Miracle-dz/UniFAR-FTUs huggingface dataset:
CTV-Dataset: A Shared Space Drone Dataset for Cyclist-Road User Interaction Derived from Campus Experiments

To achieve the goals set by the European Green Deal, the European Commission plans to expand the available cycling infrastructure. As a result, the number of cyclists is expected to rise, raising safety concerns, specifically in shared spaces where multiple road users interact. One major dilemma that urban planners will face is assessing the safety and performance of cycling infrastructure. Microscopic cyclist models are capable of fulfilling this need. However, datasets for cyclist interactions in shared spaces are missing. In this work, we propose CTV-Dataset, a cyclist-focused top-view dataset collected from laboratory experiments using a camera-equipped drone. The scenarios have been specifically designed to fill the missing scenarios in literature: cyclists' lateral, bilateral, and crossing interactions with multiple road users, such as pedestrians, cyclists, and cars. Additionally, we performed other scenarios for cyclist free-flow movement and interactions with an obstructed line of sight. Lastly, motivated by the lack of suitable computer vision methods to extract trajectories from top-view videos, a general-purpose multi-object tracking tool was developed to be used for trajectories extraction. Our dataset comprises 1.68 hours of pure cyclists scenarios, with 593 clips. The dataset is available at (https://www.ifi-mec.tu-clausthal.de/ctv-dataset)
A New Scleractinian Coral Lobophyllia hemprichii (Family Mussidae) Reported First Time From the Marine National Park, Gulf of Kachchh, India

An extensive survey to explore the diversity and distribution of coral and associated faunal community around the Gulf of Kachchh Islands in India, yielded a new zoogeographical record of a coral species namely, Lobophyllia hemprichii from Pirotan Island, Marine National Park, Gulf of Kachchh. Literature reveals that 56 species of scleractinian corals have been recorded from the Gulf of Kachchh. Detailed description, distribution and variation in morphological characters of this species were presented in this account.
An End-to-End Autonomous Driving Pre-trained Transformer Model for Multi-Behavior-Optimal Trajectory Generation

Advanced end-to-end (E2E) autonomous driving planning methods, e.g., reinforcement learning and imitation learning, employ neural networks to generate driving trajectories. However, most of them suffer from low data efficiency, i.e, they usually require a large amount of data to obtain even a simple behavior like car-following which a rule-based method can easily achieve. To this end, this work tackles the problem by introducing a pre-training method for E2E planning, which can generate multiple initial near-optimal trajectories for further fine-tuning with specific datasets. The challenges lie in that 1) various driving behaviors may be involved in a single case, leading to different optimal trajectories; 2) abstract driving behaviors are hard to be manually defined. Our idea is to first design a multi-head transformer network structure to capture the optimal trajectories for different driving behaviors. The training trajectory dataset is formed by an offline trajectory optimizer with different convex corridors which grow from sampled trajectories and imply various driving behaviors. In this way, the model can be pre-trained with various behavior-optimal trajectories, which can cover the whole driving space without manually defined behaviors. The results show that the pre-trained model can generate multiple trajectories for different behaviors without collected human demonstrations. This work can accelerate the future E2E model fine-tuning, with an acceptable initial baseline performance.
Applying E-Payment for Improving MSME’s Revenue in Jabodetabek: An Extended TAM Analysis

The objective of this research is to explore the variables that influence the usage of E-Payment in Jabodetabek region’s MSMEs as well as how it may influence the revenue of the business, using the Extended Technology Acceptance Model. The quantitative method is use to collect data and information, where primary data will be obtained through Online Questioners and interviews with MSMEs owners and employees-in-charge, 188 responses were processed using Smart PLS 4. The findings show that applying E-Payment in MSMEs has a positive and significant impact towards increasing their revenue. However, many respondents reported problems regarding a delay for their revenue reimbursement and some connectivity issues. Thus, not only making it hard to manage their cash flow, but also made some MSMEs not interested in continuing to use E-Payment. This study gave insights to EPayment vendors to improve their platforms so more MSMEs will apply this payment method for their business and help the Indonesian government to fulfill their digital transformation for transactions in the country.
Predicting Customer Trust and Behavioral Intention to Invest Cryptocurrency

Cryptocurrency continues to be accepted globally, but many individuals (investors) still hesitate to use it (invest). Therefore, this study examines the predictive model of investors’ behavioral intentions to invest in Cryptocurrency by considering the following factors: experience, trust, social influence, and attitudes. Questionnaires were distributed online to the Cryptocurrency community in DKI Jakarta. Questionnaire data were analyzed using multivariate statistics: structural equation modeling based on variance (PLS-SEM). The study’s results prove that attitude, perceived trust, and social influence positively and significantly affect behavioral intention. Furthermore, social influence and perceived experience positively and significantly affect perceived trust. Finally, perceived trust has a positive and significant effect on attitude. This finding contributes to investment behavior by examining the determinants of adopting blockchain technology in the form of Cryptocurrency.
Application of the Vision Transformer and Mask R-CNN Joint Algorithm to Assist Forest Decisions

