Intelligent Connected Vehicles Research Articles

Cellular Vehicle-to-Everything Automated Large-Scale Testing: A Software Architecture for Combined Scenarios

As the commercialisation of Intelligent Connected Vehicles (ICVs) accelerates, Vehicle-to-Everything (V2X)-based general testing and assessment systems have emerged at the forefront of the research. Current field testing schemes mostly follow the norms of traditional vehicle tests. In contrast, Original Equipment Manufacturers (OEMs) have increasingly focused on the potential influence of V2X communication performance on the application response characteristics. Our previous work resulted in a C-V2X (Cellular-V2X) large-scale testing system (LSTS) for communication performance testing. However, when addressing the need to combine application and communication, the system software faces confronts heightened technical challenges. This paper proposes a layered software architecture for the automated C-V2X LSTS, which is tailored to combined scenarios. This architecture integrates scenario encapsulation technology with a large-scale node array deployment strategy, enabling communication and application testing under diversified scenarios. The experimental results demonstrate the scalability of the system, and a case study of Forward Collision Warning (FCW) validates the effectiveness and reliability of the system.

Over-the-air upgrading for enhancing security of intelligent connected vehicles: a survey

The continuous improvement in the connectivity, automation and autonomy levels of Intelligent Connected Vehicles (ICVs) significantly increases the probability of potential security threats. Over-the-Air (OTA) is a promising technique for upgrading features of ICVs and enhancing their reliability and security against environmental disturbances as well as malicious attacks. To better understand the potential security risks and possible countermeasures, we survey research works in ICV security during OTA from cloud upgrade, terminal upgrade, and object upgrade. We also summarize existing methods in OTA upgrading techniques and systematically investigate the overall framework of OTA upgrading methods from the perspectives of Software-Over-the-Air (SOTA) and Firmware-Over-the-Air (FOTA).We further discuss possible mitigation strategies and open issues yet to be resolved in this research direction. This survey shows that OTA provides a powerful technique for upgrading the ICV features and improving ICV security.

Enhancing Cooperative Lane-Changing for Intelligent Connected Vehicles: A DDPG-Based Approach

Current research on lane-changing for intelligent connected vehicles (ICVs) primarily focuses on the control of a single ICV. Even studies on cooperative lane-changing among multiple ICVs often use fixed control algorithms, which are difficult to adapt to constantly changing traffic conditions. This paper proposes a method based on the deep deterministic policy gradient (DDPG) model. The DDPG algorithm has strong generalization capabilities, enabling it to handle complex traffic scenarios. Combined with the design of innovative reward functions, it successfully achieves the control of cooperative lane-changing for multiple ICVs. The novelty of this model is mainly reflected in the implementation of multiple ICVs control and cooperative strategies through the design of a speed reward function. Specifically, when two or more ICVs come within a 15-m range, a collaborative strategy is implemented. In this strategy, the original speeds of the vehicles involved are replaced with their average speed. This average speed is then used to compute the velocity reward. Moreover, an additional headway reward is introduced when two vehicles are on the same lane with a distance of less than 10[Formula: see text]m between them, further optimizing lane-changing decisions and enhancing driving efficiency. The innovative approach described in this paper has been validated through simulations in the highway-env simulation environment. The model’s effectiveness was tested under various conditions, including different road densities and numbers of ICVs. Results indicate that with an increasing number of episodes, the model consistently enhances both the fleet’s and each individual vehicle’s average speed and steps. It also ensures that the headway is maintained within a desired range of 10–20[Formula: see text]m. This paper showcases the potential applications of a new DDPG-based multi-networked vehicular cooperative lane-changing decision model across different scenarios. These findings provide important reference values for future research and development in intelligent transportation systems (ITS), particularly in optimizing vehicle cooperative behaviors and enhancing traffic efficiency. This paper emphasizes the model’s potential to significantly impact the management and operation of future vehicular networks, paving the way for more sophisticated and efficient transportation systems.

