DriveCheck: A Driving Behavior Monitor

Engineering Technology & Applied Science Research 2026 Open access

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Authors: Michael Andre Samuel Cuadros-Ccahuana, Kevin Enrique Servat-Farfan, Pedro Castañeda, Juan Mansilla-Lopez, Alberto Daniel Garcia-Nunez
DOI: 10.48084/etasr.15030
Cited by: 0
References: 23

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).

Fields of Study & Subjects

Traffic and Road Safety IoT and GPS-based Vehicle Safety Systems Human-Automation Interaction and Safety

Publication details

Journal / source
Engineering Technology & Applied Science Research
Volume
16
Issue
1
Pages
32506–32513
Publication date
9 February 2026
Language
EN