With the continuous iterative development of remote sensing technology and deep learning algorithms, it provides strong support for the rational development, management, and protection of forest resources. This article proposes a Vision Transformer and Mask R-CNN joint algorithm architecture that applies NEON high-resolution remote sensing image data to achieve forest type classification and recognition, as well as single tree crown area instance segmentation. Research has found that the ViT algorithm performs well in large-scale data, but it relies on reasonable parameter settings, and the ViT model pre-trained through a large dataset outperforms the vast majority of other models at present. Mask R-CNN relies on the accuracy of data annotation, and models trained with a small amount of data can also achieve good training results.
Robust and Precise Sidewalk Detection with Ensemble Learning: Enhancing Road Safety and Facilitating Curb Space Management

The study highlights a robust ensemble model's effectiveness for accurate sidewalk detection, vital for both road safety and efficient curb space management. To evaluate the proposed ensemble model, three distinct datasets were utilized: Cityscapes, Ade20k, and the Boston Dataset. The results demonstrated the superiority of the ensemble model over its individual components, as manifested by mIOU scores of 93.1%, 90.3%, and 90.6% on the Cityscapes, Ade20k, and Boston datasets respectively, in optimal conditions. Under exposure to various noise types, such as Salt-and-Pepper and Speckle, across low to high intensities, the model revealed a steady, controlled decline in performance. This stands in contrast to the rapid drop experienced by individual models. Overall, the model demonstrated robustness and dependability. The ensemble model's resilience and dependability make a strong case for its application in enhancing road safety through reliable sidewalk detection and efficient curb space management.
A Study on Behavioral Intention to Use in Cryptocurrency Applications among Generation Z using UTAUT Model

The study aims to examine the factors influencing the behavioral intention to use applications among Generation Z using the UTAUT Model. UTAUT model analyzes consumers’ adoption or usage of technology. A quantitative methodology has been adopted. A survey was conducted by distributing a questionnaire to 184 respondents. The data collected was analyzed using SEM-PUS. The findings show that the Behavioral Intention to use cryptocurrency applications among Gen Z is positively and significantly influenced by Trust, Performance Expectancy, Social Influence, and Facilitating Conditions. While the other variable namely Perceived Security and Behavioral Intention failed to show significant results.
A Supervised Learning Approach for 4D Air Traffic Conflict Prediction under Trajectory Uncertainty

This paper presents a Supervised Learning approach for the problem of air traffic conflict prediction in 4-dimensional space (3-dimensional space and time) under trajectory uncertainties, resulting in non-nominal conflict points. Decision support systems for conflict prediction offer short-term conflict alerts, triggering alarms within a two-four-minute window before loss of separation (LOS), while medium-term conflicts are flagged eight to twelve minutes prior to LOS. However, the underlying models rely on flight plans and extrapolated short-term trajectory prediction. Such models lack the capabilities of predicting emergent conflicts and new conflict birth points resulting from track deviation due to non-nominal events such as weather. These deficiencies manifest themselves in the form of misdetection in the event of non-nominal conflicts. With the goal to build better tools for conflict prediction, the present study models trajectory uncertainty in the form of weather avoidance and aircraft intent during the generation of conflict scenarios. The scenarios were then simulated in BlueSky Open Air Traffic Simulator and the resulting conflict trajectories were used as inputs for supervised machine learning. The present study also includes new features, via the introduction of the Jacobian matrix for space and time, for machine learning model training as opposed to the regular features used in the past. It is demonstrated that features with rate of change are more significant in identifying conflict as opposed to classical features. The results also demonstrated significant improvement in conflict prediction (with and without trajectory uncertainty) for a two-to-twelve-minute window, as compared to the state-of-the-art conflict detection algorithms.
Classification of Driving Style in Relation to Risk Level Based on G-G Diagram

This paper proposes a novel method to classify drivers' driving styles based on G-G diagrams that display the lateral and longitudinal accelerations on the x- and y-axes. A data-based safe driving area was defined to distinguish risky acceleration points from safe acceleration points. The Jensen–Shannon divergence was used to measure the similarity between the distributions of data points outside the safe driving area. A hierarchical clustering algorithm was used to classify the drivers into two groups based on similarity: non-aggressive and aggressive groups. To verify the proposed method, we used the naturalistic driving data of 71 drivers. The results indicated that the proposed method could help understand the risk level of drivers and individual differences in their driving styles. Drivers in the aggressive group exhibited diverse types of risky driving styles, and the percentage of points outside the safe driving area was much higher than that for the low-risk group. The proposed method can be applied in driving performance feedback provision, fleet management, and usage-based insurance.
A Critical Scenario Filter to Accelerate Testing for Automated Vehicles