Hyper-relational interaction modeling in multi-modal trajectory prediction for intelligent connected vehicles in smart cites

Trajectory prediction of surrounding traffic participants is vital for the driving safety of Intelligent Connected Vehicles (ICVs). It has been enabled with the help of the availability of multi-sensor information collected by ICVs. For accurately predicting the future movements of traffic agents, it is crucial to subtlety model the inter-agent interaction. However, existing works focus on the correlations between agents and the map information while neglecting the importance of directly modeling the impact of map elements on inter-agent interactions, the direct modeling of which is beneficial for the representation of agent behaviors. Against this background, we propose to model the hyper-relational interaction, which incorporates map elements into the inter-agent interaction. To tackle the hyper-relational interaction, we propose a novel Hyper-relational Multi-modal Trajectory Prediction (HyperMTP) approach. Specifically, a hyper-relational driving graph is first constructed and the hyper-relational interaction is represented as the hyperedge, directly connecting to various nodes (i.e., agents and map elements). Then a structure-aware embedding initialization technique is developed to obtain unbiased initial embeddings. Afterward, hypergraph dual-attention networks are designed to capture correlations between graph elements while retaining the hyper-relational structure. Finally, a heterogeneous Transformer is devised to further capture the correlations between agents’ states and their corresponding hyper-relational interactions. Experimental results show that HyperMTP consistently outperforms the best-performing baseline with an average improvement of 4.8% across two real-world datasets. Moreover, HyperMTP also boosts the interpretability of trajectory prediction by quantifying the impact of map elements on inter-agent interactions.

A Roadside Precision Monocular Measurement Technology for Vehicle-to-Everything (V2X).

Within the context of smart transportation and new infrastructure, Vehicle-to-Everything (V2X) communication has entered a new stage, introducing the concept of holographic intersection. This concept requires roadside sensors to achieve collaborative perception, collaborative decision-making, and control. To meet the high-level requirements of V2X, it is essential to obtain precise, rapid, and accurate roadside information data. This study proposes an automated vehicle distance detection and warning scheme based on camera video streams. It utilizes edge computing units for intelligent processing and employs neural network models for object recognition. Distance estimation is performed based on the principle of similar triangles, providing safety recommendations. Experimental validation shows that this scheme can achieve centimeter-level distance detection accuracy, enhancing traffic safety. This approach has the potential to become a crucial tool in the field of traffic safety, providing intersection traffic target information for intelligent connected vehicles (ICVs) and autonomous vehicles, thereby enabling V2X driving at holographic intersections.

Distributed finite-time attack detection and estimation of intelligent connected vehicle platoon

ABSTRACT This paper presents a distributed finite-time observer design approach for the problem of intelligent connected vehicles (ICVs) containing both attack signals and unknown disturbances. Firstly, an analysis is conducted on the two types of network attacks that ICVs may encounter, followed by the construction of a dynamic vehicle model and its transformation into a state space equation. Secondly, an intermediate variable is introduced to design a distributed attack estimation observer, which both ensures accurate estimation of the vehicle safety state and attack signal within a limited time and overcomes the constraints of traditional observers. Additionally, sufficient conditions for the finite-time boundedness of the observer error system with H ∞ performance are provided in the form of linear matrix inequalities. Furthermore, an adaptive threshold attack detection method is designed. Finally, the simulation results validate the efficiency of the design methods.

Modeling risk potential fields for mandatory lane changes in intelligent connected vehicle environment

In the environment of Intelligent Connected Vehicles (ICVs), the ICV system is capable of gathering, processing, and transmitting the majority of traffic information to each vehicle, thereby empowering drivers to make more informed and secure decisions. Nevertheless, ICVs are still incapable of completely avoiding traffic conflicts and accidents. Consequently, developing a risk assessment model for ICVs is crucial for their deployment. To investigate the dynamic patterns of lane-changing vehicle risks within ICV environments, this study developed a quantitative relationship model based on the Artificial Potential Field (APF) theory. The model correlates the Total Potential Field (TPF) of mandatory lane-changing (MLC) vehicle with the longitudinal/transverse gravitational potential field (LGPF/TGPF) and the longitudinal/transverse repulsion potential field (LRPF/TRPF). Subsequently, three sets of Human Machine Interface (HMI) systems were developed to delineate distinct ICV environments: baseline, warning group, and guidance group. The baseline furnishes fundamental data, the warning group supplements preceding vehicle icons and real-time headway information, whereas the guidance group additionally augments speed and voice guidance functionalities. Following this, a simulated driving experiment involving 43 participants was carried out to simulate MLC scenarios in highway merging sections. Using enumeration and gradient descent methods, the quantitative relationship between different types of risk fields was determined. The findings indicated that the ICV environment significantly impacts the mean values of LGPF, TGPF, LRPF (Leading Vehicle, LV), and TRPF, while insignificantly affecting the mean value of LRPF (Following Vehicle, FV). Nevertheless, a notable discrepancy in the mean value of LRPF (FV) was observed between the warning and guidance groups. Comparative results with lane positions demonstrate that the proposed TPF accurately depicts vehicle lane-changing behaviors. Comparative results with classical risk indicators indicate that TPF more accurately depicts the actual driving risks and variation trends faced by vehicles in different MLC processes. The research findings will be pivotal in improving real-time control and guidance strategies for vehicles in highway merging sections under ICV environments.

