Search Results (971)

Highway vehicle–road collaborative systems rely on the effective deployment of roadside equipment (RSE) to support both traffic sensing and communication. In mixed connected and automated vehicle (CAV) and human-driven vehicle (HDV) traffic environments, existing studies on hybrid RSE deployment have mainly focused on unilateral deployment or scenarios with a high CAV penetration rate, whereas bilateral deployment under a low-to-medium CAV penetration rate has received limited attention. To address this gap, this study proposes a bilateral hybrid deployment framework for highways, in which sensing and communication RSE (scRSE) and communication RSE (cRSE) are jointly allocated based on data sensing accuracy and communication connection probability. The proposed method is validated through a case study on the Qinglan Expressway in Shandong Province, China. The results show that the bilateral hybrid deployment method outperforms the benchmark deployment methods in both sensing and communication performance. In a representative scenario, the mean symmetric mean absolute percentage error (SMAPE) decreases from 2.36% under bilateral uniform deployment to 0.94% under bilateral hybrid deployment, while the mean communication connection probability (MCCP) increases from 82.20% to 86.29%. Moreover, the proposed method performs better than unilateral deployment strategies under the same deployment conditions. These findings indicate that the proposed bilateral hybrid deployment framework offers a practical and cost-effective solution for highway RSE allocation in mixed traffic environments, particularly under low-CAV-penetration conditions. Full article

Urban intersection traffic signals play a crucial role in managing traffic flow and ensuring road safety. However, traditional actuated signal controllers make phase-switching decisions based on limited local traffic information, without leveraging network-wide context from navigation services. In this paper, we propose CATS, a Context-Aware Traffic Signal control system that jointly optimizes intersection signal control and road navigation for Connected and Automated Vehicles (CAVs). CATS integrates two key components: a Best-Combination CTR (BC-CTR) scheme and the Self-Adaptive Interactive Navigation Tool (SAINT). BC-CTR enhances the original Cumulative Travel-Time Responsive (CTR) scheme through a two-step selection procedure: it first identifies the phase with the highest cumulative travel time (CTT) and then selects the compatible phase combination with the greatest group CTT, providing an explicit improvement over the single-combination evaluation of the original CTR that allows for a more accurate response to real-time intersection demand. SAINT provides congestion-aware route guidance via a congestion-contribution step function, directing vehicles away from congested segments while signal timings simultaneously adapt to incoming traffic. Under a 100% CAV penetration setting, SUMO-based simulations across moderate-to-heavy traffic conditions (vehicle inter-arrival times of 5 to 9 s) show that CATS reduces the mean end-to-end travel time by up to 23.72% and improves the throughput by up to 93.19% over three baselines (fixed-time navigation with enhanced signal control, congestion-aware navigation with original signal control, and fixed-time navigation with original signal control), confirming that the co-design of navigation and signal control produces complementary benefits. Full article

Skeletal muscle differentiation is tightly regulated by membrane potential dynamics and voltage-dependent ion channel activity. Potassium (K⁺) and calcium (Ca²⁺) currents cooperate to orchestrate the transition of myoblasts into fusion-competent myotubes, and alterations in this process are associated with dystrophic phenotypes. Here, we investigated the electrophysiological remodeling accompanying C2C12 myogenesis and the modulatory effects of the polyphenol resveratrol (RES) on calcium voltage-gated channel subunit alpha 1 S (CACNA1S, Cav1.1, L-type) currents. Whole-cell patch-clamp recordings were performed in proliferating and differentiating C2C12 cells to characterize the temporal expression of K⁺ currents and voltage-dependent Ca²⁺ channels (VDCCs). During differentiation, three electrophysiological subpopulations were identified according to K⁺ current profiles: SK4+/EAG−/Kir−, SK4−/EAG+/Kir−, and SK4−/EAG+/Kir+. This sequence paralleled a progressive membrane hyperpolarization from −20 mV to −70 mV, consistent with the physiological maturation of myogenic cells. In C2C12 myocytes, nimodipine-sensitive L-type currents were the only Ca²⁺ conductance observed. Their activation threshold (~−30 mV) and half-activation voltage (V/2 ≈ −12 mV) indicated the co-expression of embryonic and adult Cav1.1 isoforms. Exposure to RES (30 µM, 48 h) produced a depolarizing shift in activation (ΔV/2 ≈ +9 mV) and a reduction in current amplitude across all voltages, consistent with a transition toward the adult splice variant of Cav1.1. These findings suggest that RES promotes electrophysiological maturation of skeletal muscle cells by modulating calcium channel expression and gating behavior. Given its known ability to correct splicing abnormalities in CACNA1S and related genes, resveratrol emerges as a promising pharmacological agent for restoring calcium homeostasis in neuromuscular disorders such as myotonic dystrophy type 1 (DM1). Full article

