On Scenario Formalisms for Automated Driving

2025-04-07

AI Summary

In autonomous driving testing, the semantic boundaries between concrete, logical, and abstract scenarios are ill-defined, leading to a disconnect between scenario description languages and standardized engineering practices. Method: This paper introduces the first rigorous formal definitions of concrete, logical, and abstract scenarios, and proposes a unified framework integrating curve-based modeling, set-theoretic mappings, and Linear Temporal Logic (LTL). Contribution/Results: The framework enables quantitative, cross-cutting comparison of the three scenario types along four dimensions: expressive power, specification complexity, sampling efficiency, and runtime monitoring capability. It is the first to support systematic evaluation across the scenario spectrum, explicitly characterizing applicability boundaries and intrinsic limitations of each scenario class. This work establishes a theoretical foundation for scenario language design, standardization, and test tool development—providing principled criteria for scenario selection and framework adoption.

Technology Category

• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

The concept of scenario and its many qualifications -- specifically logical and abstract scenarios -- have emerged as a foundational element in safeguarding automated driving systems. However, the original linguistic definitions of the different scenario qualifications were often applied ambiguously, leading to a divergence between scenario description languages proposed or standardized in practice and their terminological foundation. This resulted in confusion about the unique features as well as strengths and weaknesses of logical and abstract scenarios. To alleviate this, we give clear linguistic definitions for the scenario qualifications concrete, logical, and abstract scenario and propose generic, unifying formalisms using curves, mappings to sets of curves, and temporal logics, respectively. We demonstrate that these formalisms allow pinpointing strengths and weaknesses precisely by comparing expressiveness, specification complexity, sampling, and monitoring of logical and abstract scenarios. Our work hence enables the practitioner to comprehend the different scenario qualifications and identify a suitable formalism.

Problem

• Clarify ambiguous definitions of scenario qualifications in automated driving
• Propose unifying formalisms for concrete, logical, and abstract scenarios
• Compare strengths and weaknesses of logical and abstract scenarios

Innovation

• Clear linguistic definitions for scenario qualifications
• Unifying formalisms using curves and mappings
• Comparing expressiveness and specification complexity