DriveCheck: A Driving Behavior Monitor
Received: 23 September 2025 | Revised: 10 November 2025 | Accepted: 17 November 2025 | Online: 9 February 2026
Corresponding author: Juan Mansilla-Lopez
Abstract
This paper introduces the DriveCheck driving behavior monitor, a web application for the evaluation of risky driving behaviors. The tool uses a random forest supervised learning algorithm to classify normal and risky events based on acceleration, braking, and turning information. Initially, a public Kaggle dataset labeled by driving level was used. Subsequently, a proprietary dataset was collected from 27 real urban trips in Lima, where selected maneuvers (sharp turns, sudden acceleration, and harsh braking) were induced under controlled conditions to calibrate the model. The results showed a risk classification accuracy of 97%, validating the robustness of the model and its applicability in real urban settings (field tests in Lima).
Keywords:
safe driving, risk assessment, behavior analysis, road safetyDownloads
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Copyright (c) 2026 Michael Andre Samuel Cuadros-Ccahuana, Kevin Enrique Servat-Farfan, Pedro Castaneda, Juan Mansilla-Lopez, Alberto Daniel Garcia-Nunez
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