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crossrefApplied Sciences2024-04-24Cited by 3

Vehicle-to-Cyclist Collision Prediction Models by Applying Machine Learning Techniques to Virtual Reality Bicycle Simulator Data

Ángel Losada, Francisco Javier Páez, Francisco Luque, Luca Piovano, Nuria Sánchez, Miguel Hidalgo

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 were calibrated by means of on-track tests with a sensorized bicycle in order to improve the accuracy of the bicycle virtual model. From the data obtained in the virtual tests, a battery of predictive models was built using supervised machine learning classifiers. All of them exhibited an accuracy higher than 85%, especially the K-Nearest Neighbors model. This model allowed us to obtain the best balance between the prediction of avoidance and collision cases, as well as enabling computationally lower times to be incorporated into the decision-making algorithm of an AEB system.

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crossrefApplied Sciences2026-01-21

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As autonomous vehicles (AVs) become part of urban environments, pedestrian safety and interactions with these vehicles are critical to creating sustainable, walkable cities. Intuitive pedestrian-vehicle communication is essential not only for reducing crash risk but also for supp…

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crossrefApplied Sciences2025-09-30Cited by 3

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crossrefApplied Sciences2026-02-03

The Relationship Between Breakdowns and Production, and the Detection of Breakdown Units in Mining Vehicles Using Machine Learning

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crossrefApplied Sciences2026-02-10

AI-Driven Prediction of Bitumen Content in Paving Mixtures: A Hybrid Machine Learning Model Applied to Salalah, Oman

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Sustainable pavement solutions that lessen the dependency on virgin materials are required due to mounting environmental and economic pressures. Although recycled asphalt concrete (RAC) has structural and environmental advantages, binder heterogeneity and non-linear material inte…

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crossrefApplied Sciences2026-02-28

Comparative Analysis of Machine Learning and Deep Learning Models for Atrial Fibrillation Detection from Long-Term ECG

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Atrial fibrillation is the most prevalent sustained cardiac arrhythmia and a major risk factor for stroke, heart failure, and premature mortality. Automatic detection remains challenging due to the variability of electrocardiogram (ECG) morphology, noise, and the paroxysmal natur…

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crossrefApplied Sciences2025-07-30Cited by 3

The Choice of Training Data and the Generalizability of Machine Learning Models for Network Intrusion Detection Systems

Marcin Iwanowski, Dominik Olszewski, Waldemar Graniszewski, Jacek Krupski, Franciszek Pelc

Network Intrusion Detection Systems (NIDS) driven by Machine Learning (ML) algorithms are usually trained using publicly available datasets consisting of labeled traffic samples, where labels refer to traffic classes, usually one benign and multiple harmful. This paper studies th…

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