Search Results (310)

Autonomous cleaning robots operating in semi-structured dynamic environments must execute task-oriented motions while safely interacting with surrounding agents. These agents include pedestrians, vehicles, and other robots. In such environments (e.g., interaction-rich campus environments), reliable self-decision-making requires anticipating the future motions of surrounding agents rather than relying solely on reactive obstacle avoidance. This paper presents a trajectory prediction-enabled self-decision-making framework for autonomous cleaning robots in campus environments. A learning-based multi-agent trajectory prediction model is trained offline using public benchmarks and real-world operational data to capture typical interaction patterns in corridor-following, edge-cleaning, and intersection scenarios. The predicted trajectories are then incorporated as forward-looking priors into the robot’s online decision-making and planning process, enabling prediction-aware yielding, detouring, and task continuation decisions. The proposed framework is evaluated using real-world data-driven scenario reconstruction on a high-fidelity simulation platform that incorporates realistic vehicle dynamics and heterogeneous traffic participants. This evaluation focuses on short-horizon prediction performance and its impact on downstream decision-making stability. The results show that integrating trajectory prediction into the decision-making loop leads to more stable motion behavior and fewer abrupt adjustments in interaction scenarios. Under short-term prediction horizons, the evaluation results show that the proposed model achieves ADERate and FDERate exceeding 90% under predefined error thresholds, while lane-change prediction accuracy remains around 79%. In addition, the robot maintains stable speed tracking with only minor fluctuations under medium-density traffic conditions. Full article

In indoor crowd navigation, quickly and accurately acquiring the kinematic data of pedestrians within a robot’s field of view is a crucial factor determining success. Existing indoor pedestrian tracking methods have limitations in accuracy and real-time performance. To address these issues, a lightweight pedestrian tracking method based on an improved YOLOv10s and DeepSORT is proposed. In the detection stage, a CPNGhostNetV2 module incorporating Ghost Convolution and attention mechanisms is first designed to replace the original C2f module in YOLOv10s. This achieves lightweight while effectively preserving global feature information. Secondly, the GSConv module is introduced to further reduce computational load and model parameters. Finally, the Focal Loss function is introduced to enhance the detection capability of the YOLOv10s model in dense scenes. In the tracking stage, a novel trajectory management mechanism is proposed to reduce the ID-switching problem under occlusion conditions. The experimental results show that the improved YOLOv10s reduces computational complexity by 33.9% and parameters by 17.4% compared to the original model. It also improves mAP@50 by 0.6%. The improved DeepSORT algorithm achieves a 7.0% increase in MOTA, a 1.4% increase in MOTP, and a 24.8% reduction in ID-switch counts compared to the original YOLOv10-DeepSORT. It outperforms traditional algorithms in terms of accuracy, real-time performance, and computational efficiency, demonstrating promising application prospects. Full article

Near-miss events, defined as hazardous traffic interactions without actual collisions, provide valuable indicators for proactive traffic safety assessment. However, existing studies mainly focus on collision detection or object-level perception, while near-miss interactions and their severity remain insufficiently explored. This study proposes a video-based framework for real-time near-miss detection and risk evaluation in complex urban intersections. The framework integrates an enhanced YOLOv11 detector with a small-object detection head, BoT-SORT multi-object tracking, and bird’s-eye-view (BEV) transformation to accurately extract trajectories and motion features of heterogeneous road users. A Near-Miss Risk Index (RI) is developed by jointly considering spatial proximity, time-to-collision, and motion intensity to quantify near-miss severity levels. Experimental results on real-world CCTV data demonstrate that the proposed method effectively identifies high-risk interactions among vehicles, motorcycles, and pedestrians, providing interpretable severity assessment and supporting proactive traffic safety analysis for intelligent transportation systems. Full article

