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Understanding and benchmarking the performance of motion forecasting in autonomous driving systems

Data preprocessing:

  • random masking
  • masking schemes
  • multimodal data (graph images, images)
  • graph connectivity (topology connectivity, distance connectivity, fully connectivity)

Encoding Technique:

  • concatenate
  • cross-attention
  • local attention
  • routing-based attention
  • global-attention
  • axial-attention

Task decomposition:

  • Goal-conditioned
  • Anchor-conditioned
  • Routing-conditioned
  • Relation-conditioned (M2I)
  • Latent variable model (prior learning)

Auxiliary Task:

  • graph completion
  • collision/offroad avoidance
  • multi-agent prediction
  • planning task
  • contrastive learning

Neural Architecture: (maybe skipped)

  • encoding layer
  • graph layer

Visualize what part they are paying attention to

Analyze what kind of scenarios they fail most

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