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Artículos de revistas sobre el tema "Machine learning-based collision prediction"

Autor: Grafiati

Publicado: 7 de junio de 2025

Última modificación: 2 de agosto de 2025

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1

Li, Xiaohang, Hongda Wang, Meiting Jiang, et al. "Collision Cross Section Prediction Based on Machine Learning." Molecules 28, no. 10 (2023): 4050. http://dx.doi.org/10.3390/molecules28104050.

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Ion mobility-mass spectrometry (IM-MS) is a powerful separation technique providing an additional dimension of separation to support the enhanced separation and characterization of complex components from the tissue metabolome and medicinal herbs. The integration of machine learning (ML) with IM-MS can overcome the barrier to the lack of reference standards, promoting the creation of a large number of proprietary collision cross section (CCS) databases, which help to achieve the rapid, comprehensive, and accurate characterization of the contained chemical components. In this review, advances i

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2

Chang, Che-Cheng, Yee-Ming Ooi, and Bing-Herng Sieh. "IoV-Based Collision Avoidance Architecture Using Machine Learning Prediction." IEEE Access 9 (2021): 115497–505. http://dx.doi.org/10.1109/access.2021.3105619.

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3

Liu, Peng, Weiwei Zhang, Xuncheng Wu, Wenfeng Guo, and Wangpengfei Yu. "Driver Injury Prediction and Factor Analysis in Passenger Vehicle-to-Passenger Vehicle Collision Accidents Using Explainable Machine Learning." Vehicles 7, no. 2 (2025): 42. https://doi.org/10.3390/vehicles7020042.

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Vehicle accidents, particularly PV-PV collisions, result in significant property damage and driver injuries, causing substantial economic losses and health risks. Most existing studies focus on macro-level predictions, such as accident frequency, but lack detailed collision-level analysis, which limits the precision of severity prediction. This study investigates various accident-related factors, including environmental conditions, vehicle attributes, driver characteristics, pre-crash scenarios, and collision dynamics. Data from NHTSA’s CRSS and FARS datasets were integrated and balanced using

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4

Lammers, Caleb, Miles Cranmer, Sam Hadden, Shirley Ho, Norman Murray, and Daniel Tamayo. "Accelerating Giant-impact Simulations with Machine Learning." Astrophysical Journal 975, no. 2 (2024): 228. http://dx.doi.org/10.3847/1538-4357/ad7fe5.

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Abstract Constraining planet-formation models based on the observed exoplanet population requires generating large samples of synthetic planetary systems, which can be computationally prohibitive. A significant bottleneck is simulating the giant-impact phase, during which planetary embryos evolve gravitationally and combine to form planets, which may themselves experience later collisions. To accelerate giant-impact simulations, we present a machine learning (ML) approach to predicting collisional outcomes in multiplanet systems. Trained on more than 500,000 N-body simulations of three-planet

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5

Raj, Nitish, and Prabhat Kumar. "Leveraging HDBSCAN, LSTM and R-DTW for Proactive Detection and Collision Prediction in Maritime Traffic." Defence Science Journal 75, no. 4 (2025): 490–97. https://doi.org/10.14429/dsj.20660.

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Detecting anomalies in Automatic Identification System (AIS) data is crucial for marine safety, especially with over 60,000 vessels navigating seaways at any moment. This study proposes an enhanced approach to AIS data analysis for detecting anomalous ship behaviours and predicting collisions in maritime environments. Unlike traditional methods that rely on static threshold-based rules or simpler clustering techniques, our approach integrates advanced machine learning methods like Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) and Long Short-Term Memory (LST

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6

Babaoglu, Liza, and Ceni Babaoglu. "Prediction of Fatalities in Vehicle Collisions in Canada." Promet - Traffic&Transportation 33, no. 5 (2021): 661–69. http://dx.doi.org/10.7307/ptt.v33i5.3782.

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Traffic collisions affect millions around the world and are the leading cause of death for children and young adults. Thus, Canada’s road safety plan is to reduce collision injuries and fatalities with a vision of making the safest roads in the world. We aim to predict fatalities of collisions on Canadian roads, and to discover causation of fatalities through exploratory data analysis and machine learning techniques. We analyse the vehicle collisions from Canada’s National Collision Database (1999–2017.) Through data mining methodologies, we investigate association rules and key contributing f

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7

Abhishek, Saxena, and A. Robila Stefan. "Automated machine learning for analysis and prediction of vehicle crashes." International Journal of Informatics and Communication Technology 12, no. 1 (2023): 46–53. https://doi.org/10.11591/ijict.v12i1.pp46-5.