This study introduces a novel virtual robotic approach for automated characterization of pedestrian network accessibility from semantically segmented 3D LiDAR point clouds. With approximately 8 million Canadians living with disabilities, scalable accessibility assessment methods are critical. The proposed methodology integrates a Tangent Bug navigation algorithm—extended from 2D to 3D point cloud environments—with a triangular virtual robot grounded in ADA and IBC accessibility standards. The robot navigates classified point cloud data to simultaneously extract related parameters per step including those related to the accessibility assessment, including running slope, cross-slope, path width, surface type, and step height, aligned with the Measure of Environmental Accessibility (MEA) framework. Unlike existing approaches, the method characterizes not only formal sidewalk segments but also the critical transitional linkages between building entrances and the pedestrian network. Rather than evaluating features against fixed binary thresholds, it records continuous raw measurements enabling personalized accessibility assessment tailored to individual user profiles. Quantitative validation demonstrates high accuracy for path width (NRMSE = 2.71%) and reliable slope tracking. The proposed approach is faster, more cost-effective, and more comprehensive than traditional manual methods, and its segment-independent architecture makes it well-suited for future city-scale deployment. Full article

Real-time, precise monitoring of the number and distribution of indoor personnel is crucial for building safety management, operational optimization, and personnel scheduling. However, narrow entrances and high-density passageways often lead to missed detections, false positives, and tracking failures in pedestrian detection, thereby reducing cross-line counting accuracy. Additionally, edge devices deployed in practical scenarios frequently process multiple video streams simultaneously, resulting in computational resource constraints. To address these challenges, this paper proposes a lightweight, enhanced multi-object pedestrian tracking and counting method tailored for indoor scenarios by optimizing deep learning models. Firstly, modular optimizations are applied to the YOLOv8n model to construct a more lightweight detector, RL_YOLOv8, reducing computational overhead while maintaining accuracy. Secondly, correlated pedestrian auxiliary prediction and pedestrian position change constraints are employed to mitigate ID switching, tracking interruptions, and trajectory jumps in dense scenes. Finally, a buffer zone auxiliary counting strategy is designed to further reduce missed detections of pedestrians crossing lines. Experimental results demonstrate that compared to the original detection-and-tracking-based line-crossing counting method, the improved approach effectively enhances counting accuracy and real-time performance, better meeting the requirements of practical intelligent security and crowd monitoring systems. Full article

As population aging accelerates, age-related declines in visual sensitivity and attentional control make older drivers more vulnerable to suboptimal in-vehicle interface designs. Head-up displays (HUDs) are intended to reduce gaze shifts by overlaying information within the forward field of view, yet empirical evidence remains limited on how specific HUD presentation strategies reshape older drivers’ visual attention allocation. Grounded in theories of visual attention and cognitive load, this study systematically investigates three design variables that are increasingly common in contemporary HUDs (including AR-HUDs): (1) dynamic versus static navigation cues, (2) pedestrian warning strategies under different lighting conditions, and (3) the spatial placement of high-priority information. We first conducted a formative user study to define variables and operationalizations, and then carried out three within-subject driving-simulator experiments using controlled HUD stimuli and eye tracking. Objective gaze measures (e.g., fixation count, total fixation duration, and time to first fixation) were combined with subjective preference ratings to characterize attentional capture, search efficiency, and potential attentional costs. Findings reveal a robust trade-off: continuously changing navigation cues enhance attentional capture but can also increase attentional “stickiness,” unnecessarily consuming older drivers’ limited attentional resources. In pedestrian hazard tasks, real-time overlay warnings that were spatially aligned with the hazard significantly improved visual localization under low-light conditions, outperforming early warnings and multi-stage strategies. Across tasks and layout conditions, the central HUD region showed a stable attentional advantage—placing critical information centrally elicited greater visual attention and stronger subjective preference. These results provide mechanistic evidence for how HUD parameters modulate older drivers’ attention and yield actionable implications for prioritization, temporal pacing of dynamic navigation cues, and a “center-first” layout strategy to guide age-friendly HUD design. Full article

