Search Results (145)

To address the stringent demands of intelligent connected vehicles for high bandwidth, low latency, and highly reliable communication, this paper systematically summarizes the semantic communication technology of the Internet of Vehicles (IoV) based on information “meaning” transmission, covering basic theory, key technologies, application practice and challenge and trends. First, the paper expounds the knowledge driven and task oriented paradigm characteristics of semantic communication and its efficiency advantages in the IoV. Second, in terms of key technologies, semantic extraction achieves efficient feature compression through multimodal fusion and Generative Artificial Intelligence (GAI); semantic coding employs hierarchical codebooks and adaptive strategies to optimize transmission efficiency; semantic transmission leverages deep reinforcement learning for the joint scheduling of resources such as spectrum and power; and semantic decoding utilizes reconstruction networks and GAI to enhance resilience against impairments. Application practices demonstrate that semantic communication can significantly compress image data transmission volume for autonomous driving collaborative perception while maintaining high-fidelity reconstruction under adverse channel conditions. It significantly reduces the communication load and improves the system utility in vehicle-to-infrastructure coordination and in-vehicle service. Despite facing technical challenges such as semantic consistency, dynamic adaptability, and security trustworthiness, future semantic communication will evolve towards deep integration with distributed collaborative knowledge networks, lightweight real-time decision-making agents, and integrated “communication, sensing, and computing” architectures, positioning itself as a key enabling technology for empowering Sixth Generation mobile communication (6G) of intelligent vehicular networks. Full article

With the fast-paced advancement of intelligent connected vehicles (ICVs), In-Vehicle Ethernet has gradually become the core of in-vehicle network communication systems, thanks to its superior high-bandwidth data transmission performance. However, the open nature of In-Vehicle Ethernet and the intricacy of its communication protocols have brought significant challenges to the practice of network intrusion detection for this technology. To tackle the problem of network intrusion detection in In-Vehicle Ethernet, this study takes into account the data characteristics of the AVTP protocol as well as common network attack approaches. We put forward a novel intrusion detection method based on a feature mask algorithm, which is designed to enhance the overall security level of In-Vehicle Ethernet. Experimental results show that the proposed algorithm can detect 99.5% of abnormal data in In-Vehicle Ethernet. Compared with traditional anomaly detection algorithms such as the Bayesian algorithm and decision tree method, the proposed method achieves detection rate increases of 12.4% and 7.8% respectively. Compared with the state-of-the-art CNN and XGBoost algorithms, the proposed method yields a relatively modest improvement in detection rate, while reducing inference latency by 81.6% and 21.8% respectively. These findings effectively boost the network security performance of In-Vehicle Ethernet and provide a reliable foundation for safeguarding the network security of intelligent connected vehicles. Full article

Transitions of control between automated driving systems and human drivers remain safety-relevant and cognitively demanding moments in human–automation interaction. Recent studies show that transition performance depends not only on takeover timing or response speed but also on traffic complexity, driver readiness, automation limitations, trust calibration, and situational-awareness recovery. As in-vehicle interaction evolves toward conversational and agentic AI assistance, takeover support also becomes a problem of governing how natural-language AI systems communicate with the driver under uncertainty. This paper proposes a digital-twin-mediated framework for human-aware takeover support in automated driving. In this framework, the companion AI is treated as an assumed LLM-based in-vehicle conversational or agentic assistant used as an advisory interaction component. The contribution is defined at the architectural level: human, vehicle, and context/road digital twins provide structured semantic state abstractions through a semantic state interface exposing confidence, freshness, provenance, and consistency metadata, while a trustworthy companion AI (TCAI) layer grounds, constrains, validates, and governs companion AI output proposals before HMI delivery. Building on the research on driver-state monitoring, adaptive HMI, trust calibration, explainability, conversational assistance, and human assistance systems (HASs), the framework coordinates advisory interaction across vigilance support, contextual explanation, trust-calibrating communication, and directive handover guidance. The TCAI layer combines bounded reasoning, human-factor-derived guardrails, state-consistency management, dynamic explanation-depth control, trust-dynamics modeling, graded watchdog veto handling, mandatory access-control assumptions, and deterministic fallback. Safety-critical vehicle-control and minimum risk condition (MRC) functions remain assigned to the deterministic vehicle-control stack, while the authorized output path of the TCAI layer is validated HMI delivery. The paper concludes with a validation agenda and technical roadmap covering planned transitions, urgent handovers, degraded or adversarial conditions, temporal fusion of driver-state evidence, phase-sensitive HMI policies, trust-calibration trajectories, driver veto and partial-disabling mechanisms, and staged simulator-to-vehicle evaluation. Although motivated by SAE Level 3 automation, the framework may also inform fallback-related Level 4 scenarios in which human and automated agency must be managed under uncertainty. Full article

