**1. Introduction**

With the rapid development of autonomous driving technology, Autonomous Vehicles (AV) have entered the operational stage in the road transport system. It is foreseeable that, in the near future, the proportion of AV will gradually increase. However, extensive autonomous driving is still out of reach. Considering the enormous possession of conventional vehicles, the first possibility of autonomous driving to implement on the road is the mixed traffic flow. This possibility will first appear in the motorway scenario, which is much simpler than urban roads. The mixing of AV and conventional vehicles will definitely have a significant impact on the performance of motorway traffic.

AV refer to the vehicles that can achieve the environment perception, route planning, decision making, and vehicle control functions in a highly intelligent and safe manner

**Citation:** Fang, X.; Li, H.; Tettamanti, T.; Eichberger, A.; Fellendorf, M. Effects of Automated Vehicle Models on the Mixed Traffic Situation on a Motorway Scenario. *Energies* **2022**, *15*, 2008. https://doi.org/10.3390/ en15062008

Academic Editor: Mario Marchesoni

Received: 30 January 2022 Accepted: 7 March 2022 Published: 9 March 2022

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through the advanced on-board sensors, controllers, and actuators. The Society of Automotive Engineers (SAE) divides autonomous driving into six levels from Level 0 to Level 5 according to the need of the amount of driver intervention [1]. Level 0–Level 2 are defined as Advance Driver Assistant System (ADAS) while Level 3–Level 5 are defined as high-level automatic driving system. As high-level autonomous vehicles carry massive electronic devices, they are usually based on electric vehicles [2,3]. Connected and Automated Vehicles (CAV) refer to autonomous vehicles with integrated communication systems and network technologies to realize intelligent information transfer, exchange, and sharing between vehicles and everything (other vehicles, transport infrastructure, passersby, clouds, etc.). CAV have the capability of complex environment perception, intelligent decision making, and collaborative control, which can realize safe, efficient, comfortable, and energy-saving driving.

In this paper, we mainly focus on the Highly Autonomous Vehicles (HAV) and CAV simulation where HAV are defined as autonomous vehicles with Level 3 automation technology introduced in [4]. Vehicles with increasing levels of automation will fuse information from on-board multi-sensors and systems, allowing the vehicle to perceive the surrounding traffic and to locate itself precisely. Meanwhile, systems can enable the piloting of the vehicle with little or no human intervention during highly automated driving. Furthermore, the CAV model in this paper refers to vehicles with dedicated shortrange communication technologies based on highly automated driving function, which allows vehicles to communicate with their surroundings, including infrastructure and other vehicles. In addition, it can provide drivers with real-time information about road and traffic conditions, as well as a wide range of connectivity services.

According to the market forecast of [5], the share of HAV and CAV in new car sales will increase from about 10% in 2025 to about 50% in 2035. Therefore, it is particularly important to evaluate the existing traffic scenario, driven by the huge market prospect.

Vehicle automation and communication technologies are considered promising approaches to improve the efficiency, safety, and environmental protection of traffic systems. Numerous studies have investigated the impacts of autonomous vehicles on traffic with simulation technology. However, the current Traffic Analysis, Modeling, and Simulation (TAMS) tools are not adequate for evaluating CAV or HAV driving behavior. Changes of the driving behavior parameter even had the opposite effect in different microscopic traffic simulation tools [6]. The reasons for this are as follows. First, for the CAV model, most TAMS tools cannot simulate vehicle inter-connectivity, i.e., V2V communication information sharing. Additionally, the majority of driving models are unrealistic, and many existing models require parameter calibration. Refs. [7–9] introduce the approaches to use empirical data to calibrate Wiedemann 99 model in Vissim in order to replicate CAV and HAV driving behavior. This method requires much time for collecting road data, which reduce the cost of modeling but require a lot of effort in training the samples as well as data statistics. In addition, most of them did not systematically evaluate the lateral/longitudinal control model, and [10,11] apply a linearized ACC model to perform speed control while considering only the following distance. To real driving conditions, the driver's desired speed should also be considered as a significant input to the system. Finally, Ref. [12] introduces HAV and CAV simulation models where control strategy is simplified. Although this approach reduces the difficulty of modeling, it does not reflect the actual vehicle driving behavior. In order to realistically reflect the driving behavior of HAV and CAV on the highway, we propose a driving model based on the Highway Chauffeur (HWC) function, which is introduced and defined in [13].
