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About this paper
Hannes Reichert, Lukas Lang, Kevin Rösch, Daniel Bogdoll, Konrad Doll, Bernhard Sick, Hans-Christian Reuss, Christoph Stiller and 1 more
This paper conceptualizes sensor data abstraction as a critical interface for autonomous vehicle perception systems. It defines a learned mapping from diverse sensor inputs to a unified, machine learning-suitable representation to enhance model transferability and robustness across varying sensor configurations.
Key insights
- Effectively achieving sensor-agnostic generalization for HAD perception requires overcoming data limitations, potentially through large-scale, varied data generation in simulation environments.
- Integrating metadata, such as intrinsic and extrinsic sensor parameters, holds untapped potential to improve marginal and joint sensor data abstraction by reducing sensor bias.
- A unified and sensor-independent abstraction layer is essential for a general perception pipeline that can adapt across diverse autonomous vehicle sensor configurations without full retraining.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.