Automated Vehicle (AV) safety is a matter of great importance and has garnered global attention. To ensure the safety of AVs, extensive testing and evaluation of AV functions across a wide range of scenarios are necessary. However, conducting such tests is time-consuming. In order to streamline the testing process, scenario filters have been developed to identify and prioritize safety-critical scenarios while excluding ordinary ones. Nevertheless, the existing scenario filters do not offer sufficient coverage of critical scenarios. Hence, this paper introduces a scenario filter that aims to achieve high coverage of critical scenarios. The proposed filter has been subjected to experimental evaluation, and the results validate its effectiveness in improving the coverage of critical scenarios. Specifically, the proposed filter demonstrates an enhanced coverage rate of up to 70 percent.
Pre-Training For mmWave Radar Object Detection Through Masked Image Modeling

Millimeter-wave (mmWave) radar object detection (ROD) is an indispensable technique in autonomous driving technology because of its ability to maintain stability in complex environments. Deep learning (DL) techniques have made promising achievements in radar target detection techniques, but these techniques usually require large labeled datasets. However, the labeling of millimeter-wave radar datasets is more complex. In this paper, we introduce a novel pre-training method based on mask image modeling (MIM) in the ROD task, aiming to address the dataset limitations in the development of ROD techniques. The method performs spatial and temporal masking operations on unlabeled millimeter-wave radar images, which can acquire more spatial and temporal features simultaneously in the pre-training stage. Furthermore, inspired by T-RODNet, this paper presents a lightweight SS-RODNet network for testing the effectiveness of the proposed MIM method. The model aims to fully integrate the advantages of extracting local and global features. Numerous ablation experiments prove that the proposed MIM method is effective, and SS-RODNet achieves second place on the CRUW datasets.
A Review of Intelligent Infrastructure Surveillance to Support Safe Autonomy in Smart-Railways

Since railways are considered among the most critical transportation infrastructures, significant research exists on the use of modern technology for their safety and security. In order to enable full autonomy of railway operations, it is not only important to guarantee safety from the control system perspective, but also to ensure that external threats of both intentional and natural origin are detected automatically. This literature review highlights the potential of Artificial Intelligence (AI) in physical infrastructure security and surveillance of autonomous railway operations. We survey AI applications to identify unauthorized access to restricted areas, as well as intelligent monitoring against natural disasters. We also discuss the use of AI to improve situational awareness, in order to analyze data from various sources to prevent incidents, provide real-time information to decision-makers, and support emergency response. Finally, we highlight the challenges to overcome, including data quality, privacy concerns, regulations, and other technical limitations.
Data-Driven Methods for Travel Time Estimation: A Survey

Travel time estimation is a crucial component of intelligent transportation systems, affecting various applications such as navigation, ride-hailing, and route planning. Traditional methods for travel time estimation rely on subjective judgments, limited data sources, and straightforward modeling techniques. Owing to recent advances in data mining and machine learning, numerous data-driven methods are adopted to address the problem that occurred in traditional schemes, which demonstrate exceptional performance. In this paper, we present a comprehensive survey of data-driven methods for travel time estimation, encompassing application scenarios, spatial-temporal modeling approaches, and data representation learning techniques. To support and promote further research in this field, we provide a valuable list of open data sources and source codes, offering researchers a solid foundation for their future endeavors. Furthermore, this survey discusses emerging trends and key challenges faced by the research community, such as the integration of real-time data streams and the use of uncertainty estimation. We also explore the potential impact of these advancements on transportation systems, highlighting opportunities for improvement and innovation. To the best of our knowledge, this work is among the first to offer a comprehensive, in-depth review of data-driven methods for travel time estimation, providing researchers and practitioners with a valuable reference in the field.
Credit Card Fraud Detection Using Adversarial Learning

In recent years, with the proliferation of the internet and e-commerce, the user base for credit cards has witnessed a continuous surge. However, the presence of credit card fraud has resulted in immeasurable losses for users, merchants, and financial institutions. Contemporary practices in fraud detection primarily rely on classification methods such as CNN, LSTM, and DNN. Nonetheless, these approaches predominantly consider the utilization of original features and exhibit suboptimal performance when confronted with imbalanced datasets. Moreover, they necessitate substantial volumes of annotated data for effective training. This paper introduces an unsupervised anomaly detection network that leverages dual adversarial learning for credit card fraud detection. In contrast to conventional anomaly detection methodologies, our approach emphasizes the simultaneous consideration of both original and intermediate features. Experimental results conducted on the European cardholder dataset demonstrate the superior efficacy of our approach, with an achieved accuracy of 0.9224, F1 score of 0.9208, and MCC of 0.8456, surpassing existing fraud detection techniques.
Attenuation relationship of macroseismic intensity in Italy for probabilistic seismic hazard assessment

A statistical analysis of macrosesmic intensity decay with distance has been conducted by using the most recent compilation of felt intensities relative to Italian earthquakes. An innovative aspect of the present study has been the attempt to define criteria for the validation of the attenuation relationships considered in their complete probabilistic form by a quantitative comparison with observed macroseismic fields. From the results of our analyses, a new attenuation relationship is proposed for the Italian territory, based on two independent variables only: epicentral intensity and hypocentral distance. Preliminary indications on the existence of differentiated attenuation features of macroseismic intensity over the national territory were gained.
Transformer-Based Feature Fusion Shrinkage Method for Remote Sensing Image Change Detection

In the context of deep learning, remote sensing image change detection technology is advancing rapidly. At this stage, when compared to traditional convolutional neural networks, transformers exhibit a more robust capability for modeling global context. However, transformer also has limitations, on the one hand, it is easy to ignore local feature details, and on the other hand, it cannot sufficiently aggregate features in the decoding process. To solve these two problems, we propose a transformer-based feature fusion shrinkage network (FFSNet). First we introduce the coordinate attention module, which is capable of capturing precise positional information while handling inter-channel relationships, enhancing the capture of local details. In addition, we design a feature fusion compression decoder (FFSD), which is a pyramid-structured decoder that not only optimizes local details but also decodes them adequately. After extensive experiments, we demonstrate that our model performs significantly better than existing change detection models.
You Cooperate, I Reciprocate: Well-Being and Trust in Automated Vehicles

Cooperative automated vehicles (AVs) bring the potential for better safety, efficiency, and energy-savings on the individual and system level. Yet, these benefits can only be achieved if people cooperate. In this study, we explored the effects of cooperative and reciprocal AVs on people's wellbeing, trust, and cooperation. We conducted a mixed-design study (n = 304), where participants experienced four types of social interactions as between-subject conditions: 1) all altruistic interaction; 2) all selfish interaction; 3) altruistic other vehicle, selfish ego AV; 4) selfish other vehicle, altruistic ego AV. We found people's well-being was highest when other vehicle is selfish and ego AV is altruistic; whereas people's trust was the highest when people experienced all altruistic interactions. Results suggested a design balance when evaluating people's attitudes towards AVs: altruistic AVs can promote people's trust, whereas when evaluating people's well-being, the presence of selfish AV s may be beneficial. Future studies could model the balance between the user optimal and system optimal control policies.
Research on Intelligent Vehicle Traffic Scenes Mapping Algorithm Considering Dynamic Objects

The intelligent vehicle perception algorithm is a fundamental technology for constructing the intelligent transportation system. To better estimate the trajectory of intelligent vehicles and achieve traffic scene mapping, this paper proposes an intelligent vehicle mapping algorithm framework considering dynamic objects. The proposed framework can be applied to monocular, stereo and RGB-D cameras. It combines lightweight dynamic object detection algorithms (Yolov5s) with traditional visual simultaneous localization and mapping algorithms (ORB-SLAM2) to reduce the impact of dynamic objects on mapping accuracy. The algorithm uses an improved optical flow method combined with the Yolov5 object detection algorithm. Yolov5 is utilized to detect potential dynamic objects in traffic scenes, while the improved optical flow method filters out real dynamic objects containing dynamic feature points, effectively removing dynamic objects from the images captured by the intelligent vehicle-mounted camera. Experiments on the KITTI dataset show that our algorithm can effectively reduce the impact of dynamic objects on pose estimation in intelligent vehicle traffic scenes and reduce trajectory error compared to the ORB-SLAM2 algorithm. Additionally, the algorithm has advantages over typical dynamic SLAM algorithms in terms of realtime performance.
A Comfort-Based Vehicle Routing Methodology for Autonomous Vehicles

Ensuring passenger comfort is a way to enhance market penetration rates and realize the advantages of autonomous vehicles (AVs). Unfortunately, only a few studies explored this domain. While several factors affect passenger comfort, this study focuses on route choice as an important attribute of passengers' comfort. Accordingly, this study introduces a methodology to generate the most comfortable path for AVs (instead of the shortest travel time). The first step towards realizing this goal is defining the relationship between passenger/user comfort and AVs' speed using a Support Vector Machine (SVM) based model, which is developed based on a dataset collected by the authors from passenger comfort in real-world driving scenarios. The SVM's output is called comfort costs, ranging from 0 to 2 with 0 being comfort, 1 indicating neutrality, and 2 representing discomfort. These comfort costs are then input into Dijsktra's algorithm to find the most comfortable path. The proposed methodology is tested in the University of Illinois at Urbana-Champaign street network. The findings show that the most comfortable path may increase travel time and induce completely different network-level traffic flow dynamics. The findings also suggest the importance of considering a trade-off between the comfort costs and the travel time.