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This work discusses the study and development of a graphical interface and implementation of a machine learning model for vehicle traffic injury and fatality prediction for a specified date range and for a certain zip (US postal) code based on the New York City's (NYC) vehicle crash data set. While previous studies focused on accident causes, little insight has been offered into how such data may be utilized to forecast future incidents, Studies that have historically concentrated on certain road segment types, such as highways and other streets, and a specific geographic region, this stud

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8

Choi, Dongho, Janghyuk Yim, Minjin Baek, and Sangsun Lee. "Machine Learning-Based Vehicle Trajectory Prediction Using V2V Communications and On-Board Sensors." Electronics 10, no. 4 (2021): 420. http://dx.doi.org/10.3390/electronics10040420.

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Predicting the trajectories of surrounding vehicles is important to avoid or mitigate collision with traffic participants. However, due to limited past information and the uncertainty in future driving maneuvers, trajectory prediction is a challenging task. Recently, trajectory prediction models using machine learning algorithms have been addressed solve to this problem. In this paper, we present a trajectory prediction method based on the random forest (RF) algorithm and the long short term memory (LSTM) encoder-decoder architecture. An occupancy grid map is first defined for the region surro

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9

Ribeiro, Bruno, Maria João Nicolau, and Alexandre Santos. "Using Machine Learning on V2X Communications Data for VRU Collision Prediction." Sensors 23, no. 3 (2023): 1260. http://dx.doi.org/10.3390/s23031260.

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Intelligent Transportation Systems (ITSs) are systems that aim to provide innovative services for road users in order to improve traffic efficiency, mobility and safety. This aspect of safety is of utmost importance for Vulnerable Road Users (VRUs), as these users are typically more exposed to dangerous situations, and their vehicles also possess poorer safety mechanisms when in comparison to regular vehicles on the road. Implementing automatic safety solutions for VRU vehicles is challenging since they have high agility and it can be difficult to anticipate their behavior. However, if equippe

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10

Geng, Zhaoshi, Xiaofeng Ji, Rui Cao, Mengyuan Lu, and Wenwen Qin. "A Conflict Measures-Based Extreme Value Theory Approach to Predicting Truck Collisions and Identifying High-Risk Scenes on Two-Lane Rural Highways." Sustainability 14, no. 18 (2022): 11212. http://dx.doi.org/10.3390/su141811212.

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Collision risk identification and prediction is an effective means to prevent truck accidents. However, most existing studies focus only on highways, not on two-lane rural highways. To predict truck collision probabilities and identify high-risk scenes on two-lane rural highways, this study first calculated time to collision and post-encroachment time using high-precision trajectory data and combined them with extreme value theory to predict the truck collision probability. Subsequently, a traffic feature parameter system was constructed with the driving behavior risk parameter. Furthermore, m

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11

Yang, Fan, Denice van Herwerden, Hugues Preud’homme, and Saer Samanipour. "Collision Cross Section Prediction with Molecular Fingerprint Using Machine Learning." Molecules 27, no. 19 (2022): 6424. http://dx.doi.org/10.3390/molecules27196424.

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High-resolution mass spectrometry is a promising technique in non-target screening (NTS) to monitor contaminants of emerging concern in complex samples. Current chemical identification strategies in NTS experiments typically depend on spectral libraries, chemical databases, and in silico fragmentation tools. However, small molecule identification remains challenging due to the lack of orthogonal sources of information (e.g., unique fragments). Collision cross section (CCS) values measured by ion mobility spectrometry (IMS) offer an additional identification dimension to increase the confidence

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12

Liao, Yicui, Shimiao Zhang, and Zixin Zhang. "Research on Titanic Survival Prediction Based on Machine Learning Method." Advances in Economics, Management and Political Sciences 152, no. 1 (2025): 152–62. https://doi.org/10.54254/2754-1169/2024.19451.

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On April 15, 1912, the British luxury passenger ship Titanic sank on its maiden voyage from Southampton to New York because of a collision with an iceberg, resulting in the death of 1502 out of 2224 passengers and crew. This article gains insight into the factors that influence the survival rate of passengers on the Titanic and establish a model of hard voting consisting of logistic regression, random forest and decision tree to predict what sort of people are more likely to survive in this catastrophe. The process involves dealing with the missing values, creating new variables by feature eng

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13

Saxena, Abhishek, and Stefan A. Robila. "Automated machine learning for analysis and prediction of vehicle crashes." International Journal of Informatics and Communication Technology (IJ-ICT) 12, no. 1 (2023): 46. http://dx.doi.org/10.11591/ijict.v12i1.pp46-53.

