Metal/fibre-reinforced polymer (FRP) adhesive joints inherently exhibit multiple competing failure modes due to material heterogeneity and stiffness mismatch, including cohesive failure and interfacial debonding. However, the individual fracture properties associated with these failure modes remain insufficiently quantified. This study proposes a decoupling-coupling experimental framework for hybrid steel/orthogonal woven carbon fibre-reinforced polymer (OW-CFRP) adhesive joints. Fractographic observations identify three dominant failure modes: adhesive cohesive failure, steel-side interfacial failure, and OW-CFRP-side interfacial failure. Pre-cracks are introduced at prescribed locations to preferentially activate different failure modes, enabling the characterisation of their fracture strengths and fracture toughness values. Furthermore, a quantitative area-weighted coupling approach is proposed to assess the macroscopic fracture response based on the characterised fracture properties and experimentally observed failure area fractions. Experimental validation demonstrates good agreement between the proposed approach and the measured results, with deviations within 10%. The proposed framework provides a systematic experimental methodology for quantitatively analysing multi-failure mode coupling behaviour in heterogeneous adhesive joints.

The steer-by-wire (SBW) system is recognized as a crucial technology for facilitating autonomous driving. However, system nonlinearities, parametric uncertainties, and external disturbances present significant challenges to steering control. This article proposes a control strategy integrating the multitask physics-informed neural network (MTPINN) with model predictive control (MPC), using the rack position as the tracking target. First, the SBW model, including the motor, synchronous belt, ball screw, and rack, is established, with the rack force being treated as an extended state of the system to construct the extended disturbance observer (EDO). Then, by embedding both the extended disturbance observation equation and the prediction equation into the neural network, the MTPINN model that represents the transient behavior of the SBW system is constructed. This model is integrated with the MPC architecture to improve the performance of rack position tracking control. Finally, the bench test results indicate that the maximum observation error root-mean-squared error (RMSE) and the maximum tracking error RMSE are 0.348 kN and 0.784 mm, respectively, validating the effectiveness of the MTPINN-MPC steering control strategy.

In the field of automated driving, scenario-based safety testing is essential for ensuring vehicle safety and promoting technological development. However, traditional mileage-based real-world testing methods are limited by the massive testing requirements and extremely long cycles, hardly meeting efficient validation demands. To address this, this paper proposes a Hybrid Optimal Acceleration Evaluation (HOAE) model based on virtual testing. First, a hybrid segmentation model based on fitting error is constructed to accurately characterize the distribution features of naturalistic driving data, and an acceleration model is designed with the number of tests as the optimization objective to solve for the optimal number of segments. Second, the Importance Sampling (IS) method is introduced to increase the occurrence probability of risk scenarios, and the optimal IS function is solved based on the above acceleration model. Finally, a simulation test platform is established, and comprehensive comparative experiments are conducted under various acceleration evaluation methods, indicators, and parameter distributions. The results demonstrate that the proposed HOAE method offers better universality and higher testing efficiency, and it can increase the test mileage to 104 times of the Monte Carlo (MC) method, thereby advancing safety testing of automated vehicles toward greater efficiency and broader applicability.

Cloud-based vehicle control still faces challenges in maintaining trajectory tracking accuracy and driving stability when hybrid cyber attacks interact with extreme driving maneuvers. Most existing methods are developed for isolated attack scenarios or single control objectives, and thus are insufficient to handle the coupled effects of communication disruption, false data injection, and aggressive vehicle dynamics. To address this issue, this paper proposes a physics-guided reinforcement learning control framework for cloud-based intelligent connected vehicles under hybrid cyber attacks. Specifically, a Takagi-Sugeno (T-S) fuzzy vehicle dynamics model with hybrid cyber attacks is established together with an attack-aware event triggering mechanism (AETM), providing a control-oriented representation of attack-induced communication uncertainty. A multi-objective dynamic output-feedback controller is further developed to jointly enforce trajectory tracking performance, lateral stability and roll stability. Furthermore, fuzzy rule knowledge and expert control guidance are embedded into the reinforcement learning architecture to construct a physics-guided controller that improves exploration safety and policy learning under attack-disturbed conditions. A differential braking-based tire force allocation strategy is further designed to realize the desired yaw moment. The proposed method is validated through simulations and cloud-based hardware in the loop (C-HiL) experiments. The results show that, compared with the deep deterministic policy gradient (DDPG)-based controller and the fuzzy controller, the proposed twin delayed deep deterministic policy gradient (TD3)-based controller reduces the lateral displacement error by 21.2% and 58.7%, and the heading angle error by 34.5% and 61.7%, respectively. In addition, it more effectively suppresses attack-induced control oscillations.

