# Non-Compliant Behaviour of Automated Vehicles in a Mixed Traffic Environment
## 2 Related Literature
[24] and [PERSON] et al. [25] proposed a mixed traffic simulation framework using PTV Vissim, which incorporates detailed vehicle lane-change and car-following dynamics with different connected automated vehicle behaviors. The models were implemented using the DLL-interface provided by Vissim. Besides human-driven vehicles, they considered highly automated vehicles and connected automated vehicles. Through increased uniformity in the speed distributions, it was possible to harmonize traffic flow by means of vehicle connectivity. [PERSON] and [PERSON] [26] studied the coexistence of varying AV levels with human-driven vehicles. Their simulations were conducted in SUMO using different car-following models for AVs and CAVs. The results indicated that traffic efficiency improved as the share of HDVs declined and the proportion of AVs and CAVs rose, while low AV penetration rates yielded only minimal improvements. Although some of the previous studies analyzed different following time headway parameters, they did not consider driving regulations or any other legal aspects for automated driving. In this regard, only a small number of studies are available. Some researchers considered newly developed control strategies for traffic management centers, which were assumed to reflect the role of authorities. In [PERSON] et al. [27], a level of aggressiveness was dynamically assigned to automated vehicles based on traffic state parameters. To change the level of aggressiveness, car-following parameters were adapted for AVs. With the proposed approach, the performance of mixed traffic was effectively optimized. A lane control signalization framework for connected automated vehicles (CAVs) was proposed in [PERSON] et al. [28]. In addition, they implemented a platooning algorithm for longitudinal behavior using the DLL interface provided by PTV Vissim. It was shown that the presented lane control strategy increased throughput by up to 18.4% compared with traditional lane control signalization. To improve bottleneck capacity, a traffic control framework termed variable speed release (VSR) was developed in [PERSON] et al. [29]. For simulating human-driven and automatedvehicles, the Wiedemann99 model with varying parameter sets was used. The simulations showed that the response time could be decreased in a CAV environment, which led to a reduced probability of traffic breakdown. To define safety requirements for automated vehicles, [PERSON] et al. [4] compared four different reaction models and investigated vehicles during cutting-in situations. These reaction models exhibited similar behavior to the car-following models used in TFS when deceleration by the following vehicle was required. Since anticipation of upcoming situations is key to avoiding emergency situations while retaining traffic flow, a model based on fuzzy logic (Fuzzy Safety Model) was proposed to amend UN Regulation 157. [PERSON] et al. [32] developed a theoretical model to assess the capacity of a weaving segment for mixed traffic. The model was evaluated and compared with simulation results obtained from a SUMO simulation. They showed that their theoretical model could estimate traffic capacity well, as the difference from the simulation results was below 10%. In their results, traffic capacity increased significantly with increased penetration rates, as they parameterized the reaction time of AVs with 0.5 s, while the human-driven vehicles were modeled with a reaction time of 1.5 s.  
While the studies discussed justify the general use of TFS to analyze the effects of automated driving, several aspects remain unaddressed in previous research. As noted by [PERSON] et al. [6], most studies apply car-following and lane-change models originally developed to represent human driver behavior. While this is a valid approach to analyze the overall impact of adapted AV driving behavior on traffic flow, it does not allow for the investigation of specific AV driving logics or the validation of underlying model assumptions.