ODD-diLLMma: Driving Automation System ODD Compliance Checking using LLMs

Although Driving Automation Systems (DASs) are rapidly becoming more advanced and ubiquitous, they are still confined to specific Operational Design Domains (ODDs) over which the system must be trained and validated. Yet, each DAS has a bespoke and often informally defined ODD, which makes it intractable to manually judge whether a dataset satisfies a DAS’s ODD. This results in inadequate data leaking into the training and testing processes, weakening them, and causes large amounts of collected data to go unused given the inability to check their ODD compliance. This presents a dilemma: How do we cost-effectively determine if existing sensor data complies with a DAS’s ODD? To address this challenge, we start by reviewing the ODD specifications of 10 commercial DASs to understand current practices in ODD documentation. Next, we present ODD-diLLMma, an automated method that leverages Large Language Models (LLMs) to analyze existing datasets with respect to the natural language specifications of ODDs. Our evaluation of ODD-diLLMma examines its utility in analyzing inputs from 3 real-world datasets. Our empirical findings show that ODD-diLLMma significantly enhances the efficiency of detecting ODD compliance, showing improvements of up to 147% over a human baseline. Further, our analysis highlights the strengths and limitations of employing LLMs to support ODD-diLLMma, underscoring their potential to effectively address the challenges of ODD compliance detection.