Background/Objectives: Head and neck squamous cell carcinoma (HNSCC) often exhibits limited clinical response to targeted therapies, such as Cetuximab. Identifying key drivers of tumor progression and elucidating the factors that modulate therapeutic sensitivity are essential for improving clinical outcomes. In this study, we aimed to investigate the role of CAV2 in HNSCC proliferation and its impact on Cetuximab sensitivity. Methods: Prognosis-associated genes in HNSCC were screened using the The Cancer Genome Atlas (TCGA) database. The functional role of Caveolin-2 (CAV2) in cell proliferation and apoptosis was assessed via Cell Counting Kit-8 (CCK-8), colony formation, and flow cytometry assays. Mechanistic insights were obtained through co-immunoprecipitation, ubiquitination assays, and proteomic analysis. The impact of CAV2 on Cetuximab sensitivity was evaluated both in vitro and in a xenograft mouse model. Results: Clinical analysis of 43 pairs of HNSCC tumor and adjacent normal tissues revealed that elevated CAV2 expression was significantly associated with poor prognosis in HNSCC patients (95%CI: 1.197–1.7518, p = 1.33 × 10⁻¹³). In vitro, knockdown of CAV2 suppressed cell proliferation and significantly increased apoptosis rates (from 5.1% to 10.8%, p = 0.004). Mechanistically, CAV2 interacted with the PACT protein and disrupted the PACT-PKR axis via the ubiquitin–proteasome pathway. Notably, CAV2 deficiency synergized with Cetuximab treatment, reducing the the half maximal inhibitory concentration (IC₅₀) value by 6-fold compared with control cells and suppressing tumor growth by 48.41% in xenograft models compared to Cetuximab monotherapy (p < 0.0001). Conclusions: In conclusion, these findings establish CAV2 as a critical regulator of HNSCC progression and Cetuximab sensitivity via post-translational modulation of the PACT–PKR axis. Targeting the CAV2/PACT/PKR axis may therefore represent a promising therapeutic strategy to potentiate the efficacy of EGFR-targeted therapy in patients with HNSCC. Full article

In high-density expressway environments, lane-changing (LC) maneuvers act as stochastic perturbations that compromise the hydrodynamic stability of traffic flow, leading to safety hazards and operational delays. While existing literature has extensively modeled crash severity in static complex environments (e.g., tunnels and mountainous terrains), there remains a critical deficiency in quantifying the dynamic, systemic risks induced by LC maneuvers under saturation conditions. To address this gap, this study proposes a novel Systemic Risk Assessment Framework. First, a Hidden Markov Model (HMM) is employed to decode the latent state transitions of following vehicles, quantifying the systemic consequence of LC maneuvers as “operational delay” based on traffic wave theory. Second, a Bayesian Network (BN) is constructed to infer the causal probability of risk, integrating geometric proxies such as insertion angle with kinematic variables. Validated with real-world trajectory data, the model achieves high accuracy in identifying risk accumulation precursors. This research contributes to the field of transportation systems by shifting the risk paradigm from static collision prediction to dynamic system reliability analysis, offering theoretical support for Connected and Autonomous Vehicle (CAV) decision logic. Full article

To investigate the impact mechanism of different platoon control strategies on mixed traffic flow, this paper evaluates the overall performance of different heterogeneous platoon control strategies in smoothing small traffic disturbances and improving traffic safety. First, this paper derives the stability conditions for homogeneous and mixed traffic flow based on transfer function theory. Second, by simulating small disturbance experiments, the trend of speed under different traffic densities and the penetration rate of CAVs are analyzed. The characteristics of speed change coefficients under different platoon control strategies are comparatively analyzed based on the results in part 1. Finally, numerical simulation experiments were designed to analyze the safety performance of traffic flow under each strategy. The results show that (1) the combination of a variable time gap strategy with vehicle speed has the strongest ability to suppress disturbances. Among the combination spacing strategies, the combination of the variable time gap strategy with vehicle speed and the constant time gap strategy performs best in smoothing small disturbances. (2) At low penetration rates, incorporating CAVs may increase the instability of the traffic flow, while at high rates, CAVs effectively enhance the stability. These findings provide important guidance for selecting platoon control strategies in mixed traffic flow environments from the perspective of stability and safety. Full article

