Cross-dataset tests show BEV segmentation models generalize poorly across datasets and sensor setups, but multi-dataset training improves performance over single-dataset baselines.
nuscenes: A multimodal dataset for autonomous driving,
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
representative citing papers
MSAM with kernels 1-11 added to UNet skip connections yields 2.32% mIoU and 2.88% mF1 gains on hyperspectral urban driving datasets.
A survey that organizes Transformer-based autonomous driving models by task and architecture while analyzing compression techniques as a system-level deployment concern.
citing papers explorer
-
BEVal: A Cross-dataset Evaluation Study of BEV Segmentation Models for Autonomous Driving
Cross-dataset tests show BEV segmentation models generalize poorly across datasets and sensor setups, but multi-dataset training improves performance over single-dataset baselines.
-
Multi-Scale Spectral Attention Module-based Hyperspectral Segmentation in Autonomous Driving Scenarios
MSAM with kernels 1-11 added to UNet skip connections yields 2.32% mIoU and 2.88% mF1 gains on hyperspectral urban driving datasets.
-
Transformer-Based Autonomous Driving Models and Deployment-Oriented Compression: A Survey
A survey that organizes Transformer-based autonomous driving models by task and architecture while analyzing compression techniques as a system-level deployment concern.