mlsanityData to 7 Oct 2026

Interpretable Deep Tracking

arXiv 2210.01266 ↗First posted 3 Oct 2022cs.CV cs.AI cs.LGcited by 0

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Abstract (arXiv)

Imagine experiencing a crash as the passenger of an autonomous vehicle. Wouldn't you want to know why it happened? Current end-to-end optimizable deep neural networks (DNNs) in 3D detection, multi-object tracking, and motion forecasting provide little to no explanations about how they make their decisions. To help bridge this gap, we design an end-to-end optimizable multi-object tracking architecture and training protocol inspired by the recently proposed method of interchange intervention training (IIT). By enumerating different tracking decisions and associated reasoning procedures, we can train individual networks to reason about the possible decisions via IIT. Each network's decisions can be explained by the high-level structural causal model (SCM) it is trained in alignment with. Moreover, our proposed model learns to rank these outcomes, leveraging the promise of deep learning in end-to-end training, while being inherently interpretable.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 19 references that are in the map. First posted on arXiv 3 Oct 2022.

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14 of 19 references.

References · 14

  1. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,707
  2. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  3. 2020Center-based 3D Object Detection and Trackingcited by 1,143
  4. 2021End-to-end Interpretable Neural Motion Plannercited by 240
  5. 2020PnPNet: End-to-End Perception and Prediction with Tracking in the Loopcited by 143
  6. 2020GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with Multi-Feature Learningcited by 113
  7. 2021Inducing Causal Structure for Interpretable Neural Networkscited by 107
  8. 2017Interpretable Learning for Self-Driving Cars by Visualizing Causal Attentioncited by 106
  9. 2020Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learningcited by 99
  10. 2019The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanationscited by 97
  11. 2019Grounding Human-to-Vehicle Advice for Self-driving Vehiclescited by 71
  12. 2021Learnable Online Graph Representations for 3D Multi-Object Trackingcited by 38
2 more
  1. 2020MATS: An Interpretable Trajectory Forecasting Representation for Planning and Controlcited by 30
  2. 2019Optimizing for Interpretability in Deep Neural Networks with Tree Regularizationcited by 13

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