# Objectively Scoring the Human-Likeness of Artificial Driver Models
## 3 Method
### Strategy for Validating the Method  
Since the proposed metric attempts to quantify the complex construct of human-like behavior in the absence of any official objective truth, validation is required. The validation of the method explores whether the human-like driving scores obtained by the metric correlate with people's subjective ratings. The strategy is based on the assumptions that, first, human-likeness is defined by the ability of subjects to distinguish between artificial and human behavior and second, that if a correlation is found between scores obtained by the metric and subjective ratings, validation demonstrates the ability of the metric to objectively quantify human-likeness. The subjective experiment was inspired by the work of [PERSON] et al. [22], who adjusted the Turing Test to quantify the human-likeness of their proposed methodology in a driving simulator. In order to validate the metric, participants are asked to assess the behavior of vehicles in short videos without knowing if one marked subject vehicle is driven by a real human or an artificial driver. The videos are designed to remove any indication of whether the behavior comes from artificial or real data. After each video, participants were asked to rate the behavior, of one marked vehicle, by the following scale: 1: Completely artificial driving; 2: Somewhat artificial driving; 3: Not sure; 4: Somewhat human-like driving; 5: Completely human-like driving. Based on this, it can be investigated whether the scores given by humans correlate with those of the proposed metric. In addition, it can be evaluated to what extent participants are able to distinguish between artificial and human behavior. For further validation of the method, the metric is applied to some real driving and artificial driving data, assuming that the human-likeness  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Name of Parameter Check** & **Initial** & **Fine-Tuned** \\ \hline KS Longitudinal Vel. & \textgreater{}0.993648 & \textgreater{}0.668406 \\ KS Lateral Vel. & \textgreater{}0.952358 & \textgreater{}0.624929 \\ KS Longitudinal Acc. & \textgreater{}0.780503 & \textgreater{}0.559198 \\ KS Lateral Acc. & \textgreater{}0.926266 & \textgreater{}0.647957 \\ KS Jerk & \textgreater{}0.685024 & \textgreater{}0.511960 \\ Max. Longitudinal Vel. & \textless{}66.67\% & \textless{}66.67\% \\ Max. Lateral Vel. & \textless{}73.33\% & \textless{}83.13\% \\ Max. Longitudinal Acc. & \textless{}64.00\% & \textless{}71.80\% \\ Min. Longitudinal Acc. & \textless{}78.00\% & \textless{}72.00\% \\ Min. Distance to partners & \textless{}84.00\% & \textless{}93.60\% \\ PET * & \textless{}0.64 s & \textless{}0.50 s \\ TET * & \textgreater{}4.96 s & \textgreater{}3.90 s \\ Max. Critical Time Gap * & \textgreater{}6.98 s & \textgreater{}5.40 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Thresholds for measuring human-likeness for different parameters extracted from inD data [35]. Distributions of velocities, accelerations, and jerk are compared using KS statistics. Percent ratios are assigned for maximum values and raw thresholds are assigned for context-free parameters. _Parameters marked with * are calculated context-free._score of real data is significantly higher compared to synthetic behavioral data. The metric includes several aspects that can be tuned, such as how narrowly the range of human similarity is defined or how individual parameters are weighted for the final score. The insights from the survey provide a basis for tuning the metric toward how people would distinguish between artificial and real.