Search Results (111)

Distracted driving is one of the primary causes of road traffic accidents. Behavior recognition technology based on machine vision has emerged as a research hotspot due to its non-contact and high-efficiency nature. To address the challenges of complex lighting conditions in the driver’s cabin, low detection accuracy for small-scale keypoints, and the difficulty in effectively characterizing behavioral features, this paper proposes a distracted driving behavior recognition method based on an improved YOLOv8n-Pose model and multi-feature fusion. First, the original YOLOv8n-Pose model is optimized. A P2 detection layer is added to enhance the feature extraction capabilities for small-scale human keypoints, and the SE attention module is incorporated to improve the model’s robustness under complex lighting conditions. In addition, the loss function is replaced with focal loss to tackle the class imbalance problem, thus forming the YOLOv8n-PSF-Pose keypoint detection network. Subsequently, based on the coordinates of 12 human keypoints extracted by this network, a multi-dimensional feature vector is constructed, which takes joint angles as the core and integrates the relative distances between keypoints and the number of valid keypoints. Finally, a BP neural network is adopted to classify the constructed feature vectors, enabling the accurate recognition of six typical distracted driving behaviors (normal driving, drinking or eating, making phone calls, using mobile phones, operating vehicle infotainment systems, and turning around to fetch items). The experimental results show that the improved YOLOv8n-PSF-Pose model achieves an mAP50 of 93.8% in keypoint detection, which is 6.7 percentage points higher than the original model; the BP classification model based on multi-feature fusion achieves an F1-score of 97.7% in the behavior recognition task, which is significantly better than traditional classifiers such as SVM and random forest, and the image processing speed on the NVIDIA RTX 3090TI reaches a high throughput of 45 FPS. This proves that the proposed method achieves an excellent balance between accuracy and speed. This study provides an effective solution for the real-time and accurate recognition of distracted driving behaviors. Full article

This study evaluates how different alert conditions influence driver attention transitions under conditions of visual distraction using sequence analysis. Employing a within-subject experimental design, 13 participants underwent trials in a driving simulator, experiencing three distinct alert conditions: face-tracking auditory alerts, steering wheel auditory torque alerts, and a control scenario without alerts. An eye-tracking system was used to capture drivers’ gaze durations and sequences across three key areas of interest: road, dashboard, and tablet-based infotainment system. Analysis involved computation of transition probabilities, Markov chain modeling for long-term attentional distributions, and entropy analyses to quantify the randomness of gaze transitions. Results showed that face-tracking alerts significantly increased the likelihood of gaze redirection to the road compared to the other conditions, enhancing both immediate and sustained attention. Steering wheel torque alerts demonstrated minimal effectiveness, sometimes performing worse than the no-alert condition due to their passive nature, allowing drivers to bypass attention redirection. Steady-state analyses confirmed that face alerts notably improved sustained driver focus on the road by approximately 3.6%, reinforcing their utility for prolonged attentional control. Entropy analyses further revealed that face alerts provided an optimal balance between structured attention shifts and behavioral flexibility, enhancing attentional predictability. Findings are consistent with previous literature, emphasizing the superior effectiveness of active, gaze-based interventions over passive mechanisms. This research underscores the importance of designing proactive alert systems in vehicle safety technology to effectively mitigate visual distraction-related risks. Full article

The rise of connected and automated vehicles has transformed in-vehicle infotainment (IVI) systems into critical gateways linking user interfaces, vehicular networks, and cloud-based fleet services. A concerning architectural reality is that hardcoded credentials like access point names (APNs) in IVI firmware create a cross-layer attack surface where local exposure can escalate into entire vehicle fleets being remotely compromised. To address this risk, we propose a cross-layer security framework that integrates firmware extraction, symbolic execution, and targeted fuzzing to reconstruct authentic IVI-to-backend interactions and uncover high-impact web vulnerabilities such as server-side request forgery (SSRF) and broken access control. Applied across seven diverse automotive systems, including major original equipment manufacturers (OEMs) (Mercedes-Benz, Tesla, SAIC, FAW-VW, Denza), Tier-1 supplier Bosch, and advanced driver assistance systems (ADAS) vendor Minieye, our approach exposes systemic anti-patterns and demonstrates a fully realized exploit that enables remote control of approximately six million Mercedes-Benz vehicles. All 23 discovered vulnerabilities, including seven CVEs, were patched within one month. In closed automotive ecosystems, we argue that the true measure of efficacy lies not in maximizing code coverage but in discovering actionable, fleet-wide attack paths, which is precisely what our approach delivers. Full article