Research on the Antecedents of Data Privacy Concern Toward Intelligent Connected Vehicles

Depending on Internet of Vehicle (IoV) and on-board units in 5 G, intelligent connected vehicles (ICVs) gradually become the digital infrastructure and data hub to provide personalized in-vehicle infotainment (IVI), which naturally collect, use, and share the data related to users’ bio-informatics, travel patterns, and usage habits. The privacy concern (PC) will be generated when using those data services. In this article, we construct the structural equation model (SEM) to study the influencing factors of users’ PC and self-disclosure behavior (SD) regarding to ICV data services from the personality traits (PTs) perspective. A total of 481 valid questionnaires from 700 IVI users was collected. Results show that extraversion (EXT), conscientiousness (CNS), neuroticism (NEUR), privacy invasion experience (PIE), and privacy self-efficacy (PE) have significant impacts on PC. Besides, PC does not directly affect SD, but is indirectly affected by limiting profile visibility (LPV). Through revealing the relationship among PTs, PC and SD toward ICV data services, we aim to provide insights for vehicle data governance to facilitate the human-vehicle interaction.

Multi-order feature interaction-aware intrusion detection scheme for ensuring cyber security of intelligent connected vehicles

The evolution of technology has raised concerns regarding cybersecurity for intelligent connected vehicles (ICVs). In-vehicle network in ICVs lacks robust protection mechanisms, making it vulnerable to cyber threats. In response, intrusion detection systems (IDSs) for ICVs have been developed to protect vehicles from malicious cyber attacks. However, current IDS methods solely rely on independent features, limiting their learning capabilities and increasing the number of false detections. Moreover, many IDSs require the knowledge of mapping between network messages and contents, which restricts their application. To address these limitations, we propose the Multi-order Feature Interaction-aware Intrusion Detection (MIFI) scheme for ICVs. Feature attention cross network is designed to address higher-order feature interactions, while factorization machine is used for second-order interactions. Then a discriminator is utilized to detect the attacks. MIFI expands the feature space through features interaction, thereby enhancing its ability to detect attacks. Moreover, it perceives the relationships of vehicle messages, facilitating intrusion detection without knowing the corresponding rules of vehicle messages. The performance of the proposed method is evaluated on two real-vehicle datasets, affirming its effectiveness and robustness. MIFI achieves an accuracy of over 99% in detecting different attacks. The proposed method can improve the accuracy of traditional IDS to a maximum of 99.99%, and increase the highest F1-score to 97.18%, demonstrating the model’s ability of achieving multi-order feature interactions. Ultimately, MIFI is suitable for intrusion detection in different types of ICV networks, significantly contributing to the cybersecurity of ICVs.

Deep Reinforcement Learning-based Mining Task Offloading Scheme for Intelligent Connected Vehicles in UAV-aided MEC

The convergence of unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) networks and blockchain transforms the existing mobile networking paradigm. However, in the temporary hotspot scenario for intelligent connected vehicles (ICVs) in UAV-aided MEC networks, deploying blockchain-based services and applications in vehicles is generally impossible due to its high computational resource and storage requirements. One possible solution is to offload part of all the computational tasks to MEC servers wherever possible. Unfortunately, due to the limited availability and high mobility of the vehicles, there is still lacking simple solutions that can support low-latency and higher reliability networking services for ICVs. In this article, we study the task offloading problem of minimizing the total system latency and the optimal task offloading scheme, subject to constraints on the hover position coordinates of the UAV, the fixed bonuses, flexible transaction fees, transaction rates, mining difficulty, costs and battery energy consumption of the UAV. The problem is confirmed to be a challenging linear integer planning problem, we formulate the problem as a constrained Markov decision process. Deep Reinforcement Learning (DRL) has excellently solved sequential decision-making problems in dynamic ICVs environment, therefore, we propose a novel distributed DRL-based P-D3QN approach by using Prioritized Experience Replay strategy and the dueling double deep Q-network (D3QN) algorithm to solve the optimal task offloading policy effectively. Finally, experiment results show that compared with the benchmark scheme, the P-D3QN algorithm can bring about 26.24% latency improvement and increase about 42.26% offloading utility.