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<p>This work discusses the study and development of a graphical interface and implementation of a machine learning model for vehicle traffic injury and fatality prediction for a specified date range and for a certain zip (US postal) code based on the New York City's (NYC) vehicle crash data set. While previous studies focused on accident causes, little insight has been offered into how such data may be utilized to forecast future incidents, Studies that have historically concentrated on certain road segment types, such as highways and other streets, and a specific geographic region, this

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14

Li, Yu-Ru, Tao Zhu, Zhao Tang, et al. "Inversion prediction of back propagation neural network in collision analysis of anti-climbing device." Advances in Mechanical Engineering 12, no. 5 (2020): 168781402092205. http://dx.doi.org/10.1177/1687814020922050.

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Targeting to improve the calculation efficiency of the finite element simulation, we introduce the back propagation neural network–based machine learning method to carry out the inversion prediction framework. The inversion collision model is established based on the inversion prediction framework. Then, the prediction results are compared with the finite element simulation results of the anti-climbing device to verify the feasibility of the inversion collision model. The average prediction errors of velocity, displacement, interface force, and internal energy of the anti-climbing device are 3

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15

Shanshal, Dalia, Ceni Babaoglu, and Ayşe Başar. "Prediction of Fatal and Major Injury of Drivers, Cyclists, and Pedestrians in Collisions." Promet - Traffic&Transportation 32, no. 1 (2020): 39–53. http://dx.doi.org/10.7307/ptt.v32i1.3134.

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Traffic-related deaths and severe injuries may affect every person on the roads, whether driving, cycling or walking. Toronto, the largest city in Canada and the fourth largest in North America, aims to eliminate traffic-related fatalities and serious injuries on city streets. The aim of this study is to build a prediction model using data analytics and machine learning techniques that learn from past patterns, providing additional data-driven decision support for strategic planning. A detailed exploratory analysis is presented, investigating the relationship between the variables and factors

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16

Guo, Lei, Yizhen Jia, Xianghui Hu, and Feihong Dong. "Forwarding Collision Assessment with the Localization Information Using the Machine Learning Method." Journal of Advanced Transportation 2022 (May 23, 2022): 1–10. http://dx.doi.org/10.1155/2022/9530793.

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Freeway crashes occupied 30% of total crashes. The advanced driver-assistance system (ADAS) often underestimates or omits the necessary collision warning. To investigate the forward collision in complex traffic conditions, the TTC (time-to-collision) is regarded as a surrogate for collision risk assessment. The study aims to design a forward-collision warning method for ADAS in the urban freeway scenario. The testing vehicle is equipped with sensors and a satellite navigation system. The TTC was collected from the Xi’an Rao Cheng expressway for the car-following scenario for three days. A comp

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17

Zermane, Hanane, Abderrahim Zermane, and Mohd Zahirasri Mohd Tohir. "The Effect of the COVID-19 Pandemic on Economic Growth and R&D Spending in Czechia, Germany, and Poland." ACC JOURNAL 30, no. 1 (2024): 24–49. http://dx.doi.org/10.2478/acc-2024-0003.

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Abstract Ensuring public safety on our roads is a top priority, and the prevalence of road accidents is a major concern. Fortunately, advances in machine learning allow us to use data to predict and prevent such incidents. Our study delves into the development and implementation of machine learning techniques for predicting road accidents, using rich datasets from Catalonia and Toronto Fatal Collision. Our comprehensive research reveals that ensemble learning methods outperform other models in most prediction tasks, while Decision Tree and K-NN exhibit poor performance. Additionally, our findi

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18

Samudrala, Tarunika, Daphine Joy P, Akshara Reddy Todupuniri, and KS Vishnu. "Comparative Analysis of Machine Learning Models for Accident Severity Prediction." International Journal of Human Computations and Intelligence 3, no. 5 (2025): 358–69. https://doi.org/10.5281/zenodo.14591111.

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Accidents pose two major concerns: road safety and public health. The primary objective of this study was to develop an accident severity detection system that leverages machine learning algorithms to analyze a variety of influential factors, enabling the prediction of accident severity levels. The supervised learning algorithms employed in this system include Decision Trees, Naive Bayes, Support Vector Machines (SVM), Random Forest, and Logistic Regression, all aimed at providing accurate severity predictions. Key features incorporated in the training and testing datasets encompass driver dem

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19

Liu, Sheng, Conghao Liu, Xunan An, Xin Liu, and Liang Hao. "Intelligent Damage Prediction During Vehicle Collisions Based on Simulation Datasets." Inventions 10, no. 3 (2025): 40. https://doi.org/10.3390/inventions10030040.

