concept experiments are conducted to evaluate the efficacy and durability of DeepSecDrive on real-world IVN attacks from Car-Hacking dataset, and the experimental results demonstrate the ability of our model to outperform state-of-the-art detection methods.

Traffic prediction assisted wavelength allocation in vehicle-to-infrastructure communication: A fiber-wireless network based framework

The advent of the next generation of connected and autonomous cars offers immense opportunities for both users as well as service providers. In particular, fiber-wireless (FiWi) based vehicle-to-infrastructure (V2I) network can facilitate some of the stringent requirements of sixth-generation (6G) vehicular networks, including higher capacity, lower delay, and ubiquitous connectivity. FiWi based V2I network integrates the next generation passive optical network 2 (NG-PON2) with IEEE 802.11p based V2I network. In this work, we first review the various kinds of vehicular data traffic and their desired key performance indicators (KPIs), namely throughput, delay, and reliability. Depending on the KPI requirements, the V2I traffic is classified among four classes and assigned to different transmission containers (T-CONTs) of the optical network unit (ONU). Further, in order to minimize the delay of the network, we propose a machine learning (ML) based T-CONT priority assignment wavelength allocation algorithm that minimizes the number of wavelength switching instances in the PON. The performance of the proposed ML-based wavelength allocation algorithm is compared with the other approaches, namely random and equal T-CONT based wavelength allocation algorithms. Simulation results demonstrate the efficiency of the proposed algorithm vis-a-vis other approaches in terms of end-to-end (e2e) delay, throughput, and reliability.

An Efficient Vehicle-Assisted Aggregate Authentication Scheme for Infrastructure-Less Vehicular Networks

In recent years, growing research interest from both industry and academia has been aroused to the vehicular networks, which is regarded as the fundamental component of the modern intelligent transportation system (ITS). Lots of remarkable research outputs with respect to secure vehicular data interactions and user privacy preservation has been witnessed. However, the existing schemes all focus on the common vehicular communication scenarios where facilities are deployed, whereas the secure data exchange in the abnormal infrastructure-less vehicular environment has not been properly investigated. To deal with unpredictable abnormal situations caused by artificial or natural disasters such as earthquakes and floods, a distinctive vehicle-assisted aggregate authentication mechanism for infrastructure-less vehicular networks is presented in this paper. With assistance from the neighboring vehicles, the homomorphic signature involving all requesting vehicles is generated and forwarded to the remaining functional RSUs. Meanwhile, vehicular group communication among the validated entities is enabled. Additionally, the fault-tolerant verification method is adopted such that the ineffective entities can be easily distinguished and removed without interfering with other requesting vehicles. The security proofs and discussions regarding vital security properties are presented, while the performance analysis follows. Compared with the state-of-the-art, advantages in terms of security and performance properties can be proved.

Neurophysiological mental fatigue assessment for developing user-centered Artificial Intelligence as a solution for autonomous driving.

The human factor plays a key role in the automotive field since most accidents are due to drivers' unsafe and risky behaviors. The industry is now pursuing two main solutions to deal with this concern: in the short term, there is the development of systems monitoring drivers' psychophysical states, such as inattention and fatigue, and in the medium-long term, there is the development of fully autonomous driving. This second solution is promoted by recent technological progress in terms of Artificial Intelligence and sensing systems aimed at making vehicles more and more accurately aware of their "surroundings." However, even with an autonomous vehicle, the driver should be able to take control of the vehicle when needed, especially during the current transition from the lower (SAE < 3) to the highest level (SAE = 5) of autonomous driving. In this scenario, the vehicle has to be aware not only of its "surroundings" but also of the driver's psychophysical state, i.e., a user-centered Artificial Intelligence. The neurophysiological approach is one the most effective in detecting improper mental states. This is particularly true if considering that the more automatic the driving will be, the less available the vehicular data related to the driver's driving style. The present study aimed at employing a holistic approach, considering simultaneously several neurophysiological parameters, in particular, electroencephalographic, electrooculographic, photopletismographic, and electrodermal activity data to assess the driver's mental fatigue in real time and to detect the onset of fatigue increasing. This would ideally work as an information/trigger channel for the vehicle AI. In all, 26 professional drivers were engaged in a 45-min-lasting realistic driving task in simulated conditions, during which the previously listed biosignals were recorded. Behavioral (reaction times) and subjective measures were also collected to validate the experimental design and to support the neurophysiological results discussion. Results showed that the most sensitive and timely parameters were those related to brain activity. To a lesser extent, those related to ocular parameters were also sensitive to the onset of mental fatigue, but with a delayed effect. The other investigated parameters did not significantly change during the experimental session.

DRL-Based Adaptive Sharding for Blockchain-Based Federated Learning

Blockchain-based Federated Learning (FL) technology enables vehicles to make smart decisions, improving vehicular services and enhancing the driving experience through a secure and privacy-preserving manner in Intelligent Transportation Systems (ITS). Many existing works exploit two-layer blockchain-based FL frameworks consisting of a mainchain and subchains for data interactions among intelligent vehicles, which resolve the limited throughput issue of single blockchain-based vehicular networks. However, the existing two-layer frameworks still suffer from (a) strong dependency on predetermined and fixed parameters of vehicular blockchains which limit blockchain throughput and reliability, and (b) high communication costs incurred by interactions among intelligent vehicles between the mainchain and subchains. To address the above challenges, we first design an adaptive blockchain-enabled FL framework for ITS based on blockchain sharding to facilitate decentralized vehicular data flows among intelligent vehicles. A streamline-based shard transmission mechanism is proposed to ensure communication efficiency almost without compromising the FL accuracy. We further formulate the proposed framework and propose an adaptive sharding mechanism using Deep Reinforcement Learning to automate the selection of parameters of vehicular shards. Numerical results clearly show that the proposed framework and mechanisms achieve adaptive, communication-efficient, credible, and scalable data interactions among intelligent vehicles.

Aggregated Zero-Knowledge Proof and Blockchain-Empowered Authentication for Autonomous Truck Platooning

Platooning technologies enable trucks to drive cooperatively and automatically, providing benefits including less fuel consumption, greater road capacity, and safety. To establish trust during dynamic platooning formation, ensure vehicular data integrity, and guard platoons against potential attackers in mixed fleet environments, verifying any given vehicle’s identity information before granting it access to join a platoon is pivotal. Besides, due to privacy concerns, truck owners may be reluctant to disclose private vehicular information, which can reveal their business data to untrusted third parties. To address these issues, this is the first study to propose an aggregated zero-knowledge proof and blockchain-empowered system for privacy-preserving identity verification in truck platooning. We provide the correctness proof and the security analysis of our proposed authentication scheme, highlighting its increased security and fast performance. The platooning formation procedure is re-designed to seamlessly incorporate the proposed authentication scheme, including the 1st catch-up and cooperative driving steps. The blockchain performs the role of verifier within the authentication scheme and stores platooning records on its digital ledger to guarantee data immutability and integrity. In addition, the proposed programmable access control policies enable truck companies to define who is allowed to access their platoon records. We implement the proposed system and perform extensive experiments on the Hyperledger platform. The results show that the blockchain can provide low latency and high throughput, the aggregated approach can offer a constant verification time of 500 milliseconds regardless of the number of proofs, and the platooning formation only takes seconds under different strategies. The experimental results demonstrate the feasibility of our design for use in real-world truck platooning.