Single-wheel brake failure in electromechanical brake (EMB) systems breaks the left-right symmetry of wheel forces and yaw moments, creating a critical conflict between emergency braking effectiveness and lateral stability. To address this symmetry-breaking condition, this paper proposes a bimodal, adaptive, coordinated fault-tolerant control strategy that integrates dynamic brake torque redistribution with active front steering (AFS). A novel dynamic interaction model linking deceleration demand with tire adhesion utilization enables real-time assessment and optimization of the balance between longitudinal braking performance and yaw stability. Braking forces are allocated based on adhesion utilization through a layered two-mode strategy-balanced distribution prioritizing lateral stability and compensatory distribution engaging the healthy front wheel when rear axle capacity is exceeded. An integral sliding-mode controller computes the additional yaw moment needed to suppress yaw-rate deviation, with rigorous Lyapunov stability analysis confirming closed-loop stability. AFS is triggered only when yaw-rate deviation exceeds 0.05 rad/s or adhesion utilization reaches 90%, incorporating hysteresis to ensure smooth transitions and minimize unnecessary steering intervention. Comprehensive co-simulations using Carsim and MATLAB/Simulink under diverse failure locations (left-front and right-rear wheels), road adhesion levels (mu = 0.85 and 0.5), and braking intensities (0.2 g-0.6 g) demonstrate that the proposed strategy reduces lateral displacement by up to 85.3% compared to full-time AFS control while maintaining over 99% deceleration satisfaction. The results establish an effective dual-objective fault-tolerant framework that enhances both robustness and functional safety of EMB systems under symmetry-breaking faults, offering a physically interpretable, computationally efficient solution well-suited for real-time automotive applications.

The present paper proposes an adaptive steering weight allocation strategy based on a non-cooperative Stackelberg game and Model Predictive Control (MPC) for dynamic steering authority allocation in human–machine shared control of intelligent vehicles. First, the human–machine steering interaction is modelled as a Stackelberg game, and the steering control problem is formulated as an MPC optimization problem. The optimal control sequences of the driver and the Advanced Driver Assistance System (ADAS) under game equilibrium are then derived through backward induction. Subsequently, driver behaviour is classified as aggressive, moderate, or conservative according to lateral preview error and lateral acceleration, and the driver state is quantified using parametric indicators. Furthermore, by integrating potential field-based driving risk assessment with human–machine conflict intensity, a fuzzy logic-based dynamic weight adjustment mechanism is constructed. Simulation results show that when the steering intentions of the driver and the ADAS are highly consistent, the proposed strategy can effectively reduce driver workload and improve driving safety. In high-risk driving situations, the strategy automatically transfers more steering authority to the ADAS to enhance safety, whereas under low-risk conditions with strong human–machine steering conflict, greater driver authority is preserved to ensure that the vehicle follows the intended path. Hardware-in-the-loop experiments in lane-changing assistance scenarios further verify the effectiveness of the proposed strategy under different driving styles. Quantitative results show that, compared with manual driving, the proposed strategy reduces the maximum lateral overshoot by 98.75%, 85.54%, and 98.58% for aggressive, moderate, and conservative drivers, respectively. In addition, the peak yaw rate and driver control effort are significantly reduced, indicating smoother vehicle dynamic response and lower steering workload. These results demonstrate that the proposed strategy can effectively improve lane-change stability, reduce driver burden, and maintain safe and coordinated human–machine shared control.