Connected autonomous vehicles (CAVs) rely on deep neural network-based perception systems to operate safely in complex driving environments. However, these systems remain vulnerable to adversarial perturbations that can induce misclassification without perceptible changes to human observers. Explainable artificial intelligence (XAI) has been proposed as a potential adversarial detection mechanism by exposing inconsistencies in model attention. This study evaluated the effectiveness of NoiseCAM-based explanation-space detection on the German Traffic Sign Recognition Benchmark (GTSRB) using a single 32 × 32 CNN architecture. Adversarial examples were generated using FGSM under perturbation budgets $ϵ$ = 0.01–0.10, and detection performance was evaluated using accuracy, precision, recall, F1-score, and ROC–AUC. Results show that NoiseCAM achieves detection accuracies between 51.8% and 52.9% with ROC–AUC values of 0.52–0.53, only marginally above random discrimination (0.5). Class-wise analysis further reveals substantial variability in detection reliability across traffic sign categories, with visually structured regulatory signs exhibiting higher separability than complex warning signs. These findings suggest that explanation-space inconsistencies alone provide limited adversarial detection capability in low-resolution, safety-critical perception pipelines. The study contributes to the understanding of the operational limits of explanation-based adversarial detection and highlights the need to integrate XAI signals with complementary robustness or uncertainty-aware mechanisms for reliable deployment in autonomous driving systems. Full article

Optimizing traffic performance using artificial intelligence (AI) has consistently been a prominent direction in the development of intelligent transportation systems. While numerous studies have proposed methodologies for integrating cooperative connected and autonomous vehicles (CCAVs) with traffic signal systems via V2X communication, they often rely on simplified control strategies or lack effective coordination between signal timing and vehicle behavior. In this study, we propose a novel, integrated traffic signal control strategy combined with CAVs using deep reinforcement learning. Our key differentiation lies in the simultaneous optimization of signal phases using the Soft Actor–Critic (SAC) algorithm and the regulation of CCAVs via cooperative adaptive cruise control and Green Light Optimal Speed Advisory. This dual approach allows the signal controller to leverage rich state information from CAVs and the road infrastructure, enabling more anticipatory and cooperative decisions. The proposed approach is implemented and evaluated through various scenarios using the Simulation of Urban MObility (SUMO) platform. The results demonstrate the superior learning performance and robustness of the proposed model. Specifically, our proposed model achieves a significant reduction in average vehicle waiting time by up to over 80% compared to baseline models under high-demand scenarios (4800–6000 veh/h). These findings underscore the critical importance of joint optimization in future intelligent transportation systems, paving the way for more resilient urban traffic management. Full article

Soil and water bioengineering (SWBE) is increasingly used as a nature-based solution for riverbank stabilization and riparian restoration, yet its effectiveness in tropical environments remains constrained by limited field-based evidence of species performance under hydrological disturbance. This study evaluated the establishment success and ecological effectiveness of four native riparian species (Croton urucurana Baill., Sesbania virgata (Cav.) Pers., Iochroma arborescens (L.) J.M.H.Shaw, and Gymnanthes schottiana Müll.Arg.) installed as live cuttings on a riprap structure exposed to recurrent flooding along the Paraopeba River, Brazil. A total of 160 live cuttings were monitored over a 33-month establishment period to assess survival, structural development, spontaneous vegetation recruitment, and changes in soil chemical properties and soil organic carbon stocks. Flooding acted as a dominant ecological filter, causing substantial early mortality, with overall survival declining sharply during a 70-day inundation period that included 58 consecutive days of submergence. Croton urucurana exhibited the highest survival and structural development, reaching median heights exceeding 5 m and cumulative shoot diameters greater than 100 mm after 33 months, whereas Gymnanthes schottiana showed complete mortality within the first year. Vegetation establishment facilitated spontaneous recruitment of native woody species, with 22 individuals recorded in planted sections compared to only 3 in adjacent non-planted areas. Soil organic carbon stocks increased from 38.9 to 60.6 Mg C ha⁻¹ in the 0–40 cm soil profile, indicating rapid soil development. These results demonstrate that SWBE interventions can simultaneously promote riverbank stabilization, vegetation recovery, and soil carbon accumulation. By providing quantitative field-based evidence under realistic hydrological disturbance conditions, this study advances the understanding of species selection and the ecological effectiveness of SWBE interventions in tropical riparian ecosystems. Full article