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Accurate prediction of vehicle damage in collision scenarios is crucial for enhancing road safety. However, traditional collision simulation methods are computationally intensive and time consuming. In this study, we proposed an intelligent damage prediction model that significantly reduces the computational time required for collision simulations by leveraging collision simulation datasets in conjunction with the random forest (RF) algorithm. A finite element model for vehicle collision simulation was first established. Subsequently, a dataset comprising 160 collision scenarios was generated

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20

Matyushin, Dmitriy D., Ivan A. Burov, and Anastasia Yu Sholokhova. "Uncertainty Quantification and Flagging of Unreliable Predictions in Predicting Mass Spectrometry-Related Properties of Small Molecules Using Machine Learning." International Journal of Molecular Sciences 25, no. 23 (2024): 13077. https://doi.org/10.3390/ijms252313077.

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Mass spectral identification (in particular, in metabolomics) can be refined by comparing the observed and predicted properties of molecules, such as chromatographic retention. Significant advancements have been made in predicting these values using machine learning and deep learning. Usually, model predictions do not contain any indication of the possible error (uncertainty) or only one criterion is used for this purpose. The spread of predictions of several models included in the ensemble, and the molecular similarity of the considered molecule and the most “similar” molecule from the traini

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21

Mohammad, Fokhrul Islam Buian, Anan Arde Ramisha, Masum Billah Md, Debnath Amit, and Md Siddique Iqtiar. "Advanced analytics for predicting traffic collision severity assessment." World Journal of Advanced Research and Reviews 21, no. 2 (2024): 2007–18. https://doi.org/10.5281/zenodo.14043830.

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Accurate prediction of accident risks plays a crucial role in proactively implementing safety measures and allocating resources effectively. This paper introduces an innovative approach aimed at improving accident risk prediction by harnessing unique data sources and extracting insights from diverse yet sparse datasets. Traditional models often face limitations due to a lack of diversity and scope in the available data, which hinders their predictive capabilities. In response to this challenge, our study integrates a broad spectrum of heterogeneous data, encompassing traffic flow, weather cond

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22

Xia, Yulan, Yaqin Qin, Xiaobing Li, and Jiming Xie. "Risk Identification and Conflict Prediction from Videos Based on TTC-ML of a Multi-Lane Weaving Area." Sustainability 14, no. 8 (2022): 4620. http://dx.doi.org/10.3390/su14084620.

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Crash risk identification and prediction are expected to play an important role in traffic accident prevention. However, most of the existing studies focus only on highways, not on multi-lane weaving areas. In this paper, a potential collision risk identification and conflict prediction model based on extending Time-to-Collision-Machine Learning (TTC-ML) for multi-lane weaving zone was proposed. The model can accurately learn various features, such as vehicle operation characteristics, risk and conflict distributions, and physical zoning characteristics in the weaving area. Specifically, TTC w

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23

Gao, Kai, Di Yan, Fan Yang, et al. "Conditional Artificial Potential Field-Based Autonomous Vehicle Safety Control with Interference of Lane Changing in Mixed Traffic Scenario." Sensors 19, no. 19 (2019): 4199. http://dx.doi.org/10.3390/s19194199.

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Car-following is an essential trajectory control strategy for the autonomous vehicle, which not only improves traffic efficiency, but also reduces fuel consumption and emissions. However, the prediction of lane change intentions in adjacent lanes is problematic, and will significantly affect the car-following control of the autonomous vehicle, especially when the vehicle changing lanes is only a connected unintelligent vehicle without expensive and accurate sensors. Autonomous vehicles suffer from adjacent vehicles’ abrupt lane changes, which may reduce ride comfort and increase energy consump

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24

Pawan, Sahu, and Kore Sushma. "Enhancements in CSMA/CD and CSMA/CA for Collision Handling in Ethernet and Wi-Fi Networks." Journal of Network Security and Data Mining 8, no. 2 (2025): 24–30. https://doi.org/10.5281/zenodo.15356719.

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<em>Carrier Sense Multiple Access with Collision Detection (CSMA/CD) and Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) are fundamental access control mechanisms used in Ethernet and Wi-Fi networks, respectively. However, as network traffic grows and devices become more interconnected, traditional implementations of these protocols encounter inefficiencies, leading to increased collision rates, network congestion, and higher transmission delays. This paper explores recent advancements in CSMA/CD and CSMA/CA aimed at improving collision handling, reducing packet loss, and opti

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Anjani, Suputri Devi D., D. Manjusha, P. Pujith, Ch G. V. Satyanarayana, V. Sailusha, and Reddy G. Vivekananda. "Comparative analysis for survival prediction from titanic disaster using machine learning." i-manager’s Journal on Software Engineering 18, no. 1 (2023): 36. http://dx.doi.org/10.26634/jse.18.1.20137.

