Operational Design Domain Monitoring with Uncertain Measurements
DOI: 10.1109/itsc58415.2024.10919733
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Summary
This paper addresses the challenge of ensuring operational safety for Intelligent Vehicles (IVs) by proposing a robust framework for real-time Operational Design Domain (ODD) monitoring. As automation levels increase, defining the specific environmental and geographical conditions under which a vehicle can safely operate is critical. However, existing ODD descriptions are often abstract, and live sensor data used for monitoring is inherently uncertain and dynamic. The authors aim to bridge this gap by introducing a method that calculates a continuous "membership degree" (ranging from 0 to 1) indicating how well the vehicle’s current operational domain aligns with its defined ODD, explicitly accounting for measurement uncertainties and the vagueness of real-world boundaries. The methodology consists of two main components: a formal ODD description system and a fuzzy logic-based monitoring engine. First, the authors propose a taxonomy-based approach to define ODDs independently of vehicle architecture. This involves a high-level, human-readable syntax that is converted into a machine-readable JSON structure containing logical statements (e.g., "reject visibility < low"). Second, the monitoring system uses fuzzy sets to handle numerical attributes. Instead of binary inclusion, the system employs membership functions to determine the degree to which observed attributes belong to defined intervals. Crucially, the framework integrates measurement uncertainty by modeling sensor data as normal distributions. The membership value is calculated by integrating the probability density function of the uncertain measurement over the fuzzy interval. To ensure stability, a time-based sliding window average smooths the membership values, and the system computes a "Time to Exit" (TTE) metric to predict when the vehicle will leave its safe operating domain. The approach was validated using the Carla Simulator, where artificial uncertainty and bias were added to perfect sensor data to mimic real-world conditions. Two use cases were tested: one involving distance constraints relative to a bus stop, and another involving visibility thresholds. Results demonstrated that the fuzzy membership approach effectively propagates uncertainty, producing sigmoid-like membership curves as the vehicle approaches boundary conditions. The use of time-based smoothing significantly reduced false "out-of-ODD" alerts caused by transient fluctuations in sensor data. Furthermore, the TTE metric provided a reliable estimate of when the system would exit its domain, allowing for configurable prudence levels (e.g., conservative vs. standard thresholds). The study confirmed that this method allows for the definition of verifiable safety rules that are robust against the noise and ambiguity inherent in live environmental perception. The significance of this work lies in its contribution to the formalization and verification of autonomous driving safety. By providing a mathematically rigorous way to quantify ODD compliance under uncertainty, the framework enables more reliable meta-decisions, such as triggering transition-of-control fallbacks in Level 3 autonomous systems. The taxonomy-based description ensures that ODD definitions remain interpretable by regulators and engineers while being executable by machines. This approach moves beyond abstract ODD specifications, offering a practical tool for real-time safety monitoring that can help validate Advanced Driver-Assistance Systems (ADAS) and Autonomous Driving Systems (ADS) against complex, dynamic environments.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | OpenAlex-citations | — | — | 1 | 2026-06-25 |
| archive | success | unpaywall | — | — | 2 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-25 |
| chunk | success | chunk | — | — | 1 | 2026-06-25 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-25 |
| promote | success | — | — | — | 1 | 2026-06-25 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-25 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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- Theoretical Contribution: conceptual framework