Search Results (29)

With the development of constellations and satellite chains, trajectory planning for constellation satellites has gradually attracted research attention. To address the infeasibility issues of trapezoidal sequential convex programming (T-SCP) in this strong non-convexity problem, this paper proposes high-precision adaptive SCP based on the Hermite Simpson method for Low-Earth-Orbit (LEO) constellation aggregation and aimed at enhanced local reconnaissance with practical constraints, including collision avoidance. The method introduces an adaptive trust region and slack variables, which avoid convergence failures due to strong nonlinearity or large state variations over long distances and accelerate the convergence rate. Furthermore, a third-order accurate Hermite Simpson method is adopted along with a adaptive iterative collision warning mechanism, improving accuracy while reducing computational cost. Simulation results demonstrate that the proposed method improves computational efficiency by 6% and 21% compared with the T-SCP and pseudospectral method, respectively. When the number of discrete nodes is 50, it achieves position errors of 284.7 m and velocity errors of 0.33 m/s, and these errors are far lower than those of T-SCP. Monte Carlo simulations across 10,000 scenarios validate the robustness of the proposed method with a 91% overall success rate and 100% for LEO. Full article

Instance segmentation accurately extracts the position and outline of ships, serving as the foundation for maritime safety tasks such as multi-object tracking, sensor fusion, and collision warning. This study focuses on single-frame segmentation and aims to address the challenge of multi-scale ship occlusion in congested ports, providing reliable observational data through high-precision recognition to ensure navigation safety. Existing methods suffer from performance degradation in complex maritime environments due to factors such as multi-scale distribution, low resolution of distant targets, and frequent occlusions. Among these, ship occlusion is particularly problematic as it leads to feature confusion between adjacent instances and inaccurate boundary segmentation. To address these challenges, we propose a novel instance segmentation algorithm (MAF-ORDNet) based on Multi-branch Adaptive Feature Fusion and Occluded Region Decoupling. Firstly, a multi-branch adaptive feature fusion module is designed to capture contextual information through different receptive fields and dynamically fuse multi-scale features, thereby restoring occluded semantics and enhancing robustness. Secondly, an occlusion region decoupling module is constructed to accurately localize occluded regions and enhance contour responses via adaptive sampling, achieving refined boundary processing. In addition, we constructed and annotated the Occlusion ShipSeg dataset, which contains 1969 real occlusion images, 2150 simulated occlusion images, and 1132 images under adverse weather conditions, totaling 17,352 fine instance annotations. Experimental results show that, compared with PatchDCT, YOLOv11s, and Mask2Former, our method improves AP by 2.7%, 3.2%, and 2.4%, respectively, while maintaining a comparable inference speed to YOLOv8s. These results confirm that MAF-ORDNet achieves a favorable balance between accuracy and efficiency in multi-scale occluded ship segmentation tasks. Full article

The proactive identification of emerging collision risks is pivotal for maritime traffic safety, particularly in congested hub ports where multi-ship encounters exhibit complex spatiotemporal dependencies. Conventional risk assessment methods, predominantly predicated on instantaneous geometric indicators, often fall short in capturing the systemic evolution of risk. To address these limitations, this study proposes an Improved Spatio-Temporal Graph Convolutional Network (IST-GCN) framework for the short-term forecasting of ship collision risk. The framework models maritime traffic as a rule-integrated dynamic interaction graph, where edge weights are adaptively modulated by navigational rules and the Collision Risk Index (CRI). By leveraging historical observation windows, the model forecasts the maximum collective risk level over a subsequent prediction horizon, categorizing traffic scenes into three ordinal levels: Low, Medium, and High. A comprehensive case study utilizing real-world Automatic Identification System (AIS) data from the core waters of Ningbo–Zhoushan Port demonstrates the efficacy of the proposed approach. The IST-GCN achieves a superior prediction Accuracy of 92.4% and an F1-score of 0.91, significantly outperforming representative baselines including Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), and standard ST-GCN. Notably, by explicitly encoding COLREGs-based interaction logic, the framework reduces the False Alarm Rate (FAR) to 8.5% in complex crossing and merging scenarios. These findings indicate that the IST-GCN serves as an interpretable, reliable, and early-warning decision-support tool for intelligent maritime supervision and modern Vessel Traffic Services (VTS). Full article