Accurate and continuous tracking of athletes is essential to meet the infotainment demands and health and safety requirements of major marathon events. However, the current ability to track individual athletes or groups at mass sporting events is severely limited by the weight, size and cost of the equipment required. In marathons, Radio Frequency Identification (RFID) technology is typically used for timing but can only provide accurate tracking at widely spaced intervals, relying on heuristic and interpolation algorithms to estimate runners’ positions between measurement points. Alternative IOT solutions, such as Low Power Wide Area Network (LWPAN), have limitations in terms of range and require dedicated infrastructure and regulation. Therefore, we analyzed the potential use of smartwatches as accurate and continuous tracking devices for athletes, assessing battery consumption during tracking and standby drain, achievable GPS tracking accuracy and the update rate of data transfer from the device in urban environments. The 4G LTE battery drain is different from non-urban areas. Analysis of standby usage is necessary as devices need to conserve power for tracking. We programmed an application that allowed us to control the modalities of acquisition and transmission intervals, integrating advanced logging and statistics at runtime, and evaluated the achievable results in major marathon events. Our empirical evaluation at the Frankfurt, Athens and Vienna marathons with three different types of smartwatch tracking platforms showed the validity of this approach, while respecting some necessary limitations of the tracking settings. Median battery drain was 5.3%/h in standby before race start (σ 1.5) and 16.5%/h in tracking mode (σ 3.29), with an actual update rate varying between 19 and 57 s on Wear OS devices. The average GPS offset to the track was 4.5 m (σ 8.7). Future work will focus on integrating these consumer devices with existing time and tracking infrastructure. Full article

The paradigm of cellular vehicle-to-everything (C-V2X) communications assisted by unmanned aerial vehicles (UAVs) is poised to revolutionize the future of sixth-generation (6G) intelligent transportation systems, as outlined by the international mobile telecommunication (IMT)-2030 vision. This integration of UAV-assisted C-V2X communications is set to enhance mobility and connectivity, creating a smarter and reliable autonomous transportation landscape. The UAV-assisted C-V2X networks enable hyper-reliable and low-latency vehicular communications for 6G applications including augmented reality, immersive reality and virtual reality, real-time holographic mapping support, and futuristic infotainment services. This paper presents a Markov chain model to study a third-generation partnership project (3GPP)-specified C-V2X network communicating with a flying UAV for task offloading in a Federated Learning (FL) environment. We evaluate the impact of various factors such as model update frequency, queue backlog, and UAV energy consumption on different types of communication latency. Additionally, we examine the end-to-end latency in the FL environment against the latency in conventional data offloading. This is achieved by considering cooperative perception messages (CPMs) that are triggered by random events and basic safety messages (BSMs) that are periodically transmitted. Simulation results demonstrate that optimizing the transmission intervals results in a lower average delay. Also, for both scenarios, the optimal policy aims to optimize the available UAV energy consumption, minimize the cumulative queuing backlog, and maximize the UAV’s available battery power utilization. We also find that the queuing delay can be controlled by adjusting the optimal policy and the value function in the relative value iteration (RVI). Moreover, the communication latency in an FL environment is comparable to that in the gross data offloading environment based on Kullback–Leibler (KL) divergence. Full article

