Towards Scenario- and Capability-Driven Dataset Development and Evaluation: An Approach in the Context of Mapless Automated Driving
arxiv(2024)
摘要
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.
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