Technology Category
Search and Optimization: Sampling/Simulation-based SearchPlanning, Routing, and Scheduling: Model-Based ReasoningNatural Language Processing: Code Generation / Program Synthesis from Natural Language
Bridging Simulation and Usability: A User-Friendly Framework for Scenario Generation in CARLA
🤖 AI Summary
To address the challenge that existing autonomous driving simulation validation tools require programming expertise—thus hindering adoption by non-technical users—this paper proposes a no-code, interactive scenario generation framework. Methodologically, it introduces a graph-structured scenario representation model that unifies manual editing with parameterized stochastic sampling, enabling intuitive construction of diverse, high-fidelity traffic scenarios; a graphical user interface supports configuration, editing, and execution, with seamless integration into the CARLA simulator and deep learning models. Key contributions include: (1) the first application of graph-based modeling to no-code scenario generation, enhancing semantic expressiveness and manageability; (2) substantial reduction in usability barriers, empowering domain experts without coding skills to efficiently construct large-scale, variable, and photorealistic test cases; and (3) empirical evaluation demonstrating superior performance over state-of-the-art tools in scenario diversity, generation efficiency, and simulation utility.
Technology Category
Application Category
📝 Abstract
Autonomous driving promises safer roads, reduced congestion, and improved mobility, yet validating these systems across diverse conditions remains a major challenge. Real-world testing is expensive, time-consuming, and sometimes unsafe, making large-scale validation impractical. In contrast, simulation environments offer a scalable and cost-effective alternative for rigorous verification and validation. A critical component of the validation process is scenario generation, which involves designing and configuring traffic scenarios to evaluate autonomous systems' responses to various events and uncertainties. However, existing scenario generation tools often require programming knowledge, limiting accessibility for non-technical users. To address this limitation, we present an interactive, no-code framework for scenario generation. Our framework features a graphical interface that enables users to create, modify, save, load, and execute scenarios without needing coding expertise or detailed simulation knowledge. Unlike script-based tools such as Scenic or ScenarioRunner, our approach lowers the barrier to entry and supports a broader user base. Central to our framework is a graph-based scenario representation that facilitates structured management, supports both manual and automated generation, and enables integration with deep learning-based scenario and behavior generation methods. In automated mode, the framework can randomly sample parameters such as actor types, behaviors, and environmental conditions, allowing the generation of diverse and realistic test datasets. By simplifying the scenario generation process, this framework supports more efficient testing workflows and increases the accessibility of simulation-based validation for researchers, engineers, and policymakers.
Problem
Research questions and friction points this paper is trying to address.
Validating autonomous driving systems in diverse conditions is challenging
Existing scenario generation tools require programming knowledge, limiting accessibility
A no-code framework simplifies scenario generation for non-technical users
Innovation
Methods, ideas, or system contributions that make the work stand out.
Interactive no-code framework for scenario generation
Graph-based representation for structured scenario management
Random parameter sampling for diverse test datasets
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