In complex driving environments, drivers must continuously detect and respond to critical visual information such as traffic signs and pedestrians. However, important targets may sometimes be overlooked due to high cognitive load during driving. Therefore, visual attention prompt methods have been proposed to guide drivers’ gaze toward relevant targets. A visual attention prompt method is a visual cue presented in a key area in a user’s field of view to draw his/her visual attention. This study evaluates the short-term performance of five visual attention prompt methods (Point, Arrow, Blur, Dusk, and ModAF) in a driving simulator and compares their performance between novice and proficient drivers. Eye-tracking data and multiple analyses are used to examine whether the influence of these methods could be maintained after they are disabled and to clarify drivers’ response patterns across methods in consideration with their driving proficiency. The results indicate that visual attention prompt methods could induce a short-term transfer effect, as drivers still tend to fixate on target traffic signs earlier after the methods are disabled, and the elapsed-time analysis estimates that this effect lasts about 84.35 s. Overall, the Point, Arrow, and Dusk methods show relatively stronger performance with significant reductions in the elapsed time to fixate on the traffic sign. The clustering analysis further shows that drivers’ response patterns are not uniform, with two clusters for novice drivers and three clusters for proficient drivers. The results suggest that most novice drivers tend to benefit from explicit non-directional visual cues that enhance target salience, such as the Point method, whereas proficient drivers are more likely to benefit from explicit directional visual cues that provide clear directional guidance, such as the Arrow method. These findings suggest that visual attention prompt methods may be useful for developing driver training strategies tailored to different levels of driving proficiency, helping drivers maintain more effective visual attention allocation during driving and potentially contributing to improved driving safety. Full article

Precise modeling and simulation of pedestrian flow are crucial for public space safety design and emergency management. This study proposes an interdisciplinary method integrating computer vision and cellular automata (CA). First, unidirectional pedestrian flow video data with different densities were collected from an overpass scene via controlled experiments. High-precision pedestrian trajectory extraction and tracking were achieved using the YOLO 11 model and DeepSORT algorithm, with image distortion corrected by perspective transformation. For the first time, the probability distribution of pedestrian turning angles derived from trajectory analysis was converted into data-driven transition probabilities for the Moore neighborhood in the CA model. An improved evacuation model was then constructed, comprehensively considering real-data-based transition probabilities, speed–density distribution, panic coefficient, individual life value, and hazard source dynamics. Multi-scenario simulations show that moderate panic may shorten evacuation time, while excessive panic causes behavioral disorders; group movement is constrained by the slowest individual, and increased hazard source speed reduces the proportion of safe pedestrians. This study provides new insights and methodological support for refined pedestrian evacuation simulation and safety management. Full article

Driven by advancements in artificial intelligence technology, pedestrian trajectory prediction is shifting from traditional machine learning methods toward autonomous decision-making frameworks based on neural networks. However, the spatiotemporal uncertainty of pedestrian movement results in low accuracy of existing prediction models. To address this issue, we propose a multi-source perception fusion system based on INS-GNSS wearable devices. By integrating high-precision inertial measurement units (IMUs) and multi-mode global navigation satellite systems (GNSS), we enhance localization and prediction accuracy. For localization, we introduce a Gait Adaptive UKF (Gait-AUKF) that identifies pedestrian gait patterns and motion states by fusing multi-sensor data. An adaptive algorithm effectively suppresses trajectory drift and improves tracking accuracy. For trajectory prediction, we propose a pedestrian trajectory prediction framework based on a multi-source fusion attention mechanism. A GRU encoder extracts pedestrian trajectory features from historical motion data. An attention mechanism assigns varying weights to trajectory features across different scales. An LSTM decoder and A* path planning algorithm constrain spatiotemporal paths to generate future pedestrian trajectories. Experimental results demonstrate that compared to UKF and AKF, the Gait-AUKF reduces eastward error by 30%, northward error by 26.27%, and vertical error by 49.08%. The complete prediction framework achieves a 68.54% reduction in average position error (APE) and a 70.42% reduction in direction error (DE) compared to LSTM and Transformer models. Ablation experiments demonstrate that the integrated Gait-AUKF algorithm and A* path planning algorithm enhance model decision performance. After incorporating these algorithms, the model’s ADE decreased by 68.49% and FDE by 71.86%. Full article

