Autonomous Systems and Robotics Testing

Report on Current Developments in Autonomous Systems and Robotics Testing

General Direction of the Field

The field of autonomous systems and robotics testing is currently witnessing a significant shift towards more sophisticated, data-driven, and simulation-based approaches. Researchers are increasingly focusing on generating diverse, realistic, and challenging scenarios to rigorously evaluate the performance and robustness of autonomous systems, particularly in safety-critical applications. This trend is driven by the need to ensure that these systems can handle a wide range of edge cases and unforeseen situations, which are often underrepresented in real-world data.

One of the key developments is the integration of advanced machine learning techniques, particularly large language models (LLMs) and quality diversity (QD) algorithms, into scenario generation and testing frameworks. These techniques enable the creation of more complex and varied scenarios, which can be tailored to specific testing requirements and operational environments. The use of LLMs, in particular, is expanding the scope of scenario generation by leveraging extensive world knowledge and reasoning capabilities to produce realistic and diverse test cases.

Another notable trend is the adoption of ontology-based approaches for specifying and analyzing behavior in automated driving systems. These approaches aim to make behavior specifications more traceable and explicit, thereby reducing the risk of unsafe system behavior due to insufficient or ambiguous requirements. Ontologies are being used to formally represent specified behavior and establish traceability between behavior and stakeholder needs, which is crucial for ensuring the safety and compliance of autonomous vehicles.

Simulation-based testing frameworks are also evolving to support hybrid AI approaches, particularly in the context of unmanned aerial vehicles (UAVs). These frameworks are incorporating semantic context and symbolic information to better support neuro-symbolic algorithms, which are expected to play a key role in the future of autonomous systems. The focus is on creating more realistic and mission-relevant scenarios that can be used for training, testing, and assurance of these algorithms.

Noteworthy Papers

  • Multimodal Large Language Model Driven Scenario Testing for Autonomous Vehicles: Introduces OmniTester, a novel framework that leverages LLMs to generate realistic and diverse scenarios for AV testing, significantly enhancing controllability and realism.

  • A Quality Diversity Approach to Automatically Generate Multi-Agent Path Finding Benchmark Maps: Utilizes QD algorithms and Neural Cellular Automata to create diverse benchmark maps for MAPF algorithms, providing a more comprehensive and fair evaluation of these algorithms.

  • SoVAR: Building Generalizable Scenarios from Accident Reports for Autonomous Driving Testing: Develops a tool that automatically generates road-generalizable scenarios from accident reports, effectively identifying safety violations in industrial ADSs.

These papers represent significant advancements in the field, pushing the boundaries of scenario generation, testing, and evaluation for autonomous systems and robotics.

Sources

Algorithmic Scenario Generation as Quality Diversity Optimization

Multimodal Large Language Model Driven Scenario Testing for Autonomous Vehicles

A Quality Diversity Approach to Automatically Generate Multi-Agent Path Finding Benchmark Maps

An Ontology-based Approach Towards Traceable Behavior Specifications in Automated Driving

Simulation-based Scenario Generation for Robust Hybrid AI for Autonomy

Scenario Execution for Robotics: A generic, backend-agnostic library for running reproducible robotics experiments and tests

A Survey of Anomaly Detection in In-Vehicle Networks

SoVAR: Building Generalizable Scenarios from Accident Reports for Autonomous Driving Testing

ReGentS: Real-World Safety-Critical Driving Scenario Generation Made Stable