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Classifying Motorcyclist Behaviour with XGBoost Based on IMU Data.

Gerhard Navratil¹, Ioannis Giannopoulos¹

¹Department for Geodesy and Geoinformation, TU Wien, Wiedner Hauptstr. 8-10, 1040 Vienna, Austria.

Sensors (Basel, Switzerland)

|February 10, 2024

Summary

This study shows Inertial Measurement Unit (IMU) data can classify motorcyclist behaviour with 80% accuracy, aiding environmental analysis. Overtakes were the only exception, proving difficult to detect reliably.

Keywords:

IMU XGBoost behaviour classification experiment motorcycle navigation traffic

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Area of Science:

* Human-computer interaction
* Transportation engineering
* Data science

Background:

* Monitoring human behaviour during navigation offers insights into environmental conditions.
* Motorcyclists require careful observation of road surface and traffic, making their behaviour a key indicator.
* Spatial and temporal analysis benefits from understanding movement patterns.

Purpose of the Study:

* To assess the sufficiency of Inertial Measurement Unit (IMU) data for classifying motorcyclist behaviour.
* To explore the potential of IMU data for spatial and temporal analysis of road environments.
* To evaluate the effectiveness of machine learning models in behaviour classification.

Main Methods:

* An experiment was conducted using Inertial Measurement Unit (IMU) sensors to collect data from motorcyclists.
* XGBoost machine learning algorithm was employed for behaviour classification.
* Data analysis focused on identifying distinct motorcyclist behaviours during navigation.

Main Results:

* The XGBoost model successfully classified four out of five distinct motorcyclist behaviours.
* An overall classification accuracy of approximately 80% was achieved.
* Overtake manoeuvres were identified as challenging to classify reliably using IMU data.

Conclusions:

* IMU data is a viable source for classifying motorcyclist behaviour, with high accuracy for most actions.
* This classification enables valuable spatial and temporal analysis of the road environment.
* Further research is needed to improve the detection of complex manoeuvres like overtaking.