This paper presents a real-time pedestrian situation detection framework for autonomous mobility platforms. The proposed approach extracts pedestrians from images acquired by a camera mounted on an autonomous mobility system, classifies their postures, tracks their trajectories, and subsequently detects pedestrian situations. A convolutional neural network (CNN) is employed for pedestrian detection and posture classification, where the YOLOv12 model is fine-tuned via transfer learning for this purpose. To improve detection and classification performance, a region of interest (ROI) is defined using camera calibration data, enabling robust detection of small-scale pedestrians over long distances. Using a custom-labeled dataset, the proposed method achieves a precision of 96.6% and a recall of 97.0% for pedestrian detection and posture classification. The detected pedestrians are tracked using the DeepSORT algorithm, and their situations are inferred through a rule-based analysis module. Experimental results demonstrate that the proposed system operates at an execution speed of 58.11 ms per frame, corresponding to 17.2 fps, thereby satisfying the real-time requirements for autonomous mobility applications. These results confirm that the proposed framework enables reliable real-time pedestrian extraction and situation awareness in real-world autonomous mobility environments. Full article

With the advancement of Light Detection and Ranging (LiDAR) technology and computer science, LiDAR–Inertial Simultaneous Localization and Mapping (SLAM) has become essential in autonomous driving, robotic navigation, and 3D reconstruction. However, dynamic objects such as pedestrians and vehicles, with complex terrain conditions, pose serious challenges to existing SLAM systems. These factors introduce artifacts into the acquired point clouds and result in significant vertical drift in SLAM trajectories. To address these challenges, this study focuses on controlling vertical drift errors in LiDAR–Inertial SLAM systems operating in dynamic environments. The research focuses on three key aspects: ground point segmentation, dynamic artifact removal, and vertical drift optimization. In order to improve the robustness of ground point segmentation operations, this study proposes a method based on a concentric sector model. This method divides point clouds into concentric regions and fits flat surfaces within each region to accurately extract ground points. To mitigate the impact of dynamic objects on map quality, this study proposes a removal algorithm that combines multi-frame residual analysis with curvature-based filtering. Specifically, the algorithm tracks residual changes in non-ground points across consecutive frames to detect inconsistencies caused by motion, while curvature features are used to further distinguish moving objects from static structures. This combined approach enables effective identification and removal of dynamic artifacts, resulting in a reduction in vertical drift. Full article

Traditional automated monitoring systems adopted for Intersection Traffic Control still face challenges, including high costs, maintenance difficulties, insufficient coverage, poor multimodal data integration, and limited traffic information analysis. To address these issues, the study proposes a sovereign AI-driven Smart Transportation governance approach, developing a mobile AI solution equipped with multimodal perception, task decomposition, memory, reasoning, and multi-agent collaboration capabilities. The proposed system integrates computer vision, multi-object tracking, natural language processing, Retrieval-Augmented Generation (RAG), and Large Language Models (LLMs) to construct a Pipeline-based Traffic Analysis System (PTAS). The PTAS can produce real-time statistics on pedestrian and vehicle flows at intersections, incorporating potential risk factors such as traffic accidents, construction activities, and weather conditions for multimodal data fusion analysis, thereby providing forward-looking traffic insights. Experimental results demonstrate that the enhanced DuCRG-YOLOv11n pre-trained model, equipped with our proposed new activation function $\beta silu$, can accurately identify various vehicle types in object detection, achieving a frame rate of 68.25 FPS and a precision of 91.4%. Combined with ByteTrack, it can track over 90% of vehicles in medium- to low-density traffic scenarios, obtaining a 0.719 in MOTA and a 0.08735 in MOTP. In traffic flow analysis, the RAG of Vertex AI, combined with Claude Sonnet 4 LLMs, provides a more comprehensive view, precisely interpreting the causes of peak-hour congestion and effectively compensating for missing data through contextual explanations. The proposed method can enhance the efficiency of urban traffic regulation and optimizes decision support in intelligent transportation systems. Full article

