# Improving Machine Learning Identification of Unsafe Driver Behavior by Means of Sensor Fusion
## 2 Related Work  
Machine learning has been widely used to classify driving styles. Recent reviews showed how the most-used tools are neuron fuzzy logic (NF), support vector machines (SVM), and artificial neural networks (ANNs) [4; 5].  
Fuzzy logic has been successfully used in different driving style classification (e.g., aggressive, calm, comfortable, normal, etc.), using different signals from in-vehicle sensors such as acceleration, braking, steering, and speed [7; 8]. [PERSON] et al. in 2014 presented a fuzzy logic driving style recognition system based on speed, deceleration, acceleration, and time gap between the car in front [9].  
Several works made use of SVM models in driving behavior classification. For example, [PERSON] et al. in 2013 presented an SVM model to identify drivers during braking, accelerating and turning. As input features they used acceleration, deceleration, and angular speed coming from inertial sensors built-into the car [10]. [PERSON] et al. in 2016 trained an SVM model using acceleration, throttle position, engine RPM, and inertial sensors as data features. The model was then evaluated in classifying 14 drivers. [11]. In 2017 [PERSON] et al. used an SVM model to classify normal and aggressive driving styles using only in-vehicle sensors data such as speed and throttle position [12]. In the same year, [PERSON] et al. proposed an SVM model trained on top of inertial sensor data collected by means of a smartphone to classify abnormal driving pattern, while [PERSON] et. al classified driving events, such as braking, turning, and lane changing by means of smartphone sensors [13]. Finally, in 2018 [PERSON] et al. presented an SVM-based system called Amelio-Rater, which continuously records data from smartphone sensors and attributes a driving rate to the driver [14].  
A subset of machine learning works make use of ANNs to resolve driving styles classification problems. For instance, [PERSON] et al. in 2015 used an ANN aimed to classify aggressive, normal, and quiet driving styles using speed, acceleration, and RPM data collected from in-vehicle sensors [15]. [PERSON] et al. in 2017 used different classifiers, including ANNs, to identify abnormal driving behaviors starting from smartphone sensors' signals [16]. [PERSON] et al. in 2018 presented an ANN trained by means features extracted by acceleration, pedal angle, and speed signals gathered from a car simulator to classify drivers into aggressive, normal, and calm [17]. Recently [PERSON] et al. used a convolutional neural network to classify driving styles as: (i) normal; (ii) aggressive; (iii) distracted; (iv) drowsy; or (v) drunk starting from in-vehicle sensors [18]. Similarly, [PERSON] et al. implemented a convolutional neural network which makes use of in-vehicle sensor data to distinguish the driver's own style [19].  
To sum up, these studies provide a general overview of the use of machine learning in driving behavior analysis; however, none of these reported an example of sensor fusion to improve unsafe driving behavior through classification. In particular, in our work we investigated the use of different external sensors, such as a gyroscope and a magnetometer, to increase classification performance while testing it against a ground truth obtained by means of an objective methodology. To the best of our knowledge, only [PERSON] et al. in 2015 proposed a preliminary study which fused together in-vehicle sensors' signals with those obtained from inertial sensors. In particular, they made use of a hardware tool which integrated GPS, accelerometers, and a CAN bus reader to distinguish tracks collected by ten drivers whom were asked to drive the same route twice, in a normal and aggressive way respectively [20]. Despite this attempt to merge several sensors, the work proposed by [PERSON] et al. did not exploit gyroscope and magnetometer signals, did not make use of machine learning techniques, and did not compare the obtained results with an objective ground truth.