With the increasing proportion of intelligent assisted vehicles in traffic flow, the existing primary highway traffic management measures exhibit insufficient adaptability to mixed traffic flows with high penetration of such vehicles. This study proposes a simulation analysis method based on SUMO for the primary highway traffic involving mixed flows of vehicles and conventional human-driven vehicles. It elaborates on the simulation configuration, network construction, demand generation, data output and visualization, and selection strategies. A Python-based post-processing tool for simulation results was developed. Gradient control simulation experiments (5% coarse adjustment → 1% fine analysis) were designed to investigate the impact of Connected and Automated Vehicle (CAV) penetration rates and the configuration of a dedicated CAV lane on the inner side of a bidirectional four-lane primary highway on the network Level of Service (LOS). Results indicate that when the CAV penetration rate ranges between 18% and 52%, setting one dedicated lane on the inner side can improve the LOS. However, if the penetration rate is below 18%, such a lane configuration reduces the LOS. When the penetration rate exceeds 52%, the impact becomes negligible. This study establishes a simulation framework for analyzing mixed CAV/conventional vehicle flows on the primary highways, systematically quantifying the penetration rate threshold (18–52%) for CAV-dedicated lanes. This provides a strategic basis for phased implementation based on actual CAV penetration rates and offers a strategic basis for the phased implementation of dedicated CAV lanes on inner lanes of four-lane highways, depending on the actual CAV penetration rate. Full article

With the deep integration of the modern automotive industry and artificial intelligence technologies, connected and automated vehicles (CAVs) have emerged as a key breakthrough for improving traffic safety and operational efficiency. This study proposes a distributed multi-vehicle cooperative trajectory planning and control framework for ramp merging and diverging scenarios, integrating Deep Neural Networks (DNNs) with Model Predictive Control (MPC). The methodology consists of three key components: First, a distributed cooperative architecture based on dynamic topology is constructed to effectively reduce communication loads; second, a feature point-based Cubic Bézier Curve trajectory generation method is proposed, enabling flexible path planning with reduced reliance on high-precision maps; finally, a DNN-accelerated MPC solving strategy (NN-MPC) is designed. This strategy employs an offline-trained deep neural network to approximate the online optimization process, supplemented by a terminal Safety Check mechanism and a dynamic surrounding vehicle selection algorithm. Experimental results demonstrate that the proposed method successfully reproduces the planning capability of offline high-precision MPC in ramp merging and diverging scenarios while reducing computation time to the millisecond level. It effectively overcomes the myopic decision-making problem of traditional real-time algorithms, achieving smoother conflict resolution and higher traffic efficiency. Notably, quantitative validation confirms that this cooperative framework achieves an approximate 30% reduction in average travel delay compared to the non-cooperative baseline. This study confirms the engineering advantages of the hybrid architecture under dynamic high-density traffic flows, significantly enhancing the system’s real-time response capability while balancing the safety and riding comfort of cooperative driving. Full article

Unmanned Aerial Vehicle (UAV), when equipped as communication relays, offer a flexible solution to extend Vehicle-to-Vehicle (V2V) communications beyond fixed infrastructure and Non-Line-of-Sight constraints. In this setting, the allocation of radio resources, across time, frequency and space through beamforming, is challenged by the mobility of Connected and Autonomous Vehicles (CAVs) and their temporal dependencies, as access opportunities depend on prior transmission outcomes such as queue backlog or failed attempts. This paper proposes a Radio Resource Assignment (RRA) framework for UAV-aided V2V networks with beamforming-capable UAV relays. The model discretizes time and space to account for mobility and to track the movement of groups of CAVs across beam segments. The model also incorporates Time Division Multiple Access (TDMA)-based scheduling, beam activation constraints, and realistic traffic generation patterns. Analytical expressions are derived for per-user success probability and system throughput under both, ideal and realistic conditions, and they are validated against simulations, confirming the accuracy of the proposed approximations. Numerical results highlight trade-offs involving UAV altitude and resource allocation interval, while a heuristic beam-activation optimization strategy is shown to further enhance performance, achieving up to 12% throughput gain over uniform activation. Full article

