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The advancement of autonomous driving technology has had a significant impact on both transportation networks and people’s lives. Connected and automated vehicles as well as the surrounding driving environment are increasingly exchanging information. The traditional open road test or closed field test, which has large costs, lengthy durations, and few diverse test scenarios, cannot satisfy the autonomous driving system’s need for reliable and safe testing. Functional testing is the emphasis of the test since features such as frontal collision and traffic sign warning influence driving safety. As a result, simulation testing will undoubtedly emerge as a new technique for unmanned vehicle testing. A crucial aspect of simulation testing is the creation of test scenarios. With an emphasis on the map generating method and the dynamic scenario production method in the test scenarios, this article explains many scenarios and scenario construction techniques utilized in the process of self-driving car testing. A thorough analysis of the state of relevant research is conducted, and approaches for creating common scenarios as well as brand-new methods based on machine learning are emphasized.

This survey explores the evolution of test scenario generation for autonomous vehicles (AVs), distinguishing between non-adaptive and adaptive scenario approaches. Non-adaptive scenarios, where dynamic objects follow predetermined scripts, provide repeatable and reliable tests but fail to capture the complexity and unpredictability of real-world traffic interactions. In contrast, adaptive scenarios, which adapt in real time to environmental changes, offer a more realistic simulation of traffic conditions, enabling the assessment of an AV system’s adaptability, safety, and robustness. The shift from non-adaptive to adaptive scenarios is increasingly emphasized in AV research, to better evaluate system performance in complex environments. However, generating adaptive scenario is more complex and faces challenges. These include the limited diversity in behaviors, low model interpretability, and high resource requirements. Future research should focus on enhancing the efficiency of adaptive scenario generation and developing comprehensive evaluation metrics to improve the realism and effectiveness of AV testing.

With the growing penetration of renewable energy sources in power systems, it becomes increasingly important to characterize their inherent variability and uncertainty. Scenario generation is a key approach to provide a series of possible power scenarios in the future for the system planner and operator to make decisions. In this paper, a data-driven method is presented for renewable scenario generation using stable and controllable generative adversarial networks with transparent latent space (ctrl-GANs). The machine learning based algorithm can capture the nonlinear and dynamic renewable patterns without the need for modeling assumptions and complicated sampling techniques. The orthogonal regularization and spectral normalization are adopted to improve the training stabilization of the GAN model. To control the generation process, a relationship is built between features of the generated scenarios and latent vectors on the manifold. Moreover, several new metrics for GANs are used to evaluate the quality of the scenarios. The proposed approach is applied to generate realistic time series data of wind and photovoltaic power. The results demonstrate that our method has a better performance on numerical stabilization and is able to control the generation process with latent space.

As automated vehicles move from controlled environments to unpredictable real-world roads, scenario-based testing has become the cornerstone of safety validation. In recent years, substantial progress has been made in scenario representation standards and generation methodologies. However, integrating scenario generation, standards-aligned packaging, validation, curation, and structured querying into a reproducible end-to-end lifecycle remains challenging in practice. This work presents a reproducible reference architecture for Scenario Databases (SCDBs) that treats scenario collections as lifecycle-governed data systems rather than static repositories. The proposed architecture unifies the scenario lifecycle within a single workflow. It integrates scenario generation and ingestion, validation and curation, immutable storage, semantic and value-based querying, and reproducible export. Scenario semantics are represented using ASAM OpenX formats (OpenDRIVE and OpenSCENARIO), together with ASAM OpenLABEL metadata, enabling standards-aligned interoperability. Querying is performed over categorical and value-carrying metadata without requiring inspection of raw scenario artifacts at query time. The reference implementation is deployed using Infrastructure-as-Code, supporting reproducibility and low operational overhead. Execution-based metric enrichment is supported as an optional extension, enabling scenarios to be augmented with execution-derived measurements and trace metadata. The contribution is not a centralized database, but a reference architecture and deployment blueprint that supports interoperable and federated scenario ecosystems. By framing SCDBs as reproducible lifecycle systems, this work supports scalable scenario reuse and more transparent safety validation workflows.

For automatic generation of test scenarios from UML (Unified Modeling Language) activity diagrams (ADs) are very important for improving test efficiency. However, state-of-the-art approaches mainly focus on simple approaches, without specifically considering the case of concurrent activity, which may result in the path explosion problem during the generation of test scenarios. In this paper, we put forward DFS-KeyLevel, a two-layer test scenario generation approach for UML Activity Diagram. First, the ADs of the software under test are modeled and preprocessed, and each concurrent module in each AD is simplified to a composite node. Then, primary test scenarios are generated from the concurrent activity modules using our proposed KeyLevel method. Next, the high-layer test scenarios are generated from the simplified AD with our improved Depth-First Search (DFS) algorithm. Finally, the primary and high-layer test scenarios are combined to generate the final test scenarios for the AD. The experimental results show that this DFS-KeyLevel is superior to the previous approaches. The DFS-KeyLevel can generate more test scenarios under constraints. Compared with DFS-LevelPermutes, the number of test scenarios generated by our DFS-KeyLevel is 1.13 times higher. Compared with Depth-First Search and Breadth-First Search (DFS-BFS) and Improved-DFS (IDFS), the DFS-KeyLevel produced 2.37 times test scenarios. The average coverage rates of staggered activities and total activity logical path coverage (TALPC) of the DFS-KeyLevel are 83.67% and 84% respectively, which is significantly higher than the above three approaches. In addition, when our method is applied to a real embedded system, it significantly reduces test scenarios generated to avoid path explosion while ensuring enough test scenarios.