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Among the most notorious shipwrecks in history is the Titanic. Out of the 2,224 passengers and crew, 1,502 perished when the Titanic sank on April 15, 1912, during her maiden voyage, following an iceberg collision. Ship safety laws have improved as a result of this dramatic disaster that stunned the world. Scientists and investigators are beginning to understand what could have caused some passengers to survive while others perished in the Titanic catastrophe. A contributing factor in the high death toll from the shipwreck was the insufficient number of lifeboats available for both passengers

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26

Yao, Xinpeng, Nengchao Lyu, and Mengfei Liu. "A Short-Term Risk Prediction Method Based on In-Vehicle Perception Data." Sensors 25, no. 10 (2025): 3213. https://doi.org/10.3390/s25103213.

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Advanced driving assistance systems (ADASs) provide rich data on vehicles and their surroundings, enabling early detection and warning of driving risks. This study proposes a short-term risk prediction method based on in-vehicle perception data, aiming to support real-time risk identification in ADAS environments. A variable sliding window approach is employed to determine the optimal prediction window lead length and duration. The method incorporates Monte Carlo simulation for threshold calibration, Boruta-based feature selection, and multiple machine learning models, including the light grad

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27

Mounasri, Mrs, V. Ujwala, and R. Gowthami. "Motion Pattern Classification on Online/Active Data-Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 8 (2022): 1013–16. http://dx.doi.org/10.22214/ijraset.2022.45338.

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Abstract: Ship behaviour recognition and prediction is very important for the early warning of risky behaviour, identifying potential ship collision, improving maritime traffic efficiency etc., and thus is a very active topic in the intelligent maritime navigation community. The high flow of vessel traffic affects the difficulty of monitoring vessel in the middle of the sea because of limited human visibility, occurrence of vessel accidents at the sea and other illegal activities that illustrate abnormal vessel behaviour such as oil bunkering, piracy, illegal fishing and other crimes that will

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Mohsin Najim Sarayyih AL-Maliki. "Vehicle-to-Everything Cloud Collision Prediction Architecture for Random Forest and Software-Defined Networking." Journal of Information Systems Engineering and Management 10, no. 9s (2025): 302–14. https://doi.org/10.52783/jisem.v10i9s.1231.

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The rising number of road accidents worldwide underscores the urgent need to implement Vehicle- to-Everything (V2X) communications, which can help minimize fatalities and injuries. This paper proposes a novel cloud-based architecture with machine learning capabilities that enables smooth, intelligent management of V2X services. The system features a backup controller that can take over in case of failure, ensuring uninterrupted operation. Additionally, the ML models provide invaluable optimization, from predicting traffic patterns to maximizing resource utilization and connection quality. With

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29

Chai, Tian, and Han Xue. "A study on ship collision conflict prediction in the Taiwan Strait using the EMD-based LSSVM method." PLOS ONE 16, no. 5 (2021): e0250948. http://dx.doi.org/10.1371/journal.pone.0250948.

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Ship collision accidents are the primary threat to traffic safety in the sea. Collision accidents can cause casualties and environmental pollution. The collision risk is a major indicator for navigators and surveillance operators to judge the collision danger between meeting ships. The number of collision accidents per unit time in a certain water area can be considered to describe the regional collision risk However, historical ship collision accidents have contingencies, small sample sizes and weak regularities; hence, ship collision conflicts can be used as a substitute for ship collision a

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30

Truong, Duc P., Lyman K. Monroe, Robert F. Williams, and Hau B. Nguyen. "Machine Learning Framework for Conotoxin Class and Molecular Target Prediction." Toxins 16, no. 11 (2024): 475. http://dx.doi.org/10.3390/toxins16110475.

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Conotoxins are small and highly potent neurotoxic peptides derived from the venom of marine cone snails which have captured the interest of the scientific community due to their pharmacological potential. These toxins display significant sequence and structure diversity, which results in a wide range of specificities for several different ion channels and receptors. Despite the recognized importance of these compounds, our ability to determine their binding targets and toxicities remains a significant challenge. Predicting the target receptors of conotoxins, based solely on their amino acid se

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31

A, Ananya, and Srividya M S. "AI Based Hazardous Asteroid Detection." IOSR Journal of Computer Engineering 26, no. 6 (2024): 37–44. http://dx.doi.org/10.9790/0661-2606013744.

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As the need for early detection and mitigation of potential threats from near-Earth objects grows, this study presents a comprehensive approach to predicting hazardous asteroids using machine learning techniques. Given the increasing importance of protecting our planet from potential impact events, accurately classifying and predicting hazardous asteroids is cru- cial. The study proposes a comprehensive system for predicting and mitigating asteroid hazards using advanced data processing, machine learning, and simulation techniques. The proposed study collect real-time and historical asteroid d

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Mafi, Somayeh, Yassir AbdelRazig, and Ryan Doczy. "Machine Learning Methods to Analyze Injury Severity of Drivers from Different Age and Gender Groups." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 38 (2018): 171–83. http://dx.doi.org/10.1177/0361198118794292.