To address the limitations of conventional social force models in simulating high-density pedestrian crowds, this study proposes an enhanced model that incorporates visual perception constraints, group-type labeling, and collective avoidance mechanisms. Pedestrian trajectories were extracted from a bidirectional commercial street scenario using OpenCV, with YOLOv8 and DeepSORT employed for multiple object tracking. Analysis of pedestrian grouping patterns revealed that 52% of pedestrians walked in pairs, with distinct avoidance behaviors observed. The improved model integrates three key mechanisms: a restricted 120° forward visual field, group-type classification based on social relationships, and an exponentially formulated inter-group repulsive force. Simulation results in MATLAB R2023b demonstrate that the proposed model outperforms conventional approaches in multiple aspects: speed distribution (error < 8%); spatial density overlap (>85%); trajectory similarity (reduction of 32% in Dynamic Time Warping distance); and avoidance behavior accuracy (82% simulated vs. 85% measured). This model serves as a quantitative simulation tool and decision-making basis for the planning of pedestrian spaces, crowd organization management, and the optimization of emergency evacuation schemes in high-density pedestrian areas such as commercial streets and subway stations. Consequently, it contributes to enhancing pedestrian mobility efficiency and public safety, thereby supporting the development of a sustainable urban slow transportation system. Full article

Interest in the adoption of autonomous vehicles (AVs) continues to grow. It is essential to design new software architectures that meet stringent real-time, safety, and scalability requirements while integrating heterogeneous hardware and software solutions from different vendors and developers. This paper presents a lightweight, modular, and scalable architecture grounded in Service-Oriented Architecture (SOA) principles and implemented in ROS 2 (Robot Operating System 2). The proposed design leverages ROS 2’s Data Distribution System-based Quality-of-Service model to provide reliable communication, structured lifecycle management, and fault containment across distributed compute nodes. The architecture is organized into Perception, Planning, and Control layers with decoupled sensor access paths to satisfy heterogeneous frequency and hardware constraints. The decision-making core follows an event-driven policy that prioritizes fresh updates without enforcing global synchronization, applying zero-order hold where inputs are not refreshed. The architecture was validated on a 1:10-scale autonomous vehicle operating on a city-like track. The test environment covered canonical urban scenarios (lane-keeping, obstacle avoidance, traffic-sign recognition, intersections, overtaking, parking, and pedestrian interaction), with absolute positioning provided by an indoor GPS (Global Positioning System) localization setup. This work shows that the end-to-end Perception–Planning pipeline consistently met worst-case deadlines, yielding deterministic behaviour even under stress. The proposed architecture can be deemed compliant with real-time application standards for our use case on the 1:10 test vehicle, providing a robust foundation for deployment and further refinement. Full article

Amid rapid advancements in artificial intelligence, the detection of abnormal human behaviors in complex traffic environments has garnered significant attention. However, detection errors frequently occur due to interference from complex backgrounds, small targets, and other factors. Therefore, this paper proposes a research methodology that integrates the anomaly detection YOLO-SGCF algorithm with the tracking BoT-SORT-ReID algorithm. The detection module uses YOLOv8 as the baseline model, incorporating Swin Transformer to enhance global feature modeling capabilities in complex scenes. CBAM and CA attention are embedded into the Neck and backbone, respectively: CBAM enables dual-dimensional channel-spatial weighting, while CA precisely captures object location features by encoding coordinate information. The Neck layer incorporates GSConv convolutional modules to reduce computational load while expanding feature receptive fields. The loss function is replaced with Focal-EIoU to address sample imbalance issues and precisely optimize bounding box regression. For tracking, to enhance long-term tracking stability, ReID feature distances are incorporated during the BoT-SORT data association phase. This integrates behavioral category information from YOLO-SGCF, enabling the identification and tracking of abnormal pedestrian behaviors in complex environments. Evaluations on our self-built dataset (covering four abnormal behaviors: Climb, Fall, Fight, Phone) show mAP@50%, precision, and recall reaching 92.2%, 90.75%, and 86.57% respectively—improvements of 3.4%, 4.4%, and 6% over the original model—while maintaining an inference speed of 328.49 FPS. Additionally, generalization testing on the UCSD Ped1 dataset (covering six abnormal behaviors: Biker, Skater, Car, Wheelchair, Lawn, Runner) yielded an mAP score of 92.7%, representing a 1.5% improvement over the original model and outperforming existing mainstream models. Furthermore, the tracking algorithm achieved an MOTA of 90.8% and an MOTP of 92.6%, with a 47.6% reduction in IDS, demonstrating superior tracking performance compared to existing mainstream algorithms. Full article