Time-Sensitive Networking (TSN) has emerged as a critical communication protocol for automotive Ethernet to support the high-bandwidth, real-time, and deterministic transmission requirements of next-generation in-vehicle networks. However, a clear and effective TSN mechanism combination tailored to the mixed and bursty traffic characteristics of automotive scenarios remains lacking. To address this issue, this paper proposes a combined TSN scheduling mechanism for automotive scenarios. The highest-priority traffic is scheduled by class-based Time-Aware Shaper (TAS), periodic bursty sensor traffic is shaped by Credit-Based Shaper (CBS), and medium-priority traffic adopts Multi-Cyclic Queueing and Forwarding (MCQF). Based on Compositional Performance Analysis (CPA), this paper derives the worst-case latency upper bound expressions for CQF streams and optimizes the schedulability analysis to reduce conservative errors. Simulation verifies that the theoretically calculated bounds cover the maximum simulation latency, and the optimized analysis reduces conservatism, with peak conservative error of 3.07% in the ring scenario and 10.59% in the automotive scenario. Compared with the strict priority and TAMCQF (a combination of TAS and Multi-CQF), the proposed mechanism combination suppresses the latency jitter of mixed traffic, mitigates long-duration blocking of medium-priority traffic caused by high-priority burst data, and provides reliable deterministic transmission guarantees for automotive in-vehicle networks. Full article

Controller Area Network (CAN) remains a key in-vehicle communication protocol for distributed automotive control systems, where predictable communication timing is essential for coordinated operation of electronic control units (ECUs). This paper presents a cross-validated framework for timing analysis of automotive CAN networks by combining Deterministic and Stochastic Petri net (DSPN) modeling with worst-case response-time (WCRT) analysis. A DSPN model is developed to represent CAN message generation, priority-based arbitration, bus access, and non-preemptive frame transmission. The model is implemented in TimeNet to evaluate bus utilization, queue occupancy, and access-delay behavior under representative automotive traffic. In parallel, analytical WCRT equations are used to derive conservative latency bounds for each message class. The proposed framework links stochastic performance observations from DSPN simulation with deterministic schedulability guarantees from WCRT analysis, enabling consistency checks between average-case and worst-case timing results. A case study based on a 500 kbit/s automotive CAN configuration with six priority classes is presented. The results show that the network operates at approximately 35.9% bus utilization and that all message classes satisfy their timing requirements with a substantial margin, with the maximum worst-case response time remaining below 2 ms. The study further discusses the modeling assumptions, abstraction limits, and sensitivity of timing behavior to frame length and traffic configuration. The proposed framework provides a practical methodology for timing-oriented design and early-stage validation of automotive CAN communication systems. Full article

This paper presents a Threat Analysis and Risk Assessment (TARA) of the takeover request (TOR) component in Advanced Driver Assistance Systems (ADAS) for SAE Level 2–3 automation. A TOR prompts the human driver to retake control when the system approaches its Operational Design Domain limits or when risk increases; late, false, or muted requests directly impact safety. The study models the TOR pipeline (perception, driver monitoring, decision logic, in-vehicle networks, and Human–Machine Interface) as assets and data flows, applies STRIDE-based threat identification using Microsoft Threat Modeling Tool and Ansys Medini Analyze, and rates risks under ISO/SAE 21434 with traceability to ISO 26262, ISO 21448, and UNECE R155/R157. The assessment produces 165 threat rows, with an initial risk distribution of 1 Critical, 113 High, 34 Medium, and 17 Low. Results show that tampering, denial of service, and spoofing dominate the TOR threat landscape, with the central processing unit, sensor-to-CPU links, and HMI channels as primary trust anchors. After applying mitigation measures including secure boot, message authentication, intrusion detection, redundancy checks, and encrypted communication, the residual post-mitigation security levels were reduced to 0 Critical, 0 High, 13 Medium, 101 Low, and 51 Negligible. Unlike other ADAS TARA studies, this TOR-focused analysis shows that cybersecurity risk is shaped by the interaction between cyber compromise, driver-readiness estimation, HMI delivery, fallback execution, and the limited handover time budget. The results support a defence-in-depth mitigation strategy for secure TOR operation in SAE Level 2–3 vehicles. Full article