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Access to non-biased and accurate models capable of predicting driver injury severity of collision events is vital for determining what safety measures should be implemented at intersections. Inadequate models can underestimate the potential for collision events to result in driver fatalities or injuries, which can lead to improperly assessing the safety criteria of an intersection. This study investigates how injury severity differs between drivers of various ages and gender groups using cost-sensitive data-mining models. Previous research efforts have used machine learning methods for predic

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Ka, Dongho, Donghoun Lee, Sunghoon Kim, and Hwasoo Yeo. "Study on the Framework of Intersection Pedestrian Collision Warning System Considering Pedestrian Characteristics." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 5 (2019): 747–58. http://dx.doi.org/10.1177/0361198119838519.

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One of the most widely used advanced driver assistance systems (ADAS) for preventing pedestrian–vehicle collisions is the intersection collision warning system (ICWS). Most previous ICWSs have been implemented with in-vehicle distance sensors, such as radar and lidar. However, the existing ICWSs show some weaknesses in alerting drivers at intersections because of limited detection range and field-of-view. Furthermore, these ICWSs have difficulties in identifying the pedestrian’s crossing intention because the distance sensors cannot capture pedestrian characteristics such as age, gender, and h

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Luo, Zhongbin, Yanqiu Bi, Qing Ye, Yong Li, and Shaofei Wang. "A Novel Machine Vision-Based Collision Risk Warning Method for Unsignalized Intersections on Arterial Roads." Electronics 14, no. 6 (2025): 1098. https://doi.org/10.3390/electronics14061098.

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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 bo

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Losada, Ángel, Francisco Javier Páez, Francisco Luque, and Luca Piovano. "Application of Machine Learning Techniques for Predicting Potential Vehicle-to-Pedestrian Collisions in Virtual Reality Scenarios." Applied Sciences 12, no. 22 (2022): 11364. http://dx.doi.org/10.3390/app122211364.

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The definition of pedestrian behavior when crossing the street and facing potential collision situations is crucial for the design of new Autonomous Emergency Braking systems (AEB) in commercial vehicles. To this end, this article proposes the generation of classification models through the deployment of machine learning techniques that can predict whether there will be a collision depending on the type of reaction, the lane where it occurs, the visual acuity the level of attention, and consider the most relevant factors that determine the cognitive and movement characteristics of pedestrians.

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Sarkar, Amrita, and Vandana Bhattacharjee. "A Comparative Study of Accident Black Spot Identification: A Crucial Analysis Using Machine Learning and Statistical Approach." International Journal of Experimental Research and Review 47 (April 30, 2025): 84–92. https://doi.org/10.52756/ijerr.2025.v47.007.

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Identifying black spots in traffic accidents is critical for the urban traffic accident prediction model and better traffic safety management. Several strategies have been used to identify accidents on urban roadways. Some employ Statistical methodologies, while others, more recently, employ Machine Learning approaches. Fuzzy Logic-based algorithms are ideal for predicting car collisions because they can quickly generate complex non-linear relationships between data. A fully fuzzy-based technique, on the other hand, would provide the required user-defined knowledge. Neural Networks are suitabl

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Sakamoto, Nami, Takaki Oka, Yuki Matsuzawa, et al. "MS2Lipid: A Lipid Subclass Prediction Program Using Machine Learning and Curated Tandem Mass Spectral Data." Metabolites 14, no. 11 (2024): 602. http://dx.doi.org/10.3390/metabo14110602.

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Background: Untargeted lipidomics using collision-induced dissociation-based tandem mass spectrometry (CID-MS/MS) is essential for biological and clinical applications. However, annotation confidence still relies on manual curation by analytical chemists, despite the development of various software tools for automatic spectral processing based on rule-based fragment annotations. Methods: In this study, we present a novel machine learning model, MS2Lipid, for the prediction of known lipid subclasses from MS/MS queries, providing an orthogonal approach to existing lipidomics software programs in

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Park, Jaesung, and Yujin Lim. "Adaptive Access Class Barring Method for Machine Generated Communications." Mobile Information Systems 2016 (2016): 1–6. http://dx.doi.org/10.1155/2016/6923542.