With the increasing adoption of regenerative braking technology in electric vehicles (EVs), one-pedal driving (OPD) mode has become a prevalent feature. While OPD offers technical advantages in energy efficiency, its implications for driver behavior and traffic safety remain unclear. To address the lack of human factors research in this domain, this study utilized a driving simulator to systematically compare driving performance between OPD and two-pedal driving (TPD) modes. Twenty-six participants engaged in car-following tasks under varying traffic densities (uncongested vs. congested) and cognitive load levels (normal vs. 1-back). Driving performance and safety were quantified using the absolute speed difference, distance headway, braking frequency, and Time-to-Collision at brake onset (TTC_brake). The results revealed a significant trade-off: while OPD simplified operation, it led to compromised driving performance compared to TPD in specific contexts. Specifically, OPD resulted in larger speed variations and reduced safety margins during the approach stage. Conversely, under high cognitive load, OPD demonstrated a protective effect by mitigating performance degradation. These findings suggest that while OPD can benefit drivers under mental pressure, its deployment requires adaptive safety strategies, such as the integration of Headway Monitoring Warning (HMW) and Forward Collision Warning (FCW), to compensate for performance deficits in complex traffic environments. Full article

Interchange merging areas are critical safety hotspots in urban road networks, where complex vehicle interactions challenge traffic safety and efficiency. Improving safety performance at these locations is essential for developing sustainable, resilient, and intelligent urban transportation systems. To overcome the limitations of single-driver simulators, this study developed a multi-driver simulation platform based on Unity3D (Version 2022.3.1f1c1), enabling real-time interaction among multiple human drivers. High-resolution trajectory data were collected from 231 valid interaction events. An eight-direction relative position model was employed to classify behaviors into four patterns: longitudinal, lateral, front cut-in, and rear cut-in. Risk was quantified using time-exposed and time-integrated Anticipated Collision Time metrics, with events subsequently clustered into low (n = 138), medium (n = 67), and high-risk (n = 26) categories. An ordered logit regression model identified key risk factors. The results quantitatively demonstrate that interaction risk escalates significantly with abrupt speed changes (OR = 16.22) and late-stage occurrence of speed extremes (OR = 6.76) in the interacting vehicle, as well as large initial speed differences (OR = 2.45). Conversely, stable speed regulation and adaptive acceleration by the subject vehicle proved to be potent mitigating factors. These findings provide actionable insights for the development of intelligent collision warning systems and the sustainable design of interchange infrastructure. Full article

To address critical limitations in conventional forward collision warning (FCW) systems including inadequate road condition detection accuracy, significant warning area prediction errors, and poor environmental adaptability on wet/snow-covered roads, this study develops an adaptive collision warning framework based on real-time road slippery states recognition. An enhanced ED-ResNet50 model is proposed, incorporating grouped convolutions within the backbone network and embedding ECA attention mechanisms after the second/third residual blocks alongside DDS-DA modules after the fourth block, significantly improving discriminative capability for pavement texture analysis under adverse conditions. This vision-based recognition system synchronizes with YOLOv8 for preceding vehicle detection, enabling the construction of a friction-sensitive safety distance and the time-to-collision model that dynamically calibrates warning thresholds according to instantaneous vehicle velocity and road adhesion coefficients. Real-vehicle validation demonstrates an 8.76% improvement in overall warning accuracy and 7.29% reduction in lateral and early false alarm rates compared to static-threshold systems, confirming practical efficacy for safety assurance in inclement weather. Full article

