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Found 5 papers, 4 papers with code
TempBEV: Improving Learned BEV Encoders with Combined Image and BEV Space Temporal Aggregation
These results indicate the overall effectiveness of our approach and make a strong case for aggregating temporal information in both image and BEV latent spaces.
Semantic Map Learning of Traffic Light to Lane Assignment based on Motion Data
We show the effectiveness of basic statistical approaches for this task by implementing and evaluating a pattern-based contribution method.
LMR: Lane Distance-Based Metric for Trajectory Prediction
In contrast to the Euclidean Miss Rate, qualitative results show that LMR yields misses for sequences where predictions are located on wrong lanes.
Exploring Navigation Maps for Learning-Based Motion Prediction
Combined with our method for knowledge distillation, we achieve results that are close to the original HD map-reliant models.
SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks
In this work we propose SCENE, a methodology to encode diverse traffic scenes in heterogeneous graphs and to reason about these graphs using a heterogeneous Graph Neural Network encoder and task-specific decoders.
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