As one of the critical technologies for performance evaluation of automated vehicles, the test scenario generation has been widespread concerned. In this paper, we propose a novel test scenario generation technology based on optimized Latin Hypercube Sampling (OLHS) and Test Matrix method (TM), named HIS-MPSO, which is efficient to generate the test scenario that consider the complexity, coverage, and potential relationships of factors. Based on naturalistic driving data, numerous car-following scenarios are generated by HIS-MPSO. Then, an adaptive cruise control system (ACC) are evaluated in terms of the tracking errors, comfort, and safety using the generated scenarios. Results show that compared with other existing OLHS algorithms, the HIS-MPSO can better restore the relationships among test factors existed in realistic traffic scenarios.

Concurrency in application systems can be designed and visualized using concurrent activity diagrams. Such diagrams are useful to design concurrency test scenarios for testing. However, the number of test scenarios inside a fork-join construct could be exponential in size. The commonly used permutation technique generates all possible test scenarios, but it is exponential in size. Existing UML graph theoretic-based approaches generate a few test scenarios for concurrency testing. But they do not consider the full functionality of concurrent activity diagrams. In this work, we present two constrained permutation-based test scenario generation approaches, namely the level permutation and DFS level permutation for concurrent activity diagrams. These approaches restrict the exponential size to a reasonable size of test scenarios. It is achieved by generating a subset of permutations at different levels. The generated test scenarios are sufficient to uncover most concurrency errors like synchronization, data-race, and deadlocks. The proposed technique improves interleaving activity path coverage up to 35% compared to the existing approaches.

The Brazilian Interconnected Power System is hydro dominated and characterized by large reservoirs presenting multi-year regulation capability, arranged in complex cascades over several river basins. In this way, the expansion and operation planning should take into account the uncertainties about the future inflows to hydroplants reservoirs. Currently, a stochastic model for synthetic scenarios generation of monthly streamflow, based on Periodic Auto-Regressive formulation, is used to address the uncertainty. This is the official model used in the Brazilian energy operation planning by the Ministry of Mines and Energy, the National Operator of Electrical System, the Chamber of Electric Energy Commercialization and the Energy Planning Company. Recently, a great scientific effort has been made to include relevant climatic information in stochastic streamflow models. Among several important climatic phenomena in the Brazilian hydrological cycles, El Niño–Southern Oscillation has been pointed as one of the most important. Although the stochastic models that include exogenous variables or that use wavelets present good results, they have limitations for long-term horizon projections or are not suitable for applications that use stochastic dual dynamic programming, which is the case of the Brazilian electrical system. This work proposes an improvement to the current scenario generation model, in order to consider the climate information, but still being suitable to be applied in SDDP algorithms. To achieve this goal, a Markov-Switching Periodic Auto-Regressive model is presented. It is demonstrated that the methodology is able to generate synthetic scenarios which better resembles the observed streamflow, mainly during periods when the streamflow are below-average.

In minimization problems with uncertain parameters, cost savings can be achieved by solving stochastic programming (SP) formulations instead of using expected parameter values in a deterministic formulation. To obtain such savings, it is crucial to employ scenarios of high quality. An appealing way to assess the quality of scenarios produced by a given method is to conduct a re-enactment of historical instances in which the scenarios produced are used when solving the SP problem and the costs are assessed under the observed values of the uncertain parameters. Such studies are computationally very demanding. We propose two approaches for assessment of scenario generation methods using past instances that do not require solving SP instances. Instead of comparing scenarios to observations directly, these approaches consider the impact of each scenario in the SP problem. The methods are tested in simulation studies of server location and unit commitment, and then demonstrated in a case study of unit commitment with uncertain variable renewable energy generation.

Automated driving is a promising tool for reducing traffic accidents. While some companies claim that many cutting-edge automated driving functions have been developed, how to evaluate the safety of automated vehicles remains an open question, which has become a crucial bottleneck. Scenario-based testing has been introduced to test automated vehicles, and much progress has been achieved. While data-driven and knowledge-based approaches are hot research topics, this survey is mainly about Data-Driven Scenario Generation (DDSG) for automated vehicle testing. Rather than describe the contributions of every study respectively, in this survey, methodologies from various studies are anatomized as solutions for several significant problems and compared with each other. This way, scholars and engineers can quickly find state-of-the-art approaches to the issues they might encounter. Furthermore, several critical challenges that might hinder DDSG are described, and responding solutions are presented at the end of this survey.