Cloud-based intelligent connected vehicles (CICVs) are vulnerable to denial of service (DoS) and false data injection (FDI) attacks through wireless control channels, which can significantly degrade trajectory tracking performance and compromise driving safety. To improve control resilience and safety under hybrid cyber attacks, this paper proposes a safety-guided reinforcement learning control framework for CICVs. A unified discrete-time hybrid attack model is established to characterize the temporal, frequency, and amplitude constraints of hybrid cyber attacks, and reachable and controllable set analyses are performed to quantify the resulting degradation of system stability and safety margins. On this basis, a robust control Lyapunov function and control barrier function (CLBF)-based safety controller is developed by explicitly incorporating attack bounds into tightened stability and safety constraints. Furthermore, the tightened CLBF mechanism is embedded into the reinforcement learning framework through expert-guided policy learning, safety-aware reward shaping, and online action projection, thereby combining model-based safety assurance with data-driven performance optimization. Finally, simulations and cloud-based hardware-in-the-loop (C-HiL) experiments are conducted to validate the proposed method. The results show that, compared with the CLBF-based controller, the proposed method reduces the average lateral displacement error and average heading angle error by 87.42% and 94.57%, respectively, while achieving smoother control behavior and a larger safe controllable region under hybrid cyber attacks.

The safety of Autonomous Vehicles (AVs) is crucial to the development of the autonomous driving field. The accelerated evaluation methods based on scenario simulation have become a hot research direction due to low test cost and high test efficiency. Among them, the Importance Sampling (IS) method has attracted much attention. However, the IS method based on variable independence is limited in its application actual scenarios. To address this issue, this paper proposes an accelerated evaluation method compatible with both independent and dependent variables. For independent variables, a non-parametric Kernel Density Estimation (KDE) method is employed for distribution fitting, combined with IS and Bayesian Optimization (BO) to present a non-parametric sampling strategy. For dependent variables, by utilizing the Copula model and Maximum Likelihood Estimation (MLE) to capture the dependencies between variables, the joint distribution of dependent variables can be obtained, facilitating accelerated evaluation in conjunction with IS and BO. Furthermore, this paper selects the relative half-width as the convergence indicator and sets a reasonable threshold according to the probability distribution of variables. Through simulation testing in cut-in scenarios, this accelerated evaluation method not only effectively accommodates both independent and dependent variables but also achieves an increase in testing efficiency of over 200 times compared to Monte Carlo methods. Meanwhile, through the analysis of different variable combinations, it is found that this method can select the variable combination with the highest test efficiency according to the importance distribution of each variable combination, providing new ideas and technical support for the theory of accelerated evaluation.

Autonomous driving in unstructured environments faces challenges such as missing road boundaries, terrain variations, random obstacle distributions, and complex vehicle–terrain interactions, making it difficult to achieve safe navigation by relying on lane-level priors from structured roads. To address the problems of the relative separation between traversability analysis and trajectory planning, the ineffective propagation of perception uncertainty, and the insufficient scene adaptability of coupling mechanisms, this paper takes traversability as the main thread and systematically reviews the research progress of perception–planning coupling mechanisms in unstructured environments. First, traversability analysis methods based on geometric terrain, semantic understanding, and physical dynamics are reviewed, and the representation and propagation mechanisms of uncertainty in the perception–planning chain are analyzed. Second, the role of traversability information in global path search, local trajectory optimization, and data-driven planning is discussed, and the applicable boundaries of different coupling architectures are summarized from the perspectives of representation level and system organization form. Finally, datasets, simulation platforms, and evaluation metric systems are summarized, and a risk-state-oriented adaptive perception–planning coupling framework is proposed to dynamically adjust coupling strength based on risk-state information, thereby improving the safety, interpretability, and environmental adaptability of autonomous driving in unstructured environments.

