# Hybrid Representation of Sensor Data for the Classification of Driving Behaviour
## 1 Introduction  
The development of computational tools using sensor measurements for the analysis of driving behaviour raises several challenges from a machine learning and signal processing perspective. These challenges include the formulation of classification schemes capable of handling time-series of sensor measurements and the creation of sufficiently diverse datasets, encompassing data of various driving styles. The first approaches for driving behaviour analysis employed rules, which were usually based on empirically defined thresholds. [PERSON] et al. [1] defined such thresholds for acceleration, braking, and turning. They developed a tool using data obtained from mobile phone sensors and cameras, in order to provide feedback in the form of scores and alerts. [PERSON] et al. [2] used data obtained from telemetry devices and discretized speed and acceleration measurements into a finite risk space to provide personalized driving risk assessment. These rule-based approaches performed well in some setups, assuming acalibration stage to adjust proper threshold values. This calibration stage could be sensitive to certain factors, including vehicle type, road conditions, and traffic. Another approach is to use handcrafted features within a traditional classification context. [PERSON] et al. (2018) proposed a driver recognition method, which uses statistical features reflecting the frequency of acceleration, braking, and turning events, in a framework of either unsupervised learning (\(k\)-means clustering) or supervised learning (support vector machines-SVMs). [PERSON] et al. (2018) calculated the statistical features of 3-axis accelerometer measurements to classify driving behaviour as normal or aggressive. Following sequential forward feature selection (SFFS), they ended up with seven features, which were used by a \(k\)-nearest neighbors (\(k\)-NN) classifier, resulting in 100% accuracy. At this point it should be noted that the authors admit that the samples of their dataset might be too easily separable. [PERSON] et al. (2018) performed a comparative study, which concluded in favour of random forests with Bayesian optimization. In their experiments, they used speed and acceleration measurements. [PERSON] et al. (2018) combined various sensor measurements and applied probabilistic automata, as well as labelled directed graphs, in order to model driving behaviour. [PERSON] et al. (2018) considered certain driving maneuvers and defined time-series of features derived from sensor measurements. Following a feature selection stage, they used a random forest for driving behaviour classification. In a similar spirit, [PERSON] and [PERSON] (2018) proposed a system for driving risk assessment using features obtained by accelerometers and gyroscopes, standard classifiers such as SVMs, NNs, decision trees, random forests, and \(k\)-star, as well as fuzzy logic. [PERSON] et al. (2018) defined short-term and long-term handcrafted features, using acceleration and speed measurements obtained using telematics sensors and experimented with standard classifiers: \(k\)-NN, SVM and decision trees. The highest classification accuracy was obtained by means of decision trees. In general, handcrafted features are'manually' engineered and their performance may vary, depending on the task at hand. Beyond handcrafted features and standard classifiers, some recent works employed more advanced neural network (NN) architectures to differentiate between driving styles. [PERSON] et al. (2018) employed convolutional neural networks (CNNs) on image-like representations of measurements, obtained by telematics sensors. [PERSON] et al. (2018) applied RNNs on the UAH dataset ([PERSON] et al., 2018), whereas [PERSON] et al. (2018) applied two RNN-based variants, long short-term memory (LSTM) ([PERSON] et al., 2018), and gated recurrent unit (GRU) networks ([PERSON] et al., 2018; [PERSON] et al., 2018) on a dataset comprising of signals acquired by means of telematics sensors. This later work was further developed by [PERSON] et al.