Driven by the rapid advancement of intelligence and connectivity, traditional distributed and signal-oriented automotive architectures are gradually being replaced by centralized, service-oriented architectures. In response to this transition, In-Vehicle Networks (IVNs) are expected to deliver high bandwidth, hard real-time performance, high reliability, and service-oriented capabilities. Data Distribution Service (DDS) and Time-Sensitive Networking (TSN) provide key technical support from the perspectives of service orientation and quality of service, respectively. Consequently, the integration of DDS and TSN has become a focal point in the field of IVNs. However, existing DDS message scheduling mechanisms cannot eliminate publishing time jitter, which prevents effective integration with deterministic scheduling mechanisms at the TSN layer, particularly the Time-Aware Shaper (TAS). To enable deterministic DDS communication in the DDS over TSN Architecture (DoTA), a Time-Triggered (TT) communication strategy based on message preemption and guard band mechanisms is proposed. This strategy is integrated into the flow controller of the DDS middleware. By scheduling a timed-event table, the publishing time of Time-Sensitive (TS) DDS messages is precisely controlled to align with the TAS mechanism. In addition, a schedulability analysis method is proposed to estimate the Worst-Case End-to-end Delay (WCED) of TS messages in DoTA. Experimental results from a physical testbed demonstrate that the proposed TT strategy can constrain the publishing time deviation of TS messages within 3 μs. When the TT strategy is jointly deployed with the TAS mechanism, both the end-to-end delay and jitter satisfy the requirements of safety-critical in-vehicle applications. Furthermore, the maximum deviation between the experimental results and the WCED estimated from the schedulability analysis is 15.4%. This indicates that the proposed method can effectively validate the feasibility of network designs and provide sufficient safety margins. Full article

Much of the research and proposed industrial deployment of Remote Operations (ROs) in support of automated vehicles is founded on the optimistic premise that in-vehicle standby drivers and Safety Officers (SOs) can easily be replaced with ROs, with some commercial models proposing that a single RO supervise over 30 vehicles. However, emerging evidence suggests that the RO task is fundamentally different from the in-vehicle driving task. Furthermore, communications latency and reliability constraints, coupled with fragmented attention and altered task demands, introduce distinctive human factor challenges. These include degraded situational awareness, increased cognitive workload, and reduced capacity for timely intervention. The result is a widening gap between what is commercially desirable and what may be operationally appropriate. This paper argues that the central question for remote operation in support of automated vehicles is not one of technical feasibility but of human-centred appropriateness, and debates which RO roles should continue to be developed and which should be constrained or avoided. We present a synthesis of research on remote vehicle operations, identifying recurring human-factor limitations and mapping them to proposed remote tasks. The paper concludes with targeted recommendations for designers, operators, and regulators intended to question the scaling of teleoperation models and to reframe the debate from “Can we teleoperate?” to “Under what conditions should we?” Full article

Secure real-time communication is a critical requirement in modern electric vehicle (EV) networks. These networks transmit safety-critical control commands through vulnerable in-vehicle communication channels. This study proposes a novel three-dimensional symmetric chaotic system for high-security EV communication. The system exhibits extensive multistability and symmetric double-wing attractors. To enhance dynamical complexity, its parameters are optimized using chaotic-enhanced particle swarm optimization (C-PSO). The largest Lyapunov exponent is used as the optimization objective. A fixed-time nonlinear controller is designed for rapid drive–response synchronization. The settling-time bound is independent of the initial conditions. The proposed method is evaluated through realistic Controller Area Network (CAN) bus simulations. These simulations include 12-bit quantization and a 1 ms sampling period. The experimental results show synchronization within 0.057 s. The recovered signal achieves an MSE of $1.202\times {10}^{-4}$. The encrypted signal reaches a Shannon entropy of 7.9904. These results confirm accurate recovery, strong randomness, and improved resistance to cryptographic attacks. Full article