Slope driving of autonomous vehicles faces significant challenges in simultaneously achieving accurate path-tracking and maintaining lateral stability. To overcome these limitations, a cooperative control framework is presented that integrates a computationally efficient region of attraction (RoA) estimation with an adaptive output feedback distributed model predictive control (DMPC) strategy. The vehicle stability RoA is obtained from the sum-of-squares programming (SoSP) method. To reduce fitting complexity while maintaining accuracy, the polynomial parameters of the RoA are simplified. A stability coefficient is then derived from a radial basis function neural network (RBFNN) trained with simulation data to represent the real-time stability state of the vehicle. Based on the stability coefficient, the DMPC strategy is developed to coordinate the control of active front steering, active rear steering, and direct yaw moment. The weight of direct yaw moment is adjusted online and further refined through an adaptive tuning mechanism, which overcomes the convergence limitations of integral-based MPC by dynamically updating the stability coefficient according to output variation. Simulation experiments conducted in uphill and downhill cornering scenarios indicate that the proposed method significantly improves path-tracking accuracy and lateral stability, and effectively mitigates coordination conflicts among the chassis subsystems. Specifically, compared with other given baseline strategies, the lateral displacement errors are reduced by 39.69

This study addresses the state estimation problem for the preceding vehicle in vehicle-to-vehicle (V2V) cooperative perception, with the objective of balancing estimation accuracy against communication load under constraints of limited bandwidth and non-Gaussian noise interference. To this end, an extended Kalman filtering algorithm is proposed that integrates an event-triggered (ET) mechanism with the maximum correntropy criterion (MCC). The event-triggered mechanism adaptively regulates communication frequency, significantly reducing network load while preserving estimation performance. Meanwhile, the maximum correntropy criterion enhances the filter's robustness against non-Gaussian noise, enabling high-precision state estimation under limited communication resources. Simulation results under double lane-change and continuous sinusoidal steering scenarios show that, compared to conventional extended Kalman filtering and existing event-triggered robust filtering methods, the proposed algorithm achieves better estimation accuracy and stability in non-Gaussian noise environments. Moreover, it reduces the average communication load by approximately 68% without compromising estimation performance. This work provides an effective solution for state estimation in V2V cooperative perception that combines high robustness with high communication efficiency.

Sodium-ion batteries (SIBs), owing to their abundant resources, low cost, and superior low-temperature performance, show great potential for applications in energy storage systems and electric vehicles. Accurate state of charge (SOC) estimation, a critical function of battery management systems (BMS), is essential for ensuring operational safety and optimizing efficiency. However, existing SOC estimation methods for SIBs often face challenges including limited accuracy, poor wide-temperature adaptability, high computational demands, or insufficient mechanistic clarity. To overcome these issues, this study proposes a hybrid SOC estimation method integrating an Improved Gas-Liquid Dynamic (IGLD) model with the Cubature Kalman Filter (CKF). The IGLD model refines the original framework by introducing a temperature-dependent correction mechanism that dynamically maps battery capacity and internal resistance to temperature variations, thereby enhancing characterization across wide temperature ranges. Comprehensive validation was conducted under multiple temperatures (-20 degrees C to 45 degrees C) and dynamic driving cycles (DST, FUDS, UDDS, CLTC). Results indicate that the IGLD model reduces root mean square error (RMSE) and mean absolute error (MAE) to within 1.8 %, outperforming the original model. Further integration with CKF improves robustness: the IGLD-CKF method achieves RMSE and MAE below 1 %, converges within 35 iterations even with 100 % initial SOC error, and maintains errors under 1 % under strong noise interference (+10 mV voltage, +100 mA current). These results confirm the method's high accuracy, strong robustness, and excellent temperature adaptability, offering a reliable technical solution for the safe and efficient deployment of SIBs in real-world dynamic scenarios.

