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Found 5 papers, 1 papers with code
On the Choice of Data for Efficient Training and Validation of End-to-End Driving Models
The emergence of data-driven machine learning (ML) has facilitated significant progress in many complicated tasks such as highly-automated driving.
Amodal Cityscapes: A New Dataset, its Generation, and an Amodal Semantic Segmentation Challenge Baseline
In this paper, we consider the task of amodal semantic segmentation and propose a generic way to generate datasets to train amodal semantic segmentation methods.
Description of Corner Cases in Automated Driving: Goals and Challenges
Scaling the distribution of automated vehicles requires handling various unexpected and possibly dangerous situations, termed corner cases (CC).
An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving
Systems and functions that rely on machine learning (ML) are the basis of highly automated driving.
Corner Cases for Visual Perception in Automated Driving: Some Guidance on Detection Approaches
Hence, their detection is highly safety-critical, and detection methods can be applied to vast amounts of collected data to select suitable training data.