Connected and autonomous vehicles (CAVs) increasingly rely on vehicle-to-everything (V2X) communication and distributed sensing infrastructures to support cooperative driving and intelligent transportation services. While these capabilities improve traffic efficiency and safety, they also expand the attack surface of vehicular networks and expose in-vehicle communication systems such as the Controller Area Network (CAN) bus to a wide range of cyber threats. Machine learning-based anomaly detection has emerged as a promising approach for identifying malicious CAN traffic patterns; however, conventional centralized learning requires large-scale data aggregation from vehicles, which raises privacy and scalability concerns. Federated learning (FL) enables collaborative model training across distributed vehicles without requiring the exchange of raw in-vehicle data, making it attractive for privacy-preserving vehicular security applications. Nevertheless, FL systems remain vulnerable to adversarial participants that manipulate local training data or model updates to poison the global model during aggregation. In this work, we present a systematic robustness evaluation of federated anomaly detection in connected vehicular networks under adversarial conditions. The study compares six aggregation strategies, including Federated Averaging (FedAvg), coordinate-wise Median, Trimmed Mean, Krum, Multi-Krum, and Geometric Median (GeoMed), within a non-IID federated CAN bus anomaly detection setting. The evaluation covers label-flipping attacks, gradient-scaling attacks, and a feature-triggered backdoor attack. In addition, the analysis examines malicious client participation, attack-strength variation, learning-rate sensitivity, Trimmed Mean beta sensitivity, multi-seed reliability, and server-side aggregation time. The results show that FedAvg is vulnerable under strong adversarial manipulation, while Trimmed Mean is sensitive to the selected trimming fraction. Median and GeoMed provide strong robustness against gradient-scaling attacks, whereas Multi-Krum achieves the strongest resistance to label-flipping and backdoor attacks. These findings demonstrate that no single aggregation strategy is optimal across all threat models. Instead, robust aggregation for federated CAV anomaly detection should be selected according to the expected attack type, reliability requirement, and computational overhead. Full article

With the rapid development of automotive intelligent driving technologies, the demand for real-time performance and bandwidth in in-vehicle bus networks is increasing day by day. When contrasted with conventional in-vehicle bus protocols like LIN and CAN, FlexRay delivers superior performance in bandwidth capacity, communication latency and data transmission speed. Such prominent strengths establish it as a core technical solution for modern automotive network systems. Targeting the flexible bandwidth characteristics of FlexRay bus systems, this work develops a novel heterogeneous message scheduling algorithm (DHSA) tailored for the dynamic segment of FlexRay. The DHSA enables flexible timeslot and priority configuration for event-triggered and low-priority messages, thereby improving the overall scheduling efficiency of FlexRay bus communication. This work adopts the CANoe.FlexRay simulation tool to construct a dedicated experimental platform and perform comparative simulations for the proposed algorithm. The experimental results show that the bandwidth utilization of the heterogeneous scheduling algorithm proposed in this paper reaches 96.6%, an increase of 13.4% compared to the Earliest Deadline First (EDF) algorithm; meanwhile, the fastest response time of the proposed algorithm is reduced by 50% compared to the EDF algorithm. This study effectively reduces message transmission latency and enhances system real-time performance and determinism, thereby further improving the communication efficiency of the in-vehicle FlexRay bus network. Full article

A compact multiband shark-fin antenna is proposed for integrated vehicle-to-everything (V2X) platforms. The design incorporates five radiating elements within a compact $90\times 15\times 30{\mathrm{mm}}^3$ footprint, simultaneously supporting FM (88–108 MHz), TETRA (380–470 MHz), wideband cellular (0.68–6.05 GHz), and dual-band Wi-Fi services. Wideband cellular operation is realized using two mirrored planar inverted-F antennas (PIFAs), while a dual-band IFA provides Wi-Fi connectivity for in-vehicle and vehicle-to-infrastructure communications. The FM and TETRA elements employ compact meandered-line configurations to satisfy stringent rooftop space constraints. To improve multi-radio coexistence, the FM radiator is strategically placed between the two cellular elements, achieving inter-element isolation better than $-15$ dB across all operating bands. Experimental results demonstrate stable radiation performance, with realized gains ranging from 1.5 dBi to above 5 dBi and cross-polarization levels below $-13$ dB, in good agreement with simulations. With overall dimensions of $90\times 15\times 30{\mathrm{mm}}^3$, the proposed antenna is well suited for integrated V2X applications. Full article