Distributed-drive all-wheel steering (AWS) six-axle vehicles possess distinct advantages in power performance, maneuverability, and environmental adaptability. However, when navigating tight curves under sudden low-friction road conditions, their inherent long wheelbase and strong inter-axle coupling typically lead to compromised spatial maneuverability, trajectory decoupling between the vehicle nose and tail, and lateral dynamic instability. To resolve these critical issues, this paper proposes a geometry–dynamics coupled lateral control scheme with adaptive speed planning for six-axle vehicles under confined spatial and low-friction conditions by seamlessly fusing a dual-point preview mechanism with multi-mode steering mappings. First, a three-degree-of-freedom nonlinear vehicle dynamic model incorporating longitudinal, lateral, and yaw motions is constructed, alongside the formulation of extended Ackermann kinematic steering manifolds for three distinct modes: rear-axle steering, center steering, and crab steering. To rectify the kinematic under-constrained deficiency inherent in conventional single-point preview path-tracking architectures, a joint front-and-rear dual-point preview constraint mechanism is established. This framework permits the quantitative derivation of a spatial geometric reconstruction method for the instantaneous center of rotation (ICR), which algebraically maps the ideal ICR trajectory requirements onto the physical constraints of the selected steering modes. Consequently, complete geometric constraints on both the front and rear trajectories are achieved, enabling active compression of the vehicle’s turning radius. Furthermore, to handle sudden low-friction disturbances, road adhesion limits and vehicle lateral stability boundaries are explicitly incorporated to design a multi-scale adaptive preview distance dynamic scaling mechanism driven by dynamic safety margin corrections. By adaptively scaling the spatial constraint at the geometric layer, this mechanism proactively mitigates nonlinear tire sideslip force saturation via feedforward action, thereby preventing tracking divergence and catastrophic sideslip instability under physical adhesion limits. Co-simulations based on the high-fidelity TruckSim-Simulink platform demonstrate that, in standard curves, the proposed dual-point preview manifold fusion strategy reduces the minimum turning radius by 9.6–10.1% and shortens the cornering transit time by 7.5% compared with the traditional single-point preview mechanism. By actively constraining the front and rear trajectories, the trajectory decoupling between the vehicle nose and tail is effectively resolved. Under narrow-lane scenarios, the maximum lateral error is restricted within 0.78 m, representing a 37.6% reduction relative to the single-point preview, while the maximum steering angle of the front axle is compressed by approximately 18%, thereby significantly improving spatial passability and preventing intermediate body interference. Most notably, under low-friction surface disturbances, the dynamic-margin-corrected adaptive preview adjustment mechanism exhibits remarkable robustness, constraining the maximum lateral tracking error to within 0.68 m. The proposed geometry–dynamics coupled lateral control strategy successfully elevates the tight-curve maneuverability of heavy transport vehicles while concurrently reinforcing their lateral dynamic stability under limit combined spatial and adhesion constraints.

With the rapid development of electronic control and intelligent driving, vehicle chassis systems are shifting from mechanical structures to X-by-wire architectures. X-by-wire chassis reduce vehicle mass through lightweight design and offer excellent control precision, fast dynamic response, and strong scalability, which are crucial for intelligent connected vehicles. This review systematically summarizes the research progress of key technologies in X-by-wire chassis, with a focus on the control strategies of three core systems: steer-by-wire, brake-by-wire, and suspension-by-wire, as well as cutting-edge achievements in integrated chassis control. At the subsystem level, issues such as high-precision tracking control, robust fault-tolerant control and its deep integration with autonomous driving are discussed in depth. At the vehicle level, multidimensional cooperative control methods aimed at enhancing vehicle stability are thoroughly elaborated, encompassing lateral-longitudinal, lateral-vertical, longitudinal-vertical, and lateral-longitudinal-vertical cooperative control strategies. Finally, future challenges and development trends of X-by-wire chassis are outlined to provide guidance for further research in related fields.

