Drivora: A Unified and Extensible Infrastructure for Search-based Autonomous Driving Testing

2026-01-09

arXiv.org

AI Summary

This work addresses the limited reusability and adaptability of existing search-based autonomous driving testing methods, which often rely on heterogeneous frameworks and struggle to generalize across diverse scenarios, simulators, and autonomous driving systems (ADS). To overcome this, we propose Drivora—a unified and extensible testing infrastructure built upon CARLA. Drivora’s core innovations include standardized scenario definition using the OpenScenario format, decoupling of the test engine, execution module, and ADS interface, and support for multi-vehicle coordination and large-scale parallel simulation. The framework integrates twelve mainstream ADS implementations and combines evolutionary search strategies with batched simulation to significantly enhance testing efficiency, flexibility, and scalability. The source code of Drivora has been publicly released.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Application Domains: Mobility, Driving & Flight

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Search-based testing is critical for evaluating the safety and reliability of autonomous driving systems (ADSs). However, existing approaches are often built on heterogeneous frameworks (e.g., distinct scenario spaces, simulators, and ADSs), which require considerable effort to reuse and adapt across different settings. To address these challenges, we present Drivora, a unified and extensible infrastructure for search-based ADS testing built on the widely used CARLA simulator. Drivora introduces a unified scenario definition, OpenScenario, that specifies scenarios using low-level, actionable parameters to ensure compatibility with existing methods while supporting extensibility to new testing designs (e.g., multi-autonomous-vehicle testing). On top of this, Drivora decouples the testing engine, scenario execution, and ADS integration. The testing engine leverages evolutionary computation to explore new scenarios and supports flexible customization of core components. The scenario execution can run arbitrary scenarios using a parallel execution mechanism that maximizes hardware utilization for large-scale batch simulation. For ADS integration, Drivora provides access to 12 ADSs through a unified interface, streamlining configuration and simplifying the incorporation of new ADSs. Our tools are publicly available at https://github.com/MingfeiCheng/Drivora.

Problem

• search-based testing
• autonomous driving systems
• heterogeneous frameworks
• scenario definition
• test reusability

Innovation

• search-based testing
• unified infrastructure
• OpenScenario
• evolutionary computation
• autonomous driving simulation