The Vehicular Internet of Things (V-IoT) has emerged as a cornerstone of next-generation intelligent transportation systems (ITSs), enabling applications ranging from safety-critical collision avoidance and cooperative awareness to infotainment and fleet management. These heterogeneous services impose stringent quality-of-service (QoS) demands for latency, reliability, and fairness while competing for limited and dynamically varying spectrum resources. Conventional schedulers, such as round-robin or static priority queues, lack adaptability, whereas deep reinforcement learning (DRL) solutions, though powerful, remain computationally intensive and unsuitable for real-time roadside unit (RSU) deployment. This paper proposes a lightweight and interpretable reinforcement learning (RL)-based spectrum management framework for Vehicular Internet of Things (V-IoT) networks. Two enhanced Q-Learning variants are introduced: a Value-Prioritized Action Double Q-Learning with Constraints (VPADQ-C) algorithm that enforces reliability and blocking constraints through a Constrained Markov Decision Process (CMDP) with online primal–dual optimization, and a contextual Q-Learning with Upper Confidence Bound (Q-UCB) method that integrates uncertainty-aware exploration and a Success-Rate Prior (SRP) to accelerate convergence. A Risk-Aware Heuristic baseline is also designed as a transparent, low-complexity benchmark to illustrate the interpretability–performance trade-off between rule-based and learning-driven approaches. A comprehensive simulation framework incorporating heterogeneous traffic classes, physical-layer fading, and energy-consumption dynamics is developed to evaluate throughput, delay, blocking probability, fairness, and energy efficiency. The results demonstrate that the proposed methods consistently outperform conventional Q-Learning and Double Q-Learning methods. VPADQ-C achieves the highest energy efficiency (≈$8.425\times {10}^7$ bits/J) and reduces interruption probability by over $60\%$, while Q-UCB achieves the fastest convergence (within ≈190 episodes), lowest blocking probability (≈0.0135), and lowest mean delay (≈0.351 ms). Both schemes maintain fairness near $0.364$, preserve throughput around 28 Mbps, and exhibit sublinear training-time scaling with $\mathcal{O}\left(1\right)$ per-update complexity and $\mathcal{O}\left(N^2\right)$ overall runtime growth. Scalability analysis confirms that the proposed frameworks sustain URLLC-grade latency (<0.2 ms) and reliability under dense vehicular loads, validating their suitability for real-time, large-scale V-IoT deployments. Full article

Objective: In the digital age, short-video platforms are key channels for adolescents’ sex education, yet content strategies and their effects remain unclear. This study analyzes Douyin using an integrated source–content–effect framework, identifies infotainment strategies by creator type, and examines their impact on interaction and topic engagement. Methods: Quantitative content analysis of 465 sex-education videos. Content was coded on informational and entertainment value. Four information–entertainment combinations were tested. Engagement outcomes (likes, comments, favorites, shares) were modeled with negative binomial regression; the likelihood that comments were sex-education–related was modeled with logistic regression. Creator type (medical professionals vs. individual creators) entered as a covariate. Results: A functional-infotainment pattern emerged. High information–high entertainment performed best across all interaction metrics. Low information–high entertainment (pure entertainment) performed worst, significantly suppressing deeper engagement and topical discussion. Medical professionals emphasized medicalized, low-risk knowledge; individual creators covered more diverse topics yet likewise avoided sensitive issues. Conclusions: Under algorithmic incentives and cultural norms, Douyin’s sex-education content is not entertainment-first. Dissemination is driven by information-rich content delivered through a functional-infotainment model. Findings refine infotainment theory and offer data-driven guidance: prioritize informational value while pairing it with engaging forms (creators), support high-information content and proactive governance (platforms), and inform education policy. Full article

Vehicle-to-vehicle (V2V) and vehicle-to-network (V2N) communications are two key components of intelligent transport systems (ITSs) that can share spectrum resources through in-band overlay. V2V communication primarily supports traffic safety, whereas V2N primarily focuses on infotainment and information exchange. Achieving reliable V2V transmission alongside high-rate V2N services in resource-constrained, dynamically changing traffic environments poses a significant challenge for resource allocation. To address this, we propose a novel reinforcement learning (RL) framework, termed Graph Attention Network (GAT)-Advantage Actor–Critic (GAT-A2C). In this framework, we construct a graph based on V2V links and their potential interference relationships. Each V2V link is represented as a node, and edges connect nodes that may interfere. The GAT captures key interference patterns among neighboring vehicles while accounting for real-time mobility and channel variations. The features generated by the GAT, combined with individual link characteristics, form the environment state, which is then processed by the RL agent to jointly optimize the resource blocks allocation and the transmission power for both V2V and V2N communications. Simulation results demonstrate that the proposed method substantially improves V2N rates and V2V communication success ratios under various vehicle densities. Furthermore, the approach exhibits strong scalability, making it a promising solution for future large-scale intelligent vehicular networks operating in dynamic traffic scenarios. Full article

