Low-Cost System for Automatic Recognition of Driving Pattern in Assessing Interurban Mobility using Geo-Information

2026-04-16

AI Summary

This study addresses the widespread lack of effective driving behavior assessment systems in current vehicles, which hinders timely identification of hazardous driving and contributes to traffic accidents and congestion. To tackle this issue, the authors propose a low-cost, aftermarket embedded solution that integrates in-vehicle physical sensors with geographic and temporal data to construct an artificial neural network (ANN) model for real-time driving style recognition and voice-based alerts. A key innovation lies in the incorporation of geospatial-temporal features, which substantially enhances classification performance: the system achieves an average accuracy of 83% in distinguishing among three driving styles and improves binary classification accuracy (normal vs. aggressive) to 92%, representing a 13% gain over baseline methods that do not incorporate geographic information.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Remote Sensing / Geospatial AI

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Mobility in urban and interurban areas, mainly by cars, is a day-to-day activity of many people. However, some of its main drawbacks are traffic jams and accidents. Newly made vehicles have pre-installed driving evaluation systems, which can prevent accidents. However, most cars on our roads do not have driver assessment systems. In this paper, we propose an approach for recognising driving styles and enabling drivers to reach safer and more efficient driving. The system consists of two physical sensors connected to a device node with a display and a speaker. An artificial neural network (ANN) is included in the node, which analyses the data from the sensors, and then recognises the driving style. When an abnormal driving pattern is detected, the speaker will play a warning message. The prototype was assembled and tested using an interurban road, in particular on a conventional road with three driving styles. The gathered data were used to train and validate the ANN. Results, in terms of accuracy, indicate that better accuracy is obtained when the velocity, position (latitude and longitude), time, and turning speed for the 3-axis are used, offering an average accuracy of 83%. If the classification is performed considering just two driving styles, normal and aggressive, then the accuracy reaches 92%. When the geo-information and time data are included, the main novelty of this paper, the classification accuracy is improved by 13%.

Problem

• driving pattern recognition
• interurban mobility
• driver assessment system
• geo-information
• road safety

Innovation

• driving pattern recognition
• geo-information
• artificial neural network
• low-cost system
• interurban mobility