# Objectively Scoring the Human-Likeness of Artificial Driver Models
## 5 Results
### Case Study: Exemplary Application of the Method for Model Improvement
\begin{table}
\begin{tabular}{c l l l l l} \hline
**Parameter** & & \multicolumn{2}{l}{**Real Data:**} & \multicolumn{2}{l}{**Syn Data:**} & \multicolumn{2}{l}{**Real Data:**} & \multicolumn{1}{l}{**Syn Data:**} \\  & & **Macroscopic** & **Macroscopic** & **Situational** & **Situational** \\ \hline Longitudinal Vel. [m/s] & mean & 7.18 & 3.38 & 5.71 & 3.48 \\  & std & 5.24 & 3.92 & 4.68 & 3.48 \\  & min & \(-\)4.35 & 0.00 & \(-\)0.21 & 0.00 \\  & max & 27.48 & 19.05 & 20.52 & 12.34 \\ \hline Lateral Vel. [m/s] & mean & 0.04 & 0.03 & 0.04 & 0.14 \\  & std & 0.19 & 0.15 & 0.26 & 0.25 \\  & min & \(-\)2.87 & \(-\)0.77 & \(-\)0.99 & \(-\)0.07 \\  & max & 10.89 & 1.45 & 1.07 & 0.85 \\ \hline Longitudinal Acc. [m\({}^{2}\)/s] & mean & 0.06 & 0.06 & \(-\)0.08 & \(-\)0.47 \\  & std & 0.87 & 1.02 & 1.01 & 2.72 \\  & min & \(-\)6.25 & \(-\)9.58 & \(-\)4.34 & \(-\)9.58 \\  & max & 6.54 & 5.25 & 4.72 & 2.61 \\ \hline Lateral Acc. [m\({}^{2}\)/s] & mean & \(-\)0.04 & 0.13 & \(-\)0.08 & 0.65 \\  & std & 0.61 & 0.78 & 0.89 & 1.14 \\  & min & \(-\)5.57 & \(-\)3.97 & \(-\)4.62 & \(-\)0.20 \\  & max & 4.98 & 4.78 & 4.12 & 3.27 \\ \hline Jerk [m\({}^{3}\)/s] & mean & \(-\)0.05 & 0.03 & 0.06 & 0.14 \\  & std & 0.84 & 2.91 & 1.05 & 12.20 \\  & min & \(-\)20.35 & \(-\)21.40 & \(-\)6.96 & \(-\)18.17 \\  & max & 16.16 & 220.04 & 16.16 & 220.04 \\ \hline \end{tabular}
\end{table} 
Table 5: Analysis of the value distribution for dynamic parameters for model behavior and real humans on a macroscopic level (left) and scenario-specific under situational conditions (right). As shown in Table 5, the jerk parameter stands out in particular. The maximal values measured in artificial behavior are much larger compared to those observed by human drivers. It should be noted that the jerk values observed by real humans are determined by tracking algorithms that process video data from drones. Since the open-source real traffic dataset is preprocessed, it is not known to what extent the values in this data are smoothed. However, the jerk values of the driver model show significantly high maximum values, which should be further investigated. Therefore, some trajectories were extracted and analyzed. Figure 9 shows some example trajectories that illustrate the _jerking problem_ that occurs when switching between driving maneuvers. Since the driver model used is based on heuristic decision-making, the temporal behavior and motion are more discrete compared to humans. However, from a subjective visual point of view, the _jerking problem_ is not perceptible, as shown by the survey, and therefore not critical for driver models in the context of DiL traffic simulation. Further interesting insights are provided by the comparison between macroscopic behavior parameter distribution (left) and situational behavior (right) in Table 5. When considering longitudinal acceleration, for example, one can observe that distributions are in line with human value ranges in a macroscopic perspective but not when comparing the parameter situationally. This shows that macroscopic comparison can result in misleading conclusions regarding the extent of human-like behavior. In summary, the case study demonstrates the potential of the method to reveal model weaknesses and enable better model parameterization. Figure 8: Analysis to identify which parameters mostly fail when comparing artificial and human behavior. Figure 9: Exemplary trajectory of two vehicles showing significant high jerk values when approaching an intersection.