The development of intelligent connected vehicles and cloud-based control technologies offers great potential for high-level autonomous driving. In practical applications, the vehicle to infrastructure (V2I) channel between the vehicle and roadside edge cloud suffers from channel fading and quantization errors, which substantially affect lateral control performance. To address this problem, this paper develops a control framework that explicitly models and compensates for V2I uncertainty. Firstly, a V2I channel simulation model is constructed to analyse the quantization error and obtain the quantitative error ratio variance (QERV). Meanwhile, the V2I channel parameters of vehicles in multi-scene and multi-condition operation are collected to train a channel fading error ratio variance (CFERV) prediction model based on bidirectional long short term memory (Bi-LSTM) network. Secondly, a control-oriented V2I channel uncertainty model is developed to capture channel fading and quantisation effects, based on which a cloud-based intelligent connected vehicle (CICV) lateral control model with channel uncertainty is established. Then, the CICV lateral model is reformulated as a linear stochastic system, upon which a mixed H2/H∞ controller with integrated probabilistic safety constraints is synthesised to ensure multi-objective performance and lateral safety. Finally, simulation and semi-physical in the loop experiments are performed under typical conditions, and the results show that the proposed control strategy effectively improves the lateral control accuracy of the vehicle in cloud control scenarios and ensures the ride comfort of the vehicle.

Regenerative braking plays an important role in improving the driving range of electric vehicles. To achieve accurate and efficient braking deceleration control, this research focuses on the energy recovery process with ultracapacitors (UCs). According to statistical analysis results of characteristics for typical operation, a multi-step series hybrid energy storage system(M-SHESS) is constructed to realize energy recovery with grading in the braking process. To explore accurate measurement of braking deceleration without the vehicle speed sensor in the braking force control, the relationship between energy recovered by UC modules and the braking deceleration during the braking energy recovery is analyzed, and a deceleration control method with energy constraint for braking force is proposed. Results show M-SHESS can be applicable for energy recovery processes on different operations, and can effectively improve the efficiency of energy recovery. Moreover, the deceleration control method based on energy constraint can meet the vehicle braking requirement. Research for M-SHESS provides new ideas and approaches for the development of electric vehicles and fuel cell vehicles.

The rapid expansion of Autonomous vehicles (AVs) into urban environments, particularly at roundabout scenarios lacking complete and smooth reference paths, necessitates advanced path generation strategies. Piecewise curve interpolation methods have been utilised for path planning in complex scenarios, but challenges have been faced in ensuring the smoothness at the segmented path connection points and the overall path's fairness. To address these obstacles, this paper introduces a new approach using Non-Uniform Rational B-Splines (NURBS) curves to generate piecewise reference paths at roundabouts. It involves fitting reference paths with known lane centre discrete points and interpolating unknown reference paths based on high-order continuity with connection points of fitted paths. The study employs the Non-dominated Sorting Genetic Algorithm II (NSGA II) for multi-objective optimisation, enhancing path smoothness and fairness. The manuscript validates this method using CitySim data sets, focussing on standard as well as non-standard roundabout scenarios. The results indicate that while polynomial, B & eacute;zier and NURBS curves can all generate smooth reference paths, NURBS outperforms the others in fitting and building fair paths. The standard deviation of the curvature change rate of NURBS is reduced by 10.40% compared to B & eacute;zier curves and 8.36% compared to polynomial curves, demonstrating a more uniform curvature transition. This research provides a comprehensive and innovative approach for AVs to path planning, smoothing, and fairing, and overcoming the complex geometrical challenges at roundabouts.

