mlsanityData to 7 Oct 2026

CVAE-H: Conditionalizing Variational Autoencoders via Hypernetworks and Trajectory Forecasting for Autonomous Driving

arXiv 2201.09874 