# Automatic Generation System for Autonomous Driving Simulation Scenarios Based on PreScan
## 1 Introduction  
The emergence of Autonomous Vehicles (AVs) is rapidly advancing, driven by breakthroughs in sensors, artificial intelligence, communications, and positioning technologies. Paramount among the challenges in this domain is the assurance of safety in autonomous driving [1]. Conducting exhaustive tests using comprehensive road scenarios is crucial for ensuring the anticipated safety standards. Traditional road scenario testing involves assessing vehicle functions in simulated real-world scenarios or under specific working conditions. However, the traditional approach is marred by high testing costs, extended durations, limited scope in realizable scenarios, and low reusability of these scenarios. In contrast, simulation-based scenario testing offers an effective solution, overcoming the limitations of traditional methods while concurrently enhancing the safety of testing procedures [2]. Currently, researchers have proposed three primary methods of scenario generation (SG): algorithmic modeling, data driven, and standard specifications [3, 4]. Algorithmic modeling primarily employs the theoretical foundation of algorithms and scenario modeling to create marginal and diverse scenarios. Among them, the scenario combination test method [5, 6, 7] arranges and filters scenario elements, enabling the rapid generation of a multitude of scenarios. Some studies specifically focus on generating scenarios for automotive systems within simulated road environments [8, 9], and some generate corner cases based on interactions with other vehicles [10, 11]. While algorithmic modeling can produce numerous scenarios, the screening process, which relies on personal experience and judgment, necessitates significant effort to stiff out useful scenarios from the large pool of possibilities. Data driven is the generalization and reconstruction of real accident scenarios to generate simulation scenarios [12]. This includes methods for collecting data independently to extract key information and subsequently generating scenarios [13; 14], as well as approaches for extracting scenario elements from accident databases to create scenarios [15; 16]. This methodology faces challenges, including the complexity of data collection and processing, which often leads to inherent limitations in the generated scenarios. Moreover, the approach's generalization capability may be limited, potentially restricting its broader applicability. Standard specifications is to generate scenarios according to regulatory requirements. In order to standardize scenario testing, develop test contents, design test cases, and improve the test scenario library, the proposed standards and regulations will help realize the systematic, effective, and rapid testing of AVs safety. Numerous scenario projects have been proposed in recent times, including Pegasus [17], cooperative intelligent transportation system-vehicular communication application layer specification and data exchange standard (Phase I) [18], euroFOT [19], and AdaptVe [20]. It provides a scenario basis for functional verification. However, when testing simulation scenarios based on standard regulations, generalizing them into multiple similar scenarios is necessary. This process often requires repeatedly building scenarios using the existing simulation data. Such an approach faces challenges, including low automation and the labor-intensive nature of manual SG. This can lead to errors, especially when dealing with a large number of test scenarios. Commonly used scenario building software include PreScan 2021.1.0 [21], VTD 2021.4 [22] and Carmaker 10.0 [23]. PreScan represents an integration of scenario building and control modeling capabilities within the realm of autonomous driving simulations. Distinguished from other scenario building software, PreScan offers a comprehensive library of fundamental components. This library includes a diverse of vehicles, traffic signs, pedestrians, and roads, providing users with extensive resources for scenario creation.