VFL-Chain: Bulletproofing Federated Learning in the V2X environments

Federated Learning (FL) has gained significant traction as a promising approach to enable collaborative machine learning (ML) while safeguarding data privacy across diverse applications, with the Vehicle-to-Everything (V2X) environment being a notable use case. However, conventional FL systems remain susceptible to model poisoning attacks, wherein malicious participants can introduce inaccurate or deliberately misleading local model updates to the central aggregator. In the context of V2X, such attacks could spawn catastrophic outcomes and jeopardize safety-critical applications. To tackle this pressing concern, we introduce a privacy-preserving and verifiable FL framework, dubbed VFL-Chain, designed to facilitate secure and efficient collaboration among intelligent connected vehicles (ICVs). VFL-Chain aspires to deliver a privacy-centric, verifiable FL experience complemented by a robust incentive mechanism tailored for ICVs. Our proposed solution leverages Bulletproofs to enable efficient verification of model update integrity, which are assimilated within smart contracts on an underlying permissioned blockchain. Furthermore, VFL-Chain presents a fair incentive mechanism that rewards honest participation, fostering a resilient and efficient FL implementation. We conduct an exhaustive security analysis and performance assessment of our proposed system, which underscores its efficacy in countering data poisoning attacks and augmenting the accuracy of FL.

Cost Analysis of Vehicle-Road Cooperative Intelligence Solutions for High-Level Autonomous Driving: A Beijing Case Study

The development of the vehicle-road cooperative intelligence can effectively resolve the current technical impediment and cost quandary associated with high-level autonomous driving. Nevertheless, the intelligent infrastructure entails initial deployment costs and ongoing energy consumption and maintenance costs, necessitating a comprehensive and quantitative analysis of the costs of intelligent infrastructure and the corresponding changes in comprehensive costs. The cost evaluation model for the cooperative intelligent system is designed in this paper, considering the corresponding intelligent infrastructure layout scheme for different road types within the technical framework. The intelligent configuration and corresponding cost transfer from roadside to vehicle side under the synergy effect is also analyzed. Using Beijing as a case study, the results indicate that the deployment of intelligent infrastructure will effectively reduce acquisition and usage costs of high-level intelligent vehicles and achieve a greater “reuse” effect by serving more intelligent connected vehicles (ICVs). Compared to the vehicle intelligence, collaborative intelligence will reduce cumulative total costs by more than ¥200 billion from 2023 to 2050, even with the inclusion of intelligent infrastructure’s costs.

Perception Methods for Adverse Weather Based on Vehicle Infrastructure Cooperation System: A Review.

Environment perception plays a crucial role in autonomous driving technology. However, various factors such as adverse weather conditions and limitations in sensing equipment contribute to low perception accuracy and a restricted field of view. As a result, intelligent connected vehicles (ICVs) are currently only capable of achieving autonomous driving in specific scenarios. This paper conducts an analysis of the current studies on image or point cloud processing and cooperative perception, and summarizes three key aspects: data pre-processing methods, multi-sensor data fusion methods, and vehicle-infrastructure cooperative perception methods. Data pre-processing methods summarize the processing of point cloud data and image data in snow, rain and fog. Multi-sensor data fusion methods analyze the studies on image fusion, point cloud fusion and image-point cloud fusion. Because communication channel resources are limited, the vehicle-infrastructure cooperative perception methods discuss the fusion and sharing strategies for cooperative perception information to expand the range of perception for ICVs and achieve an optimal distribution of perception information. Finally, according to the analysis of the existing studies, the paper proposes future research directions for cooperative perception in adverse weather conditions.

Modeling and analysis of mixed traffic flow capacity and stability considering human-driven vehicle drivers' trust attitude towards intelligent connected vehicles