The growing demand for bandwidth-intensive vehicular applications—particularly ultra-high-definition streaming and immersive panoramic video—is pushing current network infrastructures beyond their limits, especially in urban areas with severe congestion and degraded user experience. To address these challenges, we propose an aerial-assisted vehicular network architecture that integrates 6G base stations, distributed massive MIMO networks, visible light communication (VLC), and a heterogeneous aerial network of high-altitude platforms (HAPs) and drones. At its core is a context-aware dynamic bandwidth allocation algorithm that intelligently routes infotainment data through optimal aerial relays, bridging connectivity gaps in coverage-challenged areas. Simulation results show a 47% increase in average available bandwidth over conventional first-come-first-served schemes. Our system also satisfies the stringent latency and reliability requirements of emergency and live infotainment services, creating a sustainable ecosystem that enhances user experience, service delivery, and network efficiency. This work marks a key step toward enabling high-bandwidth, low-latency smart mobility in next-generation urban networks. Full article

The integration of 5G technology with existing LTE architectures has facilitated the widespread adoption of firmware over-the-air (FOTA) updates across Android-based devices, including mobile and automotive infotainment systems. While 5G enhances communication speed and convenience, vulnerabilities related to firmware tampering and Man-in-the-Middle (MitM) attacks still present considerable risks. This study analyzes the security of the FOTA update process for six Android-based mobile manufacturers and one vehicle model, all of which utilize LTE architectures within 5G networks. Through comprehensive security testing, we explore the potential threats of certificate bypass, firmware tampering, and communication interception. Our proposed framework identifies critical security flaws in the FOTA implementation, recommending improvements in encryption protocols and integrity verification mechanisms to secure the firmware update process. Our findings underscore the urgent requirement for enhanced security measures in the deployment of FOTA updates to address vulnerabilities in Android-based IoT devices and automotive systems. Full article

Autonomous driving technologies for environmental perception are mostly based on visual cues obtained from sensors like cameras, RADAR, or LiDAR. They capture the environment as if seen through “human eyes”. If this visual information is complemented with auditory information, thereby also providing “ears”, driverless cars can become more reliable and safer. In this paper, an Acoustic Event Detection model is presented that can detect various acoustic events in an automotive context along with their time of occurrence to create an audio scene description. The proposed detection methodology uses the pre-trained network Bidirectional Encoder representation from Audio Transformers (BEATs) and a single-layer neural network trained on the database of real audio recordings collected from different cars. The performance of the model is evaluated for different parameters and datasets. The segment-based results for a duration of 1 s show that the model performs well for 11 sound classes with a mean accuracy of 0.93 and F1-Score of 0.39 for a confidence threshold of 0.5. The threshold-independent metric mAP has a value of 0.77. The model also performs well for sound mixtures containing two overlapping events with mean accuracy, F1-Score, and mAP equal to 0.89, 0.42, and 0.658, respectively. Full article

The ability to automate and personalize the recommendation of multimedia contents to consumers has been gaining significant attention recently. The burgeoning demand for digitization and automation of formerly analog communication processes has caught the attention of researchers and professionals alike. In light of the recent interest and anticipated transition to fully autonomous vehicles, this study proposes a text–image embedding method recommender system for the optimization of personalized multimedia content for in-vehicle infotainment. This study leverages existing pre-trained text embedding models and pre-trained image feature extraction methods. Previous research to date has focused mainly on textual-only or image-only analyses. By employing similarity measurements, this study demonstrates how recommendation of the most relevant multimedia content to consumers is enhanced through text–image embedding. Full article