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Cellular network is provisioned to serve traffic demands generated by human being. The random access channel used for nodes to compete for a connection with an eNB is limited. Even though machines generate very small amount of data traffic, the signaling channel of a network becomes overloaded and collisions occur to fail the access if too many MTC (Machine Type Communication) devices attempt to access network. To tackle the issue, 3GPP specifies an access class barring but leaves a specific algorithm as an implementation issue. In this paper, we propose an adaptive access barring method. Gene

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Samuel Omefe. "Post-crash analysis and injury severity prediction in vehicle-pedestrian collisions using logistic regression and AI-based predictive analytics." International Journal of Science and Research Archive 16, no. 1 (2025): 912–23. https://doi.org/10.30574/ijsra.2025.16.1.2077.

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Post-crash analysis and road safety management is critical for prediction of injury severity after a vehicle-pedestrian collision. Efficient prediction of injury severity after the occurrence of such events is necessary to guideline emergence medical care enhancement, transportation planning, as well as evidence-based safety courses of action. Traditional statistical methods, such as logistic regression, have been utilized vastly because of their transparent and interpretable results. However, recent developments in Artificial Intelligence (AI) have proposed machine learning (ML) and deep lear

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Losada, Ángel, Francisco Javier Páez, Francisco Luque, Luca Piovano, Nuria Sánchez, and Miguel Hidalgo. "Vehicle-to-Cyclist Collision Prediction Models by Applying Machine Learning Techniques to Virtual Reality Bicycle Simulator Data." Applied Sciences 14, no. 9 (2024): 3570. http://dx.doi.org/10.3390/app14093570.

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The study of vulnerable road users (VRUs) behavior is key to designing and optimizing driving assistance systems, such as the autonomous emergency braking (AEB) system. These kinds of devices could help lower the VRU accident rate, which is of particular interest to cyclists, who are the subject of this research. To better understand cyclists’ reaction patterns in frequently occurring collision scenarios in urban environments, this paper focuses on developing a virtual reality (VR) simulator for cyclists (VRBikeSim) that incorporates eye-tracking functionality. The braking and steering systems

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Dr.S. Poornima, K Elankumaran, M Nandhana, B Parthasarathy, and V Sudharshan. "Autonomous Vehicle Lane-Changing System using Light GBM." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 07 (2025): 3185–94. https://doi.org/10.47392/irjaeh.2025.0469.

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Traffic dynamics and autonomous vehicle navigation depend heavily on lane-changing behaviour. This paper introduces a machine learning-based method to predict lane-changing intentions using vehicle trajectory data and driver behaviour analysis. Our model achieves high accuracy while maintaining computational efficiency by utilizing the Gaussian Mixture Model (GMM) for driver behaviour clustering and LightGBM for lane-change classification. The system incorporates feature engineering, and hyperparameter optimization to improve prediction reliability. Our experimental results show that the sugge

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Rudresh Deepak Shirwaikar. "Predicting Accident Severity using Machine Learning." Journal of Electrical Systems 20, no. 6s (2024): 2733–46. http://dx.doi.org/10.52783/jes.3282.

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Each year, millions of people are killed in automobile accidents. Predicting the severity of an occurrence allows local authorities to respond quickly and save many lives. Information and data on traffic accidents made available by public organizations can be used to categories these incidents based on their nature and severity, and then attempt to construct predictive models that can be further investigated to identify fatal accident risk factors. The study provides ways to develop a system for determining the severity of accidents. To evaluate the severity of the collision, we use a variety

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Zhang, Jian, Zongxiao Li, Xinyue Luo, Yifei Zhao, and Fei Lu. "Study of Urban Unmanned Aerial Vehicle Separation in Free Flight Based on Track Prediction." Applied Sciences 14, no. 13 (2024): 5712. http://dx.doi.org/10.3390/app14135712.

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In recent years, the application prospect of urban logistics unmanned aerial vehicles has attracted extensive attention. The high-density operation of UAVs requires autonomous separation maintenance capability. To achieve autonomous separation maintenance, it is necessary to conduct autonomous track prediction and formulate the required separation accordingly. Based on the target level of safety requirements for UAV operation, aiming at the autonomous separation maintenance ability of UAVs and considering the accuracy of track prediction, a method to calculate the required separation between U

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Arnold, Caroline, Shivani Sharma, Tobias Weigel, and David S. Greenberg. "Efficient and stable coupling of the SuperdropNet deep-learning-based cloud microphysics (v0.1.0) with the ICON climate and weather model (v2.6.5)." Geoscientific Model Development 17, no. 9 (2024): 4017–29. http://dx.doi.org/10.5194/gmd-17-4017-2024.