During the proliferation process of Intelligent Connected Vehicles (ICVs), mixed traffic consisting of Human-Driven Vehicles (HDVs) and ICVs is inevitable. Notably, varying trust attitudes of HDV drivers towards ICVs lead to diverse driving behaviors when they interact with ICVs. The impact of these differential driving behaviors on the operational characteristics of the mixed traffic flow is currently unclear. This study focuses on examining the capacity and stability of ICV mixed traffic flow in a single-lane, no-overtaking scenario. We investigate the car-following behaviors of HDV drivers with different trust attitudes towards ICVs. From this, we discern the expected distribution probabilities of vehicles exhibiting varying behaviors in the mixed flow. Utilizing these findings, a theoretical model has been developed to analyze the fundamental diagram and stability of mixed traffic flow, considering the trust attitudes of HDV drivers toward ICVs. Through numerical analysis and simulation experiments, the impact of HDV drivers' trust attitudes in ICVs on the capacity and stability of mixed traffic flow was examined. The results show that ICV integration can bolster traffic flow capacity. Further, heightened trust attitude in ICVs among HDV drivers magnifies the positive effect of ICVs on traffic operational efficiency. However, if the ICV penetration rate remains below 70% and the trust level of HDV drivers towards ICVs does not reach a critical threshold, the integration of ICVs could potentially reduce traffic stability. Conversely, once the ICV penetration rate surpasses 70%, a simultaneous increase in ICV penetration rate and the trusting attitude of HDV drivers towards ICVs significantly improves the stability of mixed traffic flow.

Fine-Grained Radio Frequency Fingerprint Recognition Network Based on Attention Mechanism.

With the rapid development of the internet of things (IoT), hundreds of millions of IoT devices, such as smart home appliances, intelligent-connected vehicles, and wearable devices, have been connected to the network. The open nature of IoT makes it vulnerable to cybersecurity threats. Traditional cryptography-based encryption methods are not suitable for IoT due to their complexity and high communication overhead requirements. By contrast, RF-fingerprint-based recognition is promising because it is rooted in the inherent non-reproducible hardware defects of the transmitter. However, it still faces the challenges of low inter-class variation and large intra-class variation among RF fingerprints. Inspired by fine-grained recognition in computer vision, we propose a fine-grained RF fingerprint recognition network (FGRFNet) in this article. The network consists of a top-down feature pathway hierarchy to generate pyramidal features, attention modules to locate discriminative regions, and a fusion module to adaptively integrate features from different scales. Experiments demonstrate that the proposed FGRFNet achieves recognition accuracies of 89.8% on 100 ADS-B devices, 99.5% on 54 Zigbee devices, and 83.0% on 25 LoRa devices.

ICVTest: A Practical Black-Box Penetration Testing Framework for Evaluating Cybersecurity of Intelligent Connected Vehicles

Intelligent connected vehicles (ICVs) are equipped with extensive electronic control units which offer convenience but also pose significant cybersecurity risks. Penetration testing, recommended in ISO/SAE 21434 “Road vehicles—Cybersecurity engineering”, is an effective approach to identify cybersecurity vulnerabilities in ICVs. However, there is limited research on vehicle penetration testing from a black-box perspective due to the complex architecture of ICVs. Additionally, no penetration testing framework has been proposed to guide security testers on conducting penetration testing for the whole vehicle. The lack of framework guidance results in the inexperienced security testers being uncertain about the processes to follow for conducting penetration testing. Moreover, the inexperienced security testers are unsure about which tests to perform in order to systematically evaluate the vehicle’s cybersecurity. To enhance the penetration testing efficiency of ICVs, this paper presents a black-box penetration testing framework, ICVTest. ICVTest proposes a standardized penetration testing process to facilitate step-by-step completion of the penetration testing, thereby addressing the issue of inexperienced testers lacking guidance on how to initiate work when confronted with ICV. Also, ICVTest includes 10 sets of test cases covering hardware and software security tests. Testers can select appropriate test cases based on the specific cybersecurity threats faced by the target object, thereby reducing the complexity of penetration testing tasks. Furthermore, we have developed a vehicle cybersecurity testing platform for ICVTest that seamlessly integrates various testing tools. The platform enables even novice testers to conduct vehicle black-box penetration testing in accordance with the given guidance which addresses the current industry’s challenge of an overwhelming number of testing tasks coupled with a shortage of skilled professionals. For the first time, we propose a comprehensive black-box penetration testing framework and implement the framework in the form of a cybersecurity testing platform. We apply ICVTest to evaluate an electric vehicle manufactured in 2021 for assessing the framework’s availability. With the aid of ICVTest, even testers with limited experience in automotive penetration can effectively evaluate the security risks of ICVs. In our experiments, numerous cybersecurity vulnerabilities were identified involving in-vehicle sensors, remote vehicle control systems, and in-vehicle controller area network (CAN) bus.

