Driver Behavior Soft-Sensor Based on Neurofuzzy Systems and Weighted Projection on Principal Components

TL;DR

This paper presents a real-time driver behavior classification soft-sensor using neurofuzzy systems and principal component analysis, leveraging existing vehicle sensors to enhance safety without additional hardware costs.

Contribution

Develops a novel neurofuzzy-based soft-sensor that classifies driver behavior using only vehicle-internal sensor data, enabling cost-effective safety improvements.

Findings

High classification accuracy demonstrated

Effective real-time behavior detection achieved

Comparable or superior to classical classifiers

Abstract

This work has as main objective the development of a soft-sensor to classify, in real time, the behaviors of drivers when they are at the controls of a vehicle. Efficient classification of drivers' behavior while driving, using only the measurements of the sensors already incorporated in the vehicles and without the need to add extra hardware (smart phones, cameras, etc.), is a challenge. The main advantage of using only the data center signals of modern vehicles is economical. The classification of the driving behavior and the warning to the driver of dangerous behaviors without the need to add extra hardware (and their software) to the vehicle, would allow the direct integration of these classifiers into the current vehicles without incurring a greater cost in the manufacture of the vehicles and therefore be an added value. In this work, the classification is obtained based only on speed, acceleration and inertial measurements which are already present in many modern vehicles. The proposed algorithm is based on a structure made by several Neurofuzzy systems with the combination of projected data in components of various Principal Component Analysis. A comparison with several types of classical classifying algorithms has been made.