The Controller Area Network (CAN) remains the backbone of in-vehicle communication, but its lack of built-in security exposes safety-critical systems to cyberattacks. This paper presents a real-time, reconfigurable, specification-driven intrusion detection system (IDS) implemented on a custom test bench that emulates an EV powertrain. The CAN traffic captured from the four-ECU setup formed the dataset used in this study. The IDS enforces a compact, reconfigurable ruleset covering timing bounds, jitter envelopes, identifier whitelists, frame format, data length code (DLC) compliance, bus-load thresholds, application-level CRC, and alive-counter verification. The IDS achieves detection times below 2 ms with false positive rates under 1% for injection, denial of service (DoS), and fuzzy attacks, even at CAN bus loads up to 70%, while microcontroller resource usage remains within the constraints of automotive-grade devices, supporting deployment in embedded environments. The main contributions of this study are as follows: (i) a validated and reproducible EV powertrain test bench with millisecond-level timing, (ii) a deployable and easily reconfigurable ruleset with deterministic runtime, and (iii) a latency-oriented evaluation framework that is portable across automotive microcontroller platforms. The EV powertrain dataset v1.0 was released in a public GitHub repository to facilitate reproducible research and enable future benchmarking studies. Full article

As a higher-layer protocol over a controller area network (CAN) or CAN with a flexible data-rate bus, Society of Automotive Engineers (SAE) J1939 has been widely adopted in commercial vehicles. Although it supports advanced diagnostics, complex data transmission, and network management in harsh environments, SAE J1939 lacks native authentication mechanisms. Consequently, in-vehicle communication remains vulnerable to replay, spoofing, and injection attacks. In practice, deploying a Security-designated Electronic Control Unit (SeCU) is often deemed necessary to provide robust authentication, as generating and distributing session keys is essential. However, this introduces a single point of failure and renders the SeCU a high-value target for attackers. To address these issues, we propose J1939-ADBE, an authentication and key-distribution scheme that operates without a centralized SeCU. The scheme is built on Authenticated Distributed Broadcast Encryption (ADBE), a tightly integrated construction that augments distributed broadcast encryption with publicly verifiable sender authentication in a shared bilinear setting. By leveraging ADBE, we eliminate the requirement for a SeCU while achieving the desired security goals. Using the Tamarin Prover, we formally verify in the Dolev–Yao model that J1939-ADBE satisfies injective agreement, session secrecy, known-key security, and forward secrecy. Furthermore, the broadcast nature of ADBE reduces the communication cost of key distribution from $\mathcal{O}\left(n\right)$ to $\mathcal{O}\left(\right|\mathcal{G}\left|\right)$, where n denotes the number of Electronic Control Units (ECUs) and $\left|\mathcal{G}\right|$ denotes the number of ECU logical groups. Experimental results show that our proposal is practical for authentication within SAE J1939 networks. Full article

Effective communication of traffic information is critical for freeway safety. Yet, Traditional road signs and variable message displays often fail to capture drivers’ attention, as evidenced by persistent speeding-related crashes on highways. Advances in connected-vehicle technology and head-up displays (HUDs) now enable real-time, personalized in-vehicle warnings, offering new opportunities to enhance speed compliance. At the same time, these systems introduce an important design challenge: the effectiveness of warnings may depend on how messages are framed (e.g., emphasizing benefits versus risks), particularly under time pressure and varying driver characteristics. Despite its theoretical importance, the impact of message framing in real-time, HUD-based speed warning contexts remains insufficiently understood. This study proposes and evaluates a Freeway-Centered Dynamic Speed Warning System (F-DSWS) that delivers real-time speed warnings via vehicle-to-infrastructure communication and HUD interfaces. Two message-framing strategies were examined: gain-framed messages emphasizing positive and socially relevant outcomes, and loss-framed messages highlighting safety risks. A driving simulator experiment was conducted with 39 licensed drivers aged 19–78 years across multiple freeway scenarios. Results indicate that HUD-based warnings significantly outperformed traditional roadside signs in reducing speeding, lane-deviation extremes, and harsh braking. Moreover, gain-framed messages consistently produced greater improvements in driving performance than loss-framed messages across all evaluated metrics. These findings suggest that the proposed F-DSWS provides measurable safety benefits and demonstrate that framing choice is a critical design factor in personalized in-vehicle freeway traffic messaging. These results offer evidence-based guidance on framing selection within HUD-based in-vehicle freeway warnings, and support future field deployment of the proposed F-DSWS to improve freeway safety. Full article