Improving road safety is a fundamental goal of Intelligent Transportation Systems (ITS). However, the complex interplay of factors in accident-prone scenarios, such as highway–rail grade crossings, poses significant challenges for conventional analysis. This paper addresses this gap by proposing and validating a novel explainable artificial intelligence (XAI) framework, which integrates Extreme Gradient Boosting (XGBoost) with Shapley Additive Explanations (SHAP), to enhance safety analysis within ITS. Applying this framework to a comprehensive dataset of highway–rail grade crossing collisions, our research moves beyond simple correlation to uncover the nonlinear relationships and interaction effects governing injury severity. The model identifies speed-related factors, driver age, and traffic exposure as primary predictors. More critically, the SHAP analysis quantitatively reveals significant synergistic risks, demonstrating that the combination of non-dry road surfaces and poor lighting conditions drastically amplifies injury severity. These findings offer granular insights for the “smart management” and development of “resilient infrastructures,” enabling targeted interventions like adaptive lighting systems and dynamic risk warnings. This study not only provides critical safety solutions for grade crossings but also showcases the power of XAI as a robust tool for “advanced analysis” across various complex transportation safety problems, ultimately contributing to the creation of safer and more reliable ITS. Full article

With the rapid development of autonomous vehicles and intelligent transportation systems, vehicle detection and distance estimation have become critical technologies for ensuring driving safety. However, real-world in-vehicle environments impose strict constraints on computational resources, making it impractical to deploy high-end GPUs. This implies that even highly accurate algorithms, if unable to run in real time on embedded platforms, cannot fully meet practical application demands. Although existing deep learning-based detection and stereo vision methods achieve state-of-the-art accuracy on public datasets, they often rely heavily on massive computational power and large-scale annotated data. Their high computational requirements and limited cross-scenario generalization capabilities restrict their feasibility in real-time vehicle-mounted applications. On the other hand, traditional algorithms such as Semi-Global Block Matching (SGBM) are advantageous in terms of computational efficiency and cross-scenario adaptability, but when used alone, their accuracy and robustness remain insufficient for safety-critical applications. Therefore, the motivation of this study is to develop a stereo vision-based collision warning system that achieves robustness, real-time performance, and computational efficiency. Our method is specifically designed for resource-constrained in-vehicle platforms, integrating a lightweight YOLOv8n detector with SGBM-based depth estimation. This approach enables real-time performance under limited resources, providing a more practical solution compared to conventional deep learning models and offering strong potential for real-world engineering applications. Full article

Accurate forecasting of potential collision risk in dense traffic is addressed by a framework grounded in multi-vehicle trajectory prediction. A spatio-temporal fusion architecture, STGAT-EDGRU, is proposed. A Transformer encoder learns temporal motion patterns from each vehicle’s history; a boundary-aware graph (GAT) attention network models inter-vehicle interactions; and a Gated Multimodal Unit (GMU) adaptively fuses the temporal and spatial streams. Future positions are parameterized as bivariate Gaussians and decoded by a two-layer GRU. Using probabilistic trajectory forecasts for the main vehicle and its surrounding vehicles, collision probability and collision intensity are computed at each prediction instant and integrated via a weighted scheme into a Collision Risk Index (CRI) that characterizes risk over the entire horizon. On HighD, for 3–5 s horizons, average RMSE reductions of 0.02 m, 0.12 m, and 0.26 m over a GAT-Transformer baseline are achieved. In high-risk lane-change scenarios, CRI issues warnings 0.4–0.6 s earlier and maintains a stable response across the high-risk interval. These findings substantiate improved long-horizon accuracy together with earlier and more reliable risk perception, and indicate practical utility for lane-change assistance, where CRI can trigger early deceleration or abort decisions, and for risk-aware motion planning in intelligent driving. Full article

