# Investigating a Toolchain from Trajectory Recording to Resimulation
## 1 Introduction  
An increasingly diverse range of transportation options and increasing traffic densities present new challenges for road traffic safety. Consequently, there is a growing effort to implement new automated driving features and assistance systems, decreasing the likelihood of accidents due to human errors [1]. The development of these systems requires a significant number of test kilometers for training and validation. In this context, the kilometers required for testing an automated driving function were estimated. To ensure that every situation relevant to the driving function is encountered at least once with a 50% probability, approximately 2.1 billion kilometers would be needed [2]. One opportunity to minimizethe required test kilometers is realistic microscopic traffic simulations. These traffic flow simulations are used to represent the behavior of recorded road traffic participants via driver types and behavioral models. There exist several traffic flow simulation environments that enable the representation and prognosis of traffic scenarios. In this context, the simulation environment PTV Vissim should be mentioned, which is, e.g., used to simulate traffic on intersections considering traffic lights and pedestrians [3]. Aimsun is another simulation environment [4] that primarily represents accident-free traffic. A brief overview of traffic flow simulation environments can be found in [5]. Furthermore, the simulation environment SUMO (Simulation of Urban MObility) [6] is a microscopic open-source traffic flow simulation without accidents. Specific behavior models are implemented in SUMO, such as an Artificial Neural Network (ANN)-based vehicle gap acceptance decision approach [7]. SUMO can be employed to analyze road networks that can be, e.g., generated via perception algorithms like YOLO5 (you only look once) [8]. OpenPASS is based on SUMO and optimized to represent critical scenarios via behavioral models (see, e.g., [9]). Thus, it is suitable to virtually analyze automated driving assistance systems (ADAS) [10]. Behavioral models can also be based on so-called stochastic cognitive models (SCM), which have been applied to highway traffic [5]. The developments in the openPASS simulation environment are being driven forward by Original Equipment Manufacturer (OEMs) in the automotive industry, such as Mercedes Benz AG, VW, and BMW, but also by Tier 1, such as Robert Bosch AG, and are partly incorporated into the simulation environment made available as open source. Solutions have been presented in the past to simulate highway car traffic on linear roads and validate behavior models based on data (see, e.g., [11; 12]). More complex scenarios, such as inner-city car traffic involving street crossings, have been studied by [13]. They simulate vehicle traffic in a specific area and account for the effects of traffic lights; however, Vulnerable Road Users (VRUs) are not included in the analysis. Validation of the presented simulation environments is a crucial step for reliable prediction. This is typically conducted via video recordings where trajectories are extracted for vehicles and other road users. Recorded trajectories can be extracted with the help of Machine Learning (ML)-based motion tracking methods. An overview of existing motion tracking methods is given in [14; 15; 16]. Instead of optical sensors, e.g., Light Detection and Ranging (LiDAR) sensors can be employed to record traffic scenarios in transportation infrastructure [17]. This was demonstrated to identify pedestrians that aim to cross a road (see e.g., [18; 19]). LiDAR can also be employed in combination with optical sensors to record the street scene [20]. The comparison of recorded and simulated data and trajectories is commonly employed to validate microscopic simulations. In existing literature, Automated Driving Functions (ADF) are virtually implemented for crash scenarios and compared with real-world data (see e.g., [10]).