# Non-Compliant Behaviour of Automated Vehicles in a Mixed Traffic Environment
## 4 Experiment and Results  
### Scenario Setup  
To assess the effects of the developed automated driving functions on traffic flow, we investigated a highway on-ramp segment and focused on the specific driving regulations that influence AV driving functionalities. The analyzed segment had three lanes on the main carriageway and one merging lane with a length of 250 m. Loop detectors were placed at multiple cross-sections (CS). Additional infrastructure-related properties (e.g., position of speed decision points, lane width, lane-change restrictions for varying vehicle types) were modeled as described in [PERSON] and Vortisch [43]. In addition, AV decision points were modeled, which allowed the AV driver behavior to be adapted. All infrastructure-related settings of the defined simulation model are illustrated in Figure 8.  
Figure 7: State machine of longitudinal controller.  
For human-driven vehicles, we used the internal lane-change model provided in Vissim and the Wiedemann99 car-following model [30]. To calibrate the human driver behavior, a 3-step calibration process was applied utilizing single vehicle cross-section data and segment-based trajectory data according to [PERSON] et al. [44]. Any adaptations in human driver behavior due to the existence of automated vehicles [45] were not considered. In addition, we obtained the desired speed distributions for passenger cars and trucks from single vehicle data from multiple cross-sections on Austrian motorways with a speed limit of 130 km/h. A truck rate of 10% was assumed. The simulation duration was set to 3 h, and every simulation scenario was simulated with five random seeds. Since we wanted to observe varying traffic states, the traffic volume was increased from 550 veh/h to 6050 veh/h in a five-minute cycle. After two thirds of the simulation time, the traffic volume reached the maximum level and was decreased again. This approach also allowed us to investigate the dissolution process of congested traffic.  
In total, 13 different simulation scenarios are presented. Besides the base case, which did not consider any automated vehicles, we analyzed 12 scenarios with two different AV penetration rates and six different driving regulations, reflected by varying AV driving parameters. The AV penetration rates were defined based on an internal workshop, in which we reviewed various literature sources [46, 47]. The aim of the study was to highlight potential differences between different penetration rates. However, these theoretical values should not be considered as a predicted penetration rate within a certain time span. Regarding driving regulations, we distinguished between three varying permissible following time headways. For each, it was assumed that non-compliant trajectories were either prohibited or permissible. Time headways of 0.9 s and 1.8 s corresponded to risky and conservative behaviors, respectively. As both values are rather unlikely to be defined in future driving regulations, an average time headway of 1.35 s was also investigated. An overview of the simulated scenarios can be seen in Table 5.  
Figure 8: Modeled on-ramp segment indicating infrastructure-related settings, the position of loop detectors, and the position of desired speed decision points.