Onboard millimeter-wave radar is one way to improve helicopter flight safety at low altitudes in difficult meteorological conditions. Low-altitude flight is associated with the risk of collision with low-visibility obstacles such as power lines. Since power lines have the property of polarization anisotropy, it is necessary to use radar polarimetry methods to increase the radar visibility of such obstacles for detection. This paper investigates the possibility of identifying low-visibility polarization-anisotropic objects, such as power lines, in polarimetric images obtained by onboard helicopter radars to warn the pilot about the danger of a collision with a low-visibility object. Consequently, the use of a polarimetric radar with a polarization modulation of the emitted signal, the two-channel polarization synchronous reception of reflected signals and Eigenvalue signal processing in real time was proposed, aiming to eliminate the dependence of reflected signals on the spatial orientation of power transmission line wires. Additionally, an optimal algorithm for adaptive polarization selection, obtained by the maximum likelihood method, was proposed to detect such objects against the background of the underlying surface. The effectiveness of the proposed algorithm was tested using computer modeling methods. Moreover, a W-band polarimetric radar system was designed for experimental studies of this concept. The developed radar system provides digital real-time signal processing and the reconstruction of composite polarimetric images. It displays information about the polarization characteristics of the objects in the scanned area and the results of their polarization selection. Therefore, the system informs the pilot about dangerous objects along the helicopter’s route. Radar experimental tests in actual environmental conditions have confirmed the proposed concept’s correctness and the proposed method and algorithm’s effectiveness. Full article

The high-precision motion state analysis of space targets has important scientific value and application potential in the fields of geodynamics, geodesy, space collision warning and avoidance, and the capture, recovery, and removal of space debris. With the increasing repetition rate of satellite laser ranging systems, the inversion and analysis of space target motion state based on high-precision and high-repetition-rate satellite laser ranging data has become a hot spot in current research. How to filter out noise and retain valid information from the high-repetition-rate, high-precision laser ranging data has become a challenge. The traditional polynomial fitting method has problems with low data accuracy and erroneous deletion of valid data when processing laser ranging data with complex patterns. To address this problem, this paper innovatively designs a new type of spline function and accordingly proposes a laser ranging data preprocessing method that can automatically adapt to the trend of data variation. The method is validated using various characteristic observation data provided by Changchun station (7237), and the results show that the novel spline method is superior to the traditional method in maintaining the integrity of data patterns and significantly improves the data accuracy. After two iterations of denoising, the RMS of the novel spline method is reduced to one-fourth of that before denoising and as low as one-eighteenth of that of the traditional method, and the accuracy is further improved with the increase of the number of iterations. This study provides a practical and reliable novel solution for the preprocessing of laser ranging data with complex patterns. Full article

To address the critical need for collision risk warning at unsignalized intersections, this study proposes an advanced predictive system combining YOLOv8 for object detection, Deep SORT for tracking, and Bi-LSTM networks for trajectory prediction. To adapt YOLOv8 for complex intersection scenarios, several architectural enhancements were incorporated. The RepLayer module replaced the original C2f module in the backbone, integrating large-kernel depthwise separable convolution to better capture contextual information in cluttered environments. The GIoU loss function was introduced to improve bounding box regression accuracy, mitigating the issues related to missed or incorrect detections due to occlusion and overlapping objects. Furthermore, a Global Attention Mechanism (GAM) was implemented in the neck network to better learn both location and semantic information, while the ReContext gradient composition feature pyramid replaced the traditional FPN, enabling more effective multi-scale object detection. Additionally, the CSPNet structure in the neck was substituted with Res-CSP, enhancing feature fusion flexibility and improving detection performance in complex traffic conditions. For tracking, the Deep SORT algorithm was optimized with enhanced appearance feature extraction, reducing the identity switches caused by occlusions and ensuring the stable tracking of vehicles, pedestrians, and non-motorized vehicles. The Bi-LSTM model was employed for trajectory prediction, capturing long-range dependencies to provide accurate forecasting of future positions. The collision risk was quantified using the predictive collision risk area (PCRA) method, categorizing risks into three levels (danger, warning, and caution) based on the predicted overlaps in trajectories. In the experimental setup, the dataset used for training the model consisted of 30,000 images annotated with bounding boxes around vehicles, pedestrians, and non-motorized vehicles. Data augmentation techniques such as Mosaic, Random_perspective, Mixup, HSV adjustments, Flipud, and Fliplr were applied to enrich the dataset and improve model robustness. In real-world testing, the system was deployed as part of the G310 highway safety project, where it achieved a mean Average Precision (mAP) of over 90% for object detection. Over a one-month period, 120 warning events involving vehicles, pedestrians, and non-motorized vehicles were recorded. Manual verification of the warnings indicated a prediction accuracy of 97%, demonstrating the system’s reliability in identifying potential collisions and issuing timely warnings. This approach represents a significant advancement in enhancing safety at unsignalized intersections in urban traffic environments. Full article

