## 3 Driving Behavior Characterization for Fuel-Consumption Scenarios
### Selection of Relevant Features  
The dataset used in our experiments was collected using an instrumented car traveling a fixed route around the city of Istanbul, as already introduced in Section 2.1. The route is little over 25 km and lasts about 40 min, depending on weather and traffic conditions. It includes different types of road sections: city, very busy city, highway, and a university campus. With the aim of minimizing the impact of environmental variations, all of the selected driving sessions were conducted in a short period of time, from August to October, and during the central part of the day, between 11 a.m. and 4 p.m. The driver population was composed of 20 drivers, 17 male and three female, whose ages ranged from 21 to 61. This is a reduced subset of a more comprehensive data collection (i.e., about 100 drivers and a single trip per driver) provided by the \"Drive-Safe Consortium\" [45]. Because instant fuel consumption was not available within the dataset, we developed a model of the Uyanik car and used the GT-Suite tool to obtain fuel consumption data during the driving sessions [54]. Afterwards, we computed two types of features: mean values and variances of the Uyanik signals. The whole set of time series, more than 30 independent signals, was evaluated, including CAN-bus data, pedal pressure sensors, a laser scanner, and IMU unit readings. In particular, the treatment of the X-axis acceleration variables was divided into two parts, positive and negative values, since they have different consequences on fuel consumption. In fact, negative instantaneous values are associated with zero consumption. Subsequently, the features that provide the highest relationship with fuel consumption were selected, while the irrelevant or redundant features were discarded. We computed both the Pearson correlation coefficients (PCCs) and the \(p\)-values of every feature. The former provides a measure of the relevance of each feature, while the latter is used for testing the hypothesis of no correlation (i.e., the probability of obtaining a correlation as large as the observed value by random chance, when the true correlation is zero). The features were computed over 8 s windows (i.e., 256 samples) with a 4 s shift. That is to say, the overlapping between consecutive windows is 4 s (i.e., 128 samples). The format of the windows was selected by exhaustively analyzing the consequences of both the window size and the shift on the PCC of the most relevant features. Table 2 summarizes the set of low level signals (i.e., time series) that exhibit the strongest correlation with fuel consumption. Moreover, the \(p\)-values are less than 0.0001 for almost all of the features included in this table, thus guaranteeing the reliability of the correlations. The exceptions are the mean and variance of the negative X-axis acceleration, whose \(p\)-values are close to 0.05. These features were not selected because of their low PCCs. The features that present the strongest correlations with fuel consumption are remarked in bold in Table 2. Four mean values (i.e., VS, PGP, ERPM, and GP) and the PGP variance have a strong \begin{table}
\begin{tabular}{l l c c} \hline \hline
**Measurement Units** & **Signals (Time Series)** & **PCC: Mean** & **PCC: Variance** \\ \hline \multirow{4}{*}{CAN-bus} & Vehicle speed (VS) & **0.59** & 0.15 \\ \cline{2-4}  & Percent gas pedal (PGP) & **0.63** & **0.58** \\ \cline{2-4}  & Engine RPM (ERPM) & **0.66** & 0.18 \\ \hline \multirow{2}{*}{
\begin{tabular}{l} Pressure \\ sensors \\ \end{tabular} } & Brake pedal pressure (BP) & \(-\)0.35 & \(-\)0.23 \\ \cline{2-4}  & Gas pedal pressure (GP) & **0.52** & 0.20 \\ \hline \multirow{4}{*}{IMU unit} & Positive X axis acceleration & 0.32 & 0.25 \\ \cline{1-1}  & (Pos XACC) & & \\ \cline{1-1} \cline{2-4}  & Negative X axis acceleration & \(-\)0.17 & \(-\)0.11 \\ \cline{1-1}  & (Neg XACC) & & \\ \hline \hline \end{tabular} The boldface PCCs correspond to the strongest correlations.  
\end{table}