Technology Category
Application Domains: Mobility, Driving & FlightNatural Language Processing: Safety and RobustnessComputer Vision: Vision for Robotics & Autonomous Driving
Towards Scenario- and Capability-Driven Dataset Development and Evaluation: An Approach in the Context of Mapless Automated Driving
🤖 AI Summary
Existing autonomous driving datasets lack sufficient real-time perception capability and robustness against edge cases in high-definition map–free scenarios. Method: This paper proposes a scenario- and capability-driven dataset construction and evaluation methodology, systematically deriving perception requirements from ISO 21448 (SOTIF) and ISO/TR 4804, and pioneering the deep integration of SOTIF principles throughout the dataset development lifecycle. It establishes a reusable “scenario–capability” mapping framework to support both novel dataset creation and cross-dataset benchmarking. Contribution/Results: Empirical analysis reveals systemic deficiencies in mainstream lane detection datasets—particularly in real-world scenario coverage, annotation of ambiguous drivable boundaries, and representation of complex driving behaviors. The proposed methodology significantly enhances dataset safety alignment with real-world operational conditions and improves generalization across diverse, unstructured environments.
Technology Category
Application Category
📝 Abstract
The foundational role of datasets in defining the capabilities of deep learning models has led to their rapid proliferation. At the same time, published research focusing on the process of dataset development for environment perception in automated driving has been scarce, thereby reducing the applicability of openly available datasets and impeding the development of effective environment perception systems. Sensor-based, mapless automated driving is one of the contexts where this limitation is evident. While leveraging real-time sensor data, instead of pre-defined HD maps promises enhanced adaptability and safety by effectively navigating unexpected environmental changes, it also increases the demands on the scope and complexity of the information provided by the perception system.To address these challenges, we propose a scenario- and capability-based approach to dataset development. Grounded in the principles of ISO 21448 (safety of the intended functionality, SOTIF), extended by ISO/TR 4804, our approach facilitates the structured derivation of dataset requirements. This not only aids in the development of meaningful new datasets but also enables the effective comparison of existing ones. Applying this methodology to a broad range of existing lane detection datasets, we identify significant limitations in current datasets, particularly in terms of real-world applicability, a lack of labeling of critical features, and an absence of comprehensive information for complex driving maneuvers.
Problem
Research questions and friction points this paper is trying to address.
Addresses scarcity in dataset development for automated driving perception
Proposes scenario-based method to enhance dataset applicability and safety
Identifies limitations in current lane detection datasets' real-world relevance
Innovation
Methods, ideas, or system contributions that make the work stand out.
Scenario- and capability-based dataset development approach
ISO 21448 and ISO/TR 4804 grounded methodology
Structured derivation of dataset requirements
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