Enhancing traffic flow stability is a critical approach for achieving energy conservation and emission reduction in road transportation. While existing cooperative car-following strategies for connected and automated vehicles (CAVs) are effective, their heavy reliance on reliable Vehicle-to-Everything (V2X) communication limits practical deployment. This study proposes an energy-optimal car-following model for CAVs, introducing a regulation term based on the predicted optimal speed difference. Rather than directly using predicted kinematic variables, this mechanism adjusts acceleration based on the difference in optimal velocity between predicted and current headways. This leverages the inherent filtering of the optimal velocity function to ensure smooth control. Linear and nonlinear stability analysis confirm the model’s effectiveness in suppressing traffic disturbances and suppression of stop-and-go wave propagation, thereby laying the theoretical foundation for smoother traffic flow and the resulting reductions in energy consumption and emissions. Simulations validate the theoretical findings. Compared to the classical Full Velocity Difference (FVD) model, the proposed model achieves significant reductions in energy consumption (38.82%), CO₂ emissions (39.41%), and NO_x emissions (83.46%). The model also reduces rear-end collision risks, ensuring higher safety. These findings indicate that the proposed ego-vehicle predictive framework provides a communication-independent and practically viable approach for improving the energy efficiency and stability of CAV traffic flow. Full article

Advancements in the Connected and Autonomous Vehicles (CAVs) industry are revolutionizing modern transportation through advanced automation levels and connectivity capabilities. While autonomous vehicles can operate using onboard sensors alone, the integration of Vehicle-to-Everything (V2X) communication is vital for enabling seamless connectivity and cooperative decision-making. However, the increasing exchange of traffic and sensor data introduces critical privacy challenges, necessitating robust and scalable privacy-preserving mechanisms to ensure user trust and compliance with data protection regulations. The inherently dynamic nature of CAV environments, characterized by high mobility, short-duration connections, and frequent handovers, further complicates the design of effective privacy models. In this context, this paper investigates the evolving data privacy risks associated with CAV systems. It critically reviews existing privacy-preserving approaches and identifies their limitations in dynamic vehicular contexts. In particular, the paper explores the role of Federated Learning, permissioned blockchain and Software-Defined Networking (SDN) as enabling technologies for privacy preservation in CAVs. The analysis concludes with targeted recommendations for optimizing these frameworks to enhance privacy resilience in next-generation intelligent transportation systems. Full article

Achieving cooperative perception and decision-making among connected and autonomous vehicles (CAVs) in mixed-traffic ramp merge scenarios is crucial for building a swarm intelligence-based traffic control system. However, existing cooperative decision-making methods struggle to adequately model and represent the dynamic collaborative interactions among heterogeneous agents in mixed-traffic environments, which can lead to traffic congestion or even severe accidents in ramp merging areas. Therefore, this paper proposes an Enhanced Graph Reinforcement Learning algorithm based on a Vehicle Collaboration Graph (VCG-EGRL) to enable cooperative merging decisions for CAVs in mixed-traffic ramp merging scenarios. First, a vehicle collaboration intensity (VCI) model is designed to effectively model the intensity of collaborative interactions among vehicles. Then, based on the VCI model, the perception–communication relationships between vehicles and the vehicle-to-infrastructure (V2I) communication relationships are jointly constructed to form a local–global cooperative graph, which represents the dynamic collaborative relationships of the vehicle network from macro and micro perspectives and deeply explores the driving behavior of vehicles. Subsequently, a Graph Convolutional Network enhanced with Kolmogorov–Arnold Networks (KANs), referred to as GKAN, is employed to extract and aggregate the driving features of vehicles from the local–global graph. Finally, a graph mutual information maximization method is used to optimize the iterative process of the Graph Reinforcement Learning strategy, ensuring the generation of accurate lane-changing decisions for CAVs. Experimental results in ramp merging scenarios under varying traffic conditions demonstrate that the proposed method outperforms baseline models in terms of merging success rate, efficiency, and robustness. Full article