As modern vehicles continue to evolve, advanced technologies are integrated to enhance the driving experience. A key enabler of this advancement is the Controller Area Network (CAN) bus, which facilitates seamless communication between vehicle components. Despite its widespread adoption, the CAN bus was not designed with security as a priority, making it vulnerable to various attacks. In this paper, we propose CANGuard, an Intrusion Detection System (IDS) designed to detect attacks on the CAN network and identify the originating node in real time. Using a simulated CAN-enabled system with four nodes representing diverse vehicle components, we generated a dataset featuring Denial-of-Service (DoS) attacks by exploiting the arbitration feature of the CAN bus, which prioritizes high-criticality messages (e.g., engine control) over lower-criticality ones (e.g., infotainment). We trained and evaluated several machine learning models for their ability to detect attacks and pinpoint the responsible node. Results indicate that Gradient Boosting outperformed other models, achieving high accuracy in both attack detection and node identification. While the Multi-Layer Perceptron (MLP) model demonstrated strong attack detection performance, it struggled with node identification, achieving less than 50% accuracy. These findings underscore the potential of tree-based models for real-time IDS applications in CAN-enabled vehicles. Full article

Car cabin noise generated by heating, ventilation, and air-conditioning (HVAC) systems significantly impacts passengers’ acoustic comfort. In fact, with the reduction in engine noise due to the passage from internal combustion to electric or hybrid-electric engines, interior background noise has dramatically reduced, especially at 25% and 50% HVAC airflow rates. While previous research has focused on the effect of HVAC noise in car cabins, this paper investigates the possibility of using car infotainment-system audio cues to moderate onboard sound quality perception. A laboratory experiment combining the factors of infotainment-system audio (ISA) cues, signal-to-noise ratios (SNRs), and airflow rates (AFRs) at different levels was performed in two university laboratories in Italy and Japan involving groups of local individuals. The results indicate that introducing ISA cues in car cabins fosters improvements in the perceived aesthetic dimension of sound quality, making it more functioning, natural, and pleasant. For the Italian group, adding ISA cues also moderated the loudness dimension by reducing noise perception. The moderating effects of ISA cues differed between the Italian and Japanese groups, depending on the AFR. All these effects were more evident at the SNR level of −4 dB when the ISA cues competed with existing background noise. Full article

The accurate and continuous location monitoring of athletes helps in meeting health and safety requirements and supporting the infotainment needs of large marathon events with thousands of participants. Currently, the tracking of individuals and groups of athletes at mass sports events is only possible to a limited extent, due to the weight, size, and cost constraints of the necessary devices. At marathon events, the usual infrastructure for timekeeping is Radio Frequency Identification (RFID) technology, which allows only precise tracking at huge intervals, with heuristic and interpolative algorithms to estimate runner positions in between the measuring points. Setting up RFID tracking stations on site is also material- and labor-intensive. We instead propose a continuous, real-time tracking solution, relying on Long-Range Wide-Area Network (LoRaWAN) GPS trackers. Due to the large geographical area and urban space in which marathon events take place, the positioning of static gateways cannot provide complete and continuous coverage. This research article presents an implementation with multiple LoRa trackers and mobile LoRa gateways installed on vehicle escorts to assess coverage quality. The tracking data collected by a receiving LoRaWAN Network Server (LNS) are stored in a database. Three experiments were conducted at three different official running events: a 10 km race, a half marathon, and a marathon. The backdrop for the 42.195 km event was the official Vienna City Marathon 2024 with more than 35,000 participants. The experimental results under these realistic conditions show the reception quality of this approach; e.g., during the marathon, the received packets from LoRa gateways were at an average distance of about 136 m (σ 157 m) from the tracker with a median update rate of 31 s across all trackers, using DR3/SF9. At greater distances, the quality decreased, although some outliers were received up to a distance of two kilometers. A possible prospect is that the low-power wide-area network (LPWAN) may repeat the history of RFID by entering the mass sports market from the industry domain. Full article