Search Results (965)

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

Protease-activated receptor 2 (PAR2) is a G protein-coupled receptor (GPCR) expressed in both the peripheral and central nervous systems. It plays a pivotal role in mediating neuroimmune interactions, particularly in the context of inflammation and pain. Upon activation by proteases, PAR2 modulates nociception through signaling cascades that influence key ion channels, including transient receptor potential (TRP) ion channels vanilloid 1 and 4 (TRPV1 and TRPV4), ankyrin 1 (TRPA1), acid-sensing ion channel 3 (ASIC3), P2X purinoceptor 3 (P2X3), Cav3.2 (T-type Ca²⁺ channel), and potassium Kv7 (M-current) channels, altering their expression and function. Through this crosstalk, PAR2 contributes to heightened neuronal excitability and pain hypersensitivity in various inflammatory conditions. In this narrative review, we highlight and discuss the mechanistic and functional interplay between PAR2 and nociceptive ion channels, which might be contributing to the pathogenesis of inflammatory pain. Targeting these specific molecular interactions between PAR2 and nociceptive ion channels may offer a promising therapeutic strategy for treating inflammatory pain. Full article

Connected and Autonomous Vehicles (CAVs) are exposed to increasingly sophisticated cyber threats hidden within high-dimensional, heterogeneous network traffic. A critical bottleneck in existing Intrusion Detection Systems (IDS) is the feature heterogeneity gap: discrete protocol signatures (e.g., flags, services) and continuous traffic statistics (e.g., flow duration, packet rates) reside in disjoint latent spaces. Traditional deep learning approaches typically rely on naive feature concatenation, which fails to capture the intricate, non-linear semantic dependencies between these modalities, leading to suboptimal performance on long-tail, minority attack classes. This paper proposes HCA-IDS, a novel framework centered on Semantics-Aware Cross-Modal Alignment. Unlike heavy-weight models, HCA-IDS adopts a streamlined Multi-Layer Perceptron (MLP) backbone optimized for edge deployment. We introduce a dedicated Multi-Head Cross-Attention mechanism that explicitly utilizes static “Pattern” features to dynamically query and re-weight relevant dynamic “State” behaviors. This architecture forces the model to learn a unified semantic manifold where protocol anomalies are automatically aligned with their corresponding statistical footprints. Empirical assessments on the NSL-KDD and CICIDS2018 datasets, validated through rigorous 5-Fold Cross-Validation, substantiate the robustness of this approach. The model achieves a Macro-F1 score of over 94% on 7 consolidated attack categories, exhibiting exceptional sensitivity to minority attacks (e.g., Web Attacks and Infiltration). Crucially, HCA-IDS is ultra-lightweight, with a model size of approximately 1.00 MB and an inference latency of 0.0037 ms per sample. These results confirm that explicit semantic alignment combined with a lightweight architecture is key to robust, real-time intrusion detection in resource-constrained CAVs. Full article

Two biological control agents, Archanara neurica (Hübner) and Lenisa geminipuncta (Haworth) (Lepidoptera: Noctuidae), are being released in Canada for the control of invasive common reed, Phragmites australis australis (Cav.) Trin. Ex Steud (hereafter Phragmites). The release of larvae implanted in cut Phragmites stems is the most reliable way to establish agents at new sites, but the number of larvae that can be used for releases is limited by the short period of time over which egg hatch occurs. We conducted a cold storage experiment to assess whether the timing of egg hatch can be manipulated without affecting hatch success. Additionally, we conducted visual assessments of developing eggs to determine whether hatch timing can be predicted based on early signs of development. Eggs hatched indoors had lower hatch rates than eggs hatched in outdoor conditions. For A. neurica and L. geminipuncta, eggs could be held in cold storage for 11 and 8 weeks, respectively, without affecting hatch rates. Eggs of both species began hatching 4–7 days after the appearance of visible signs of larval development. Manipulating the timing of hatch in A. neurica and L. geminipuncta will increase the number of larval releases that can be conducted during the spring and allow the timing of releases to be optimized. Full article

Lipoprotein(a) [Lp(a)] is a causal, genetically determined risk factor for atherosclerotic cardiovascular disease (ASCVD) and calcific aortic valve stenosis (CAVS). Although elevated Lp(a) affects approximately 20% of the global population, specific pharmacological options have long been unavailable, leaving a major gap in residual risk management. This review synthesizes current understanding of Lp(a) molecular architecture, genetics, and metabolism, and integrates mechanistic evidence linking Lp(a) to pro-atherogenic, pro-inflammatory, and pro-thrombotic pathways. We summarize epidemiological and genetic data associating Lp(a) with a broad spectrum of cardiovascular outcomes and discuss current clinical guidelines on screening and risk stratification. Furthermore, we provide an up-to-date overview of the emerging therapeutic landscape, including RNA-targeted therapies and novel oral small molecules. With pivotal phase 3 outcome trials nearing completion, the field is transitioning from viewing Lp(a) as an untreatable biomarker to an actionable therapeutic target, with important implications for precision cardiovascular prevention. Full article

Background: Caveolin-1 (Cav-1) is a protein found in various forms and locations within cells and tissues throughout the body. Studying its structure and function provides valuable insights into key cellular processes such as growth, death, and cell signaling. This review synthesizes evidence from human studies and animal models to elucidate the complex role of Caveolin-1 (Cav-1) in regulating nitric oxide (NO) synthesis within the vasculature and perivascular adipose tissue (PVAT) during atherosclerosis. Cav-1 is a master regulator of endothelial NO synthase (eNOS), a relationship well-defined in rodent endothelial cells and cell lines. In humans, loss-of-function CAV1 mutations are linked to pulmonary arterial hypertension, suggesting a protective vascular role. Paradoxically, Cav-1 is upregulated in atherosclerotic plaques. Whether this represents a pathological process reducing NO bioavailability or a compensatory response remains unclear. Furthermore, the direct translation of the Cav-1/eNOS axis to PVAT—a metabolically active tissue expressing Cav-1—is not fully established outside of preclinical models. PVAT influences vascular tone and inflammation, potentially contributing to the paradoxical, stage-specific roles of Cav-1 in disease. Resolving these questions requires integrating human observational data with mechanistic insights from animal models to evaluate Cav-1 as a therapeutic target in vascular disease. Full article