Data-Mechanism Adaptive Switched Predictive Control for Heterogeneous Platoons With Wireless Communication Interruption

Benefiting from the advancement of intelligent transportation systems (ITSs), intelligent connected vehicles (ICVs) are ushering in a once-in-a-generation development opportunity. Considering the widespread presence of heterogeneous vehicles with disturbances and uncertain dynamics in actual platoon scenarios as well as the multimodel switching produced by unavoidable interruptions in the communication process, this paper proposes a data–mechanism adaptive switched predictive (DASP) control strategy. The characteristics of the mechanism model are mapped based on state data to more accurately describe the system’s dynamic characteristics and improve the interpretability of variables. The introduction of Givens rotations and switching criteria enables online adaptive switching of the controller. A robustness analysis of heterogeneous platoon switching control under bounded disturbance is presented, and sufficient conditions for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal{L}_{2}$</tex-math> </inline-formula> string stability are provided. Finally, CarSim simulations and real-time bench experiments are reported to demonstrate the effectiveness of the DASP algorithm for heterogeneous multivehicle regulation with communication interruptions.

Knowledge Mapping with CiteSpace, VOSviewer, and SciMAT on Intelligent Connected Vehicles: Road Safety Issue

The rapid development of the Intelligent connected vehicle (ICV) industry has stimulated technological innovation in energy and communication while also highlighting the need for effective policies and road safety measures. Understanding and addressing road safety issues in the context of ICVs can contribute to ICV development and safe driving. This paper employs a knowledge mapping approach to scientifically and intuitively demonstrate research on the road safety issues of ICV over the last decade. By utilizing bibliometric tools such as CiteSpace, VOSviewer, and SciMAT, a total of 3661 original articles from the Web of Science are examined to explore three aspects. Firstly, the study investigates the collaborative relationships among authors and institutions within the industry. Secondly, it summarizes major research topics by analyzing and clustering keywords. Lastly, the paper identifies research hotspots and predicts future research directions. The findings reveal a dynamic field characterized by close collaboration among diverse institutions, with China and the United States emerging as the most active countries and mathematics and computer science journals becoming mainstream. According to three bibliometric tools, the research topics primarily revolve around three areas: Vehicular ad hoc Networks (VANET), intelligent transportation systems (ITS), and network security. Machine learning and V2X communication are predicted to be essential research topics in the next stage. Research on traffic accidents still has potential as the number of ICVs increases.

Characteristic Analysis and Decision Model of Lane-Changing Game for Intelligent Connected Vehicles

To study the lane-change interaction characteristics of intelligent connected vehicles (ICVs) and reduce the risk of vehicle lane-changing decisions, a decision model based on the lane-changing game characteristics of the ICV is proposed in this paper. In the modeling process, the characteristics of vehicle lane-changing interaction behavior are analyzed based on evolutionary game theory and the vehicle game lane-changing payoff functions are quantified. The stability of the game equilibrium points is analyzed by using a dynamic evolution equation, and sensitivity analysis of the main factors affecting vehicle lane changes and the time to the collision of vehicles is conducted. The SUMO software is used to simulate and verify the vehicle game decision model, and the results show that the game decision system converges to different optimal strategy combinations under different traffic conditions, and this model can effectively reduce the decision-making conflict and the collision risk of vehicles.

Intrusion Detection in Intelligent Connected Vehicles Based on Weighted Self-Information

The Internet of Vehicles (IoV) empowers intelligent and tailored services for intelligent connected vehicles (ICVs). However, with the increasing number of onboard external communication interfaces, ICVs face the challenges of malicious network intrusions. The closure of traditional vehicles had led to in-vehicle communication protocols, including the most commonly applied controller area network (CAN), and a lack of security and privacy protection mechanisms. Therefore, to protect the connected vehicles and IoV systems from being attacked, an intrusion-detection method is proposed based on the features extracted from the arbitration identifier (ID) field of CAN messages. Specifically, a sliding window is used to continuously extract a frame of streaming CAN messages first. Afterward, the weighted self-information of the CAN message ID is defined, and both the weighted self-information and the normalized value of an ID are extracted as features. Based on the extracted features, a lightweight one-class classifier, the local outlier factor (LOF), is used to identify the outliers and detect malicious network intrusion attacks. Simulation experiments were conducted based on the public CAN dataset provided by the HCR Lab. The proposed method, using four different one-class classifiers, was analyzed, and it is also benchmarked with three information entropy-based intrusion-detection methods in the literature. The experimental results indicate that, compared to the benchmarks, the proposed method dramatically improves the detection accuracy for injection attacks, namely denial-of-service (DoS) and spoofing, especially when the number of injected messages is low.