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Abstract. Machine learning (ML) algorithms can be used in Earth system models (ESMs) to emulate sub-grid-scale processes. Due to the statistical nature of ML algorithms and the high complexity of ESMs, these hybrid ML ESMs require careful validation. Simulation stability needs to be monitored in fully coupled simulations, and the plausibility of results needs to be evaluated in suitable experiments. We present the coupling of SuperdropNet, a machine learning model for emulating warm-rain processes in cloud microphysics, with ICON (Icosahedral Nonhydrostatic) model v2.6.5. SuperdropNet is train

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Vysotska, Khrystyna, and Adrian Nakonechnyi. "Automobile system for predicting the trajectory of surrounding vehicles." International Science Journal of Engineering & Agriculture 3, no. 4 (2024): 38–50. http://dx.doi.org/10.46299/j.isjea.20240304.04.

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The improvement of autonomous driving systems and advanced driver assistance systems (ADAS) heavily relies on accurate vehicle trajectory prediction. This research focuses on developing a robust method for predicting the trajectories of surrounding vehicles by leveraging machine learning techniques and neural networks. The study integrates data from onboard sensors, vehicle-to-vehicle (V2V) communication, cameras, LiDAR, and Differential GPS (DGPS) to enhance the accuracy and reliability of trajectory forecasts. Traditional approaches, primarily based on physical models, fall short in complex

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Bukshetwar, Pawan. "ADAS using AI." International Journal for Research in Applied Science and Engineering Technology 12, no. 1 (2024): 112–14. http://dx.doi.org/10.22214/ijraset.2024.57919.

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Abstract: Advanced Driver Assistance Systems (ADAS) have emerged as a crucial technology in enhancing road safety and promoting autonomous driving. This thesis explores the integration of Artificial Intelligence (AI) techniques within ADAS for improved vehicle following performance. We delve into various AI approaches, including machine learning, deep learning, and computer vision, analyzing their strengths and limitations in the context of vehicle following. Additionally, the thesis highlights the challenges associated with AI-powered ADAS, such as sensor accuracy, environmental adaptability,

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Kim, Kyeongjin, WooSeok Kim, Junwon Seo, Yoseok Jeong, Meeju Lee, and Jaeha Lee. "Prediction of Concrete Fragments Amount and Travel Distance under Impact Loading Using Deep Neural Network and Gradient Boosting Method." Materials 15, no. 3 (2022): 1045. http://dx.doi.org/10.3390/ma15031045.

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In the present study, the amount of fragments generated and their travel distances due to vehicle collision with concrete median barrier (CMB) was analyzed and predicted. In this regard, machine learning was applied to the results of numerical analysis, which were developed by comparing with field test. The numerical model was developed using smoothed particle hydrodynamics (SPH). SPH is a mesh-free method that can be used to predict the amount of fragments and their travel distances from concrete structures under impact loading. In addition, deep neural network (DNN) and gradient boosting mac

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Barsotti, Damián, Franco Cerino, Manuel Tiglio, and Aarón Villanueva. "Gravitational wave surrogates through automated machine learning." Classical and Quantum Gravity 39, no. 8 (2022): 085011. http://dx.doi.org/10.1088/1361-6382/ac5ba1.

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Abstract We analyze a prospect for predicting gravitational waveforms from compact binaries based on automated machine learning (AutoML) from around a hundred different possible regression models, without having to resort to tedious and manual case-by-case analyses and fine-tuning. The particular study of this article is within the context of the gravitational waves emitted by the collision of two spinless black holes in initial quasi-circular orbit. We find, for example, that approaches such as Gaussian process regression with radial bases as kernels, an approach which is generalizable to mul

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Raj, Nitish, and Prabhat Kumar. "A Novel & Efficient LR LSTM AIS Route Data Prediction for Longer Range." Defence Science Journal 74, no. 4 (2024): 583–91. http://dx.doi.org/10.14429/dsj.74.19336.

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The growth of technology has enabled different industries to generate an excessive amount of data- one such industry being the maritime sector. Sophisticated sensory systems installed on various vessels generate information at a very large scale which can further be used in optimizing operational efficiency, improving safety standards, and aiding in the decision-making process. Researchers have henceforth identified statistical learning methods and machine learning techniques as potent tools for excavating useful insights from this copious amount of data available. This research evaluates how

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Zhong, Weifan, and Lijing Du. "Predicting Traffic Casualties Using Support Vector Machines with Heuristic Algorithms: A Study Based on Collision Data of Urban Roads." Sustainability 15, no. 4 (2023): 2944. http://dx.doi.org/10.3390/su15042944.

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Traffic accidents on urban roads are a major cause of death despite the development of traffic safety measures. However, the prediction of casualties in urban road traffic accidents has not been deeply explored in previous research. Effective forecasting methods for the casualties of traffic accidents can improve the manner of traffic accident warnings, further avoiding unnecessary loss. This paper provides a practicable model for traffic forecast problems, in which ten variables, including time characteristics, weather factors, accident types, collision characteristics, and road environment c

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