Visible Light Communication (VLC) has the potential to advance Intelligent Transportation Systems (ITS). This study explores the current advancements of VLC in ITS applications that may enhance traffic flow, road safety, and vehicular communication performance. The potential, benefits, and current research trends of VLC in ITS applications are discussed first. Then, the state-of-the-art VLC technologies including overall concept, IEEE communication protocols, hybrid VLC systems, and software-defined adaptive MIMO VLC systems, are discussed. We investigated different potential applications of VLC in ITS, such as signalized intersection and ramp metering control, collision warning and avoidance, vehicle localization and detection, and vehicle platooning using vehicle–vehicle (V2V), infrastructure–vehicle (I2V), and vehicle–everything (V2X) communications. Besides, VLC faces several challenges in ITS applications, and these concerns, e.g., environmental issues, communication range issues, standards and infrastructure integration issues, light conditions and integration issues are discussed. Finally, this paper discusses various advanced techniques to enhance VLC performance in ITS applications, such as machine learning-based channel estimation, adaptive beamforming, robust modulation schemes, and hybrid VLC integration. With this review, the authors aim to inform academics, engineers, and policymakers about the status and challenges of VLC in ITS. It is expected that, by applying VLC in ITS, mobility will be safer, more efficient, and sustainable. Full article

This paper focuses on a method of rearview camera-based blind-spot detection and a lane change assistance system for autonomous vehicles, utilizing a convolutional neural network and lane detection. In this study, we propose a method for providing real-time warnings to autonomous vehicles and drivers regarding collision risks during lane-changing maneuvers. We propose a method for lane detection to delineate the area for blind-spot detection and for measuring time to collision—both utilized to ascertain the vehicle’s location and compensate for vertical vibrations caused by vehicle movement. The lane detection method uses edge detection on an input image to extract lane markings by employing edge pairs consisting of positive and negative edges. Lanes were extracted through third-polynomial fitting of the extracted lane markings, with each lane marking being tracked using the results from the previous frame detections. Using the vanishing point where the two lanes converge, the camera calibration information is updated to compensate for the vertical vibrations caused by vehicle movement. Additionally, the proposed method utilized YOLOv9 for object detection, leveraging lane information to define the region of interest (ROI) and detect small-sized objects. The object detection achieved a precision of 90.2% and a recall of 82.8%. The detected object information was subsequently used to calculate the collision risk. A collision risk assessment was performed for various objects using a three-level collision warning system that adapts to the relative speed of obstacles. The proposed method demonstrated a performance of 11.64 fps with an execution time of 85.87 ms. It provides real-time warnings to both drivers and autonomous vehicles regarding potential collisions with detected objects. Full article

When car following is controlled by human drivers (i.e., by their behavior), the traffic system does not meet stability conditions. In order to ensure the safety and reliability of self-driving vehicles, an additional hazard warning system should be incorporated into the adaptive control system in order to prevent any possible unavoidable collisions. The time to contact is a reasonable indicator of potential collisions. This research examines systems and solutions developed in this field to determine collision times and uses various alarms in self-driving cars that prevent collisions with obstacles. In the proposed analysis, we have tried to classify the various techniques and methods, including image processing, machine learning, deep learning, sensors, and so on, based on the solutions we have investigated. Challenges, future research directions, and open problems in this important field are also highlighted in the paper. Full article