# Objectively Scoring the Human-Likeness of Artificial Driver Models
## 5 Results
### Case Study: Exemplary Application of the Method for Model Improvement  
The proposed method is characterized by two main aspects. First, by using various parameters involving functional, dynamic, and interactive behavior, the multi-modality of driving behavior is objectively measurable at different levels. Secondly, behavior is assumed to be conditional and compared within a situational context instead of comparing parameters on a macroscopic level. Therefore, this approach provides a high level of transparency and enables targeted model improvement. How the proposed method can be used for model improvement is presented in the following case study. The overall scores measured for the artificial driver model in Figure 7 show a mean human-likeness score of 78.89% showing a variance of 33.33 on interSection 1. The scoring method enabled for quantifying model behavior among multiple situations measured by various parameters. Based on the proposed method, we are able to address the following questions to enable model improvement:  
* In which scenarios does the model show less human-like behavior? * Which parameters mostly fail when comparing the model to human behavior? * Why do those parameters fail; how does the distribution of parameters distinguish when comparing model behavior and human behavior? Based on the correlation analysis presented in Table 4, the critical time gap was found to have a high negative correlation with the subjective ratings of human-like driving obtained through the survey. Therefore, the critical time gap value is further investigated. In the real traffic data, the value was determined to be 6.98 seconds, whereas the same parameter in the synthetic data was determined to be 10.05 seconds at InterSection 1 and 9.98 seconds at InterSection 2. This shows a significant difference in behavior and needs to be improved in the driver model. To further investigate in which situations model behavior mostly differs from that of humans, the distribution of failing scenario parameters is investigated. Considering all context parameters and the distribution of failed parameter checks, the following conclusions could be drawn. Vehicles are more likely to fail:  
Figure 7: Results for human-like scores for real and synthetic datasets: with tuned thresholds and weights (**left**) and initial setting (**right**). * In the maneuver states: approaching, accelerating, decelerating, or stopping;
* Giving right-of-way;
* In the object-related maneuver: approaching or waiting for a gap;
* Interacting with fewer partner vehicles;
* Driving in lower intersection density. The calculated behavior parameters of the synthetic data are analyzed to identify which behavior parameters mostly fail. Illustrated in Figure 8, the five most often failing behavior parameter checks were identified to be: KS Longitudinal Vel., KS Lateral Vel., KS Longitudinal Acc., KS Lateral Acc., and KS Jerk. In the next step, the value distributions of the extracted parameters can be analyzed. For this purpose, the distribution of the dynamic parameters extracted from real data is compared with the dynamic parameters of the artificial samples showing a human-likeness score of less than 70%. The distributions are extracted on a macroscopic level and situationally under the scenario conditions identified above as causing most of the failures. All distributions are summarized in Table 5.