To address the challenges of low computational efficiency, poor trajectory smoothness, and delayed adjustments in intelligent vehicle obstacle avoidance, an improved trajectory planning algorithm is proposed which integrates iterative optimization of path and speed. In addition, an adaptive robust model predictive control (RMPC) controller accounting for the lateral and longitudinal coupling dynamics of the vehicle is designed to enhance the adaptability and robustness of the trajectory tracking system. For trajectory planning, the evaluation system is built to provide an initial path and speed profile across the entire space. The rough solution serves as a reference for refining a smooth trajectory that is constrained by vehicle dynamics within a convex space. Iterative optimization of path and speed is performed in each cycle to enable obstacle avoidance in time. In the aspect of trajectory tracking, the RMPC controller aims to minimize path deviations and control increments within the prediction horizon. The objective function is framed as a min-max problem and solved optimally using the linear matrix inequality (LMI) method. To further enhance tracking accuracy and system adaptability, a fuzzy-based strategy is used to adjust the weight coefficient matrix. Combination simulations for both trajectory planning and tracking are carried out under static and dynamic obstacle avoidance conditions. The results demonstrate that the vehicle successfully follows safe and comfortable obstacle avoidance paths, maintaining a small tracking error and high driving stability throughout the process.

OBJECTIVES:This study aims to identify stable factors associated with traffic conflict risk in expressway weaving segments, with a particular focus on addressing the challenge of unobserved data distribution bias between training and test datasets, which can compromise model reliability. METHODS:To mitigate distribution bias and enhance result robustness, a causally regularized logistic model (CRLM) with a global causal regularizer was employed. To validate the stability of the CRLM, multi-dataset validation and model parameter consistency tests were conducted using five datasets collected from the field and simulation in two weaving types. Meanwhile, classic logistic regression (LR) and eXtreme Gradient Boosting (XGBoost) were developed for comparison. RESULTS:In the multi-dataset validation test, the average area under receiver operating characteristic curve (AUC) of the CRLMs is close to that of the XGBoosts, but with a lower standard deviation, suggesting that the CRLM provides more stable predictive performance across different combinations of training and testing datasets. In the model parameter consistency test, the CRLM can identify more stable factors across heterogeneous traffic environments. Furthermore, the causal mechanisms underlying traffic conflict risk in Type A and Type B weaving segments are distinct. The hazardous traffic flow characteristics for each weaving type were discussed in detail. CONCLUSIONS:These findings provide a novel and robust methodological framework for traffic conflict risk analysis. In addition, the model results have practical implications for developing proactive traffic control strategies and enhancing automated driving systems (ADS) to improve traffic safety in expressway weaving segments.

A cloud-based intelligent connected vehicle (CICV) provides new approaches to realizing autonomous driving, and the relatively open wireless communication network of the vehicle cloud makes it vulnerable to cyberattacks. The cyberattacks that penetrate the vehicle system tamper with or interrupt the existing control signals, potentially leaving the vehicle system's actuators in an unsafe operating region for an extended period of time, thereby increasing the risk of actuator fault. For avoiding the degradation of path-tracking accuracy and driving stability of CICVs under the coexistence of denial-of-service (DoS) attacks and steering system actuator motor fault, this article proposes a robust security control method based on time-lag state observation under the dynamic event triggering (DET) at the network layer. First, a closed-loop control system including nonuniform triggering period delay, DoS attack delay, steering system fault, and external perturbation under DET policy of the vehicle cloud wireless communication network is established. Second, the sideslip angle before the delay is estimated based on the reduced-order Kalman filtering method, and a robust observer is further constructed to observe the system state and steering system faults after the delay. Third, an observer-based dynamic output feedback robust safety controller (RSC) is designed with the path-tracking accuracy and stability of the vehicle as the control objectives. Then, an electromechanical braking (EMB) clamping force distribution controller was proposed to execute the additional yawing moment calculated by the above controller. Finally, simulations and HiL tests were performed under typical operating conditions for validation. The results indicate that the proposed DET scheme reduces the communication load by 23.5% compared with the time-triggered and static strategies while maintaining comparable control performance. Under DoS attacks, the proposed safety controller decreases the average lateral displacement error and heading angle error by 171.2% and 158.7%, respectively, relative to the conventional model predictive control (MPC).