Search Results (49)

The increasing use of artificial intelligence (AI) and deep learning (DL) techniques has driven advances in vehicle classification and detection applications for embedded devices with deployment constraints due to computational cost and response time. In the case of urban environments with high traffic congestion, such as the city of Lima, it is important to determine the trade-off between model accuracy, type of embedded system, and the dataset used. This study was developed using a methodology adapted from the CRISP-DM approach, which included the acquisition of traffic videos in the city of Lima, their segmentation, and manual labeling. Subsequently, three SSD-based detection models (MobileNetV1-SSD, MobileNetV2-SSD-Lite, and VGG16-SSD) were trained on the NVIDIA Jetson Orin NX 16 GB platform. The results show that the VGG16-SSD model achieved the highest average precision (mAP $\approx 90.7\%$), with a longer training time, while the MobileNetV1-SSD ($512\times 512$) model achieved comparable performance (mAP $\approx 90.4\%$) with a shorter time. Additionally, data augmentation through contrast adjustment improved the detection of minority classes such as Tuk-tuk and Motorcycle. The results indicate that, among the evaluated models, MobileNetV1-SSD ($512\times 512$) achieved the best balance between accuracy and computational load for its implementation in ADAS embedded systems in congested urban environments. Full article

Vehicle detection from unmanned aerial vehicles (UAVs) has gained increasing attention due to the growing availability and accessibility of these platforms. UAV-captured videos have proven valuable in a variety of applications, including agriculture, security, and search and rescue operations. To support research in UAV-based vehicle detection, this paper introduces UTUAV: Urban Traffic Unmanned Aerial Vehicle, a dataset composed of traffic video images collected over the streets of Medellín, Colombia. The images are recorded from a semi-static position at two different altitudes (100 and 120 m) and include three manually annotated vehicle types: cars, motorcycles, and large vehicles. The analysis focuses on the main characteristics and challenges presented in the dataset. In particular, data leakage occurs when a single video is used to construct the training, validation, and evaluation sets. An inadequate data split can result in highly similar samples leaking into the evaluation set, leading to inflated performance metrics that do not reflect a model’s true generalization ability. Additionally, baseline results from recent state-of-the-art object detection models based on CNNs and Transformers (YOLOv8, YOLOv11, YOLOv12 and RT-DETR) are presented. The experiments highlight several challenges, including the difficulty of detecting small-scale objects, especially motorcycles, and limited generalization capabilities under altitude changes, a phenomenon commonly referred to as domain shift. Full article

This study compares the performance of three YOLO-based object detection models—YOLOv3, YOLOv5, and YOLOv8—for vehicle detection and classification at an urban intersection in Montería, Colombia. Recordings from five consecutive days, spanning three time slots, were used, totaling approximately 135,000 frames with variability in lighting and weather conditions. Frames were preprocessed by maintaining the aspect ratio and were normalized according to each model. The evaluation employed models pre-trained on COCO, without fine-tuning, enabling an objective assessment of their generalization capacity. Precision, recall, F1-score, and mAP@0.5 were computed globally and by vehicle class. YOLOv5 achieved the best balance between precision and recall (F1-score = 0.78) and the highest mAP (0.63), while YOLOv3 showed lower recall and mAP, and YOLOv8 performed competitively but slightly below YOLOv5. Cars and motorcycles were the most robust classes, whereas bicycles and trucks showed greater detection challenges. Visual evaluation confirmed stable performance on cloudy days and in light rain, with reduced accuracy under sunny conditions with high contrast. These findings highlight the potential of modern YOLO architectures for intelligent urban traffic monitoring and management. The generated dataset constitutes a replicable resource for future mobility research in similar contexts. Full article

This paper presents the design of an autonomous robot guide for a museum-like environment in a motorcycle assembly plant. The system integrates Industry 4.0 technologies such as artificial vision, indoor positioning, generative artificial intelligence, and cloud connectivity to enhance the visitor experience. The development follows the Design Inclusive Research (DIR) methodology and the VDI 2206 standard to ensure a structured scientific and engineering process. A key innovation is the integration of mmWave sensors alongside LiDAR and RGB-D cameras, enabling reliable human detection and improved navigation safety in reflective indoor environments, as well as the deployment of an open-source large language model for natural, on-device interaction with visitors. The current results include the complete mechanical, electronic, and software architecture; simulation validation; and a preliminary implementation in the real museum environment, where the system demonstrated consistent autonomous navigation, stable performance, and effective user interaction. Full article

In recent years, the issue of drivers under the influence of medications and psychoactive substances as a cause of road accidents has gained increasing importance. This study aimed to assess the prevalence and blood concentration ranges of alcohol and psychoactive substances among drivers in northeastern Poland between 2013 and 2024. To determine the prevalence of medications and psychoactive substances in drivers’ blood, data were collected from 266 blood samples obtained from drivers (251 men and 15 women). Among these, 79 drivers died immediately, 61 drivers survived the accident, and 126 drivers were stopped for roadside checks. The presence of the studied substances was confirmed using gas chromatography combined with mass spectrometry detection (GC-MS) and liquid chromatography combined with mass spectrometry detection (LC-MS). Blood alcohol content was measured using headspace gas chromatography with a flame ionisation detector (HS-GC-FID). Psychoactive substances were detected in 152 of the 266 samples. Drivers testing positive for medications and psychoactive substances were most frequently stopped during roadside controls—67.46%. Among the total positive cases, psychoactive substances used alone or in combination included THC—46.3% (range 0.2–20 ng/mL), alcohol—26.8% (range 0.1–4.1‰), amphetamines—20.7% (range 15–2997 ng/mL), opiates—4.3% (morphine 66.0 ng/mL; methadone 174.0 ng/mL; ranges: tramadol 15.0–600.0 ng/mL; fentanyl 45.0–100.0 ng/mL), benzodiazepines—9.8% (ranges: diazepam 55.0–480.0 ng/mL; midazolam 17.0–1200.0 ng/mL; clonazepam 21.0–36.0 ng/mL), stimulants—6.10% (ranges: amphetamine 15.0–2997.0 ng/mL; cocaine 4.0–30.0 ng/mL; benzoylecgonine 38.0–602.0 ng/mL; PMMA 45.0–360.0 ng/mL; MDMA 20.0–75.0 ng/mL; mephedrone 37.5 ng/mL; alfa-PVP 120 ng/mL), psychotropic drugs—3.1% (carbamazepine 8.0–2100.0 ng/mL; zolpidem 233.0 ng/mL; citalopram 320.0 ng/mL; opipramol 220 ng/mL). The most commonly used substance among car and motorcycle drivers was THC (37.7% of car drivers and 60% of motorcyclists). Among operators of other types of vehicles, alcohol was the most frequently detected substance, present in 35% of cases. The majority of drivers (81.1%) were under the influence of a single substance. Among the drivers, 7.3% consumed alcohol in combination with at least one other substance, and 11.6% used two or more substances excluding alcohol. Among the psychoactive substances most frequently used alone or in combination with others, THC was predominant. Roadside testing, based on effects similar to alcohol intoxication, was mainly conducted on male drivers. Full article

This study presents a novel method for the rapid identification of key performance indicators (KPIs) from measured riding data of a Ducati Panigale V2 motorcycle, aimed at enhancing racing performance through a deeper understanding of rider-vehicle interaction. The methodology involves the design and implementation of mathematical tools within the RaceStudio3 software to analyze data from the motorcycle’s sensor system. This approach facilitates the swift detection of critical events, including gearshift delays, improper throttle control, and suspension issues. The fusion of data from the motorcycle enables a comprehensive evaluation of the rider’s influence on performance. The results demonstrate the potential of the proposed method to provide valuable insights for optimizing motorcycle setup and rider technique. Full article

Advanced rider assistance systems (ARAS) play a crucial role in enhancing motorcycle safety through features such as collision avoidance, blind-spot detection, and adaptive cruise control, which rely heavily on sensors like radar, cameras, and LiDAR. However, their performance is often compromised under adverse weather conditions, leading to sensor interference, reduced visibility, and inconsistent reliability. This study evaluates the effectiveness and limitations of ARAS technologies in rain, fog, and snow, focusing on how sensor performance, algorithms, techniques, and dataset suitability influence system reliability. A thematic analysis was conducted, selecting studies focused on ARAS in adverse weather conditions based on specific selection criteria. The analysis shows that while ARAS offers substantial safety benefits, its accuracy declines in challenging environments. Existing datasets, algorithms, and techniques were reviewed to identify the most effective options for ARAS applications. However, more comprehensive weather-resilient datasets and adaptive multi-sensor fusion approaches are still needed. Advancing in these areas will be critical to improving the robustness of ARAS and ensuring safer riding experiences across diverse environmental conditions. Full article

In recent years, multi-modal 3D object detection algorithms have experienced significant development. However, current algorithms primarily focus on designing overall fusion strategies for multi-modal features, neglecting finer-grained representations, which leads to a decline in the detection accuracy of small objects. To address this issue, this paper proposes the Instance-aware Fine-grained feature Enhancement Cross Modal Transformer (IFE-CMT) model. We designed an Instance feature Enhancement Module (IE-Module), which can accurately extract object features from multi-modal data and use them to enhance overall features while avoiding view transformations and maintaining low computational overhead. Additionally, we design a new point cloud branch network that effectively expands the network’s receptive field, enhancing the model’s semantic expression capabilities while preserving texture details of the objects. Experimental results on the nuScenes dataset demonstrate that compared to the CMT model, our proposed IFE-CMT model improves mAP and NDS by 2.1% and 0.8% on the validation set, respectively. On the test set, it improves mAP and NDS by 1.9% and a 0.7%. Notably, for small object categories such as bicycles and motorcycles, the mAP improved by 6.6% and 3.7%, respectively, significantly enhancing the detection accuracy of small objects. Full article

Artificial intelligence (AI) and big data technology were applied to assess carbon emissions in a high-tourism area in this study. In the study site, the Thalang Road in Phuket Old Town, Thailand, visitors and vehicles (including cars, motorcycles, trucks, vans, and Tuktuks) were counted using closed-circuit television (CCTV) footage and classified via the real-time detection transformer (RT-DETR) algorithm. The data were combined with records of electricity usage. From March to October 2024, 20,000 visitors per month visited the site. Electricity was the main source of carbon emissions, averaging 88 ± 11 tCO₂-eq monthly. Transport accounted for 500 ± 14 kg CO₂-eq. The average emission per visitor was calculated as 4.2 ± 0.4 kg CO₂-eq. The results showed how sustainable tourism policies and urban planning strategies need to be developed in Phuket. Based on the results, indirect emissions from the site need to be estimated. Full article

Road traffic emissions are still a significant contributor to air pollution, which causes adverse health effects. Remote emission sensing (RES) is a state-of-the-art technique that continuously monitors the emissions of thousands of vehicles in traffic. Automatic number plate recognition (ANPR) systems are an essential part of RES systems to identify the registered owners of high-emitting vehicles. Recognizing number plates on L-vehicles (two-wheelers) with a standard ANPR system is challenging due to differences in size and placement across various categories. No ANPR system is designed explicitly for Category L vehicles, especially mopeds. In this work, we present an automatic number plate detection and recognition system for Category L vehicles (L-ANPR) specially developed to recognize L-vehicle number plates of various sizes and colors from different categories and countries. The cost-effective and energy efficient L-ANPR system was implemented on roads during remote emission measurement campaigns in multiple European cities and tested with hundreds of vehicles. The L-ANPR system recognizes Category L vehicles by calculating the size of each passing vehicle using photoelectric sensors. It can then trigger the L-ANPR detection system, which begins detecting license plates and recognizing license plate numbers with the L-ANPR recognizing system. The L-ANPR system’s license plate detection model is trained using thousands of images of license plates from various types of Category L vehicles across different countries, and the overall detection accuracy with test images exceeded 90%. The L-ANPR system’s character recognition is designed to identify large characters on standard number plates as well as smaller characters in various colors on small, moped license plates, achieving a recognition accuracy surpassing 70%. The reasons for false recognitions are identified and the solutions are discussed in detail. Full article

This study leverages IoT technology to develop a real-time monitoring system for large motorcycles. We collaborated with professional mechanics to define the required data types and system architecture, ensuring practicality and efficiency. The system integrates the NB-IoT for efficient remote data transmission and uses MQTT for optimized messaging. It also includes advanced database management and intuitive data visualization for enhancing the user experience. For hardware installation, the system follows strict guidelines to avoid damaging the motorcycle’s original structure, comply with Taiwan’s legal standards, and prevent unauthorized modifications. The implementation of this real-time monitoring system is anticipated to significantly reduce safety risks associated with mechanical failures as it continuously monitors inappropriate driving behaviors and detects mechanical abnormalities in real time. The study indicates that the integration of advanced technologies, such as the NB-IoT and multi-sensor systems, can lead to improved driving safety and operational efficiency. Furthermore, the research suggests that the system’s ability to provide instant notifications and alerts through the platforms’ instant messaging can enhance user responsiveness to potential hazards, thereby contributing to a safer riding experience. Full article

This paper proposes the development of a smart traffic light prototype based on vehicular traffic flow measurement in the stretch between two avenues in the city of Limeira, SP, Brazil, focusing on the stretch towards UNICAMP’s School of Technology. To this end, we initially developed a Python code using the OpenCV library in order to detect and count vehicles. With the counting in operation, programming logic was inserted, aiming at preparing traffic light timers based on vehicular traffic. Finally, the traffic lights were added to display video via a code change to show the ongoing color changes, also obtaining a code for identifying vehicles and flow, in addition to the virtual traffic light system itself in the system. Vehicle counting accuracy was 75% for large vehicles, 90% for passenger cars, and 100% for motorcycles. The simulation of a smart traffic light implementation worked satisfactorily according to the post-processing of the video recorded for validation. Full article

This study explores the potential of online traffic safety education for adolescent motorcyclists in Pakistan. An e-learning platform, “Route 2 School” (R2S), was developed focusing on traffic knowledge, situation awareness, risk detection, and risk management. Male students (14–18 years) who commute to school by motorcycle were divided into an experimental group (EG) and a control group (CG), both completing pre- and post-measurement questionnaires. The EG showed significant improvement in knowledge, risk detection, and risk management compared to the CG, but not in situation awareness. Participants reported increased traffic safety awareness and suggested adding more interactive elements. The R2S platform’s scores revealed better performance in risk detection and risk management modules than situation awareness. Time spent on modules varied, with situation awareness requiring the most time. Adolescents expressed satisfaction with the platform, acknowledging its role in increasing traffic awareness. This study provides initial insights into the effectiveness of online traffic safety education in Pakistan, highlighting the potential to address the lack of comprehensive traffic safety education in schools. Further research and stakeholder engagement are recommended to integrate such platforms into formal education, potentially reducing traffic-related injuries among adolescent motorcyclists in developing countries. Full article

Accurate vehicle detection is crucial for the advancement of intelligent transportation systems, including autonomous driving and traffic monitoring. This paper presents a comparative analysis of two advanced deep learning models—YOLOv8 and YOLOv10—focusing on their efficacy in vehicle detection across multiple classes such as bicycles, buses, cars, motorcycles, and trucks. Using a range of performance metrics, including precision, recall, F1 score, and detailed confusion matrices, we evaluate the performance characteristics of each model.The findings reveal that YOLOv10 generally outperformed YOLOv8, particularly in detecting smaller and more complex vehicles like bicycles and trucks, which can be attributed to its architectural enhancements. Conversely, YOLOv8 showed a slight advantage in car detection, underscoring subtle differences in feature processing between the models. The performance for detecting buses and motorcycles was comparable, indicating robust features in both YOLO versions. This research contributes to the field by delineating the strengths and limitations of these models and providing insights into their practical applications in real-world scenarios. It enhances understanding of how different YOLO architectures can be optimized for specific vehicle detection tasks, thus supporting the development of more efficient and precise detection systems. Full article

Robust object detection and weather classification are essential for the safe operation of autonomous vehicles (AVs) in adverse weather conditions. While existing research often treats these tasks separately, this paper proposes a novel multi objectives model that treats weather classification and object detection as a single problem using only the AV camera sensing system. Our model offers enhanced efficiency and potential performance gains by integrating image quality assessment, Super-Resolution Generative Adversarial Network (SRGAN), and a modified version of You Only Look Once (YOLO) version 5. Additionally, by leveraging the challenging Detection in Adverse Weather Nature (DAWN) dataset, which includes four types of severe weather conditions, including the often-overlooked sandy weather, we have conducted several augmentation techniques, resulting in a significant expansion of the dataset from 1027 images to 2046 images. Furthermore, we optimize the YOLO architecture for robust detection of six object classes (car, cyclist, pedestrian, motorcycle, bus, truck) across adverse weather scenarios. Comprehensive experiments demonstrate the effectiveness of our approach, achieving a mean average precision (mAP) of 74.6%, underscoring the potential of this multi objectives model to significantly advance the perception capabilities of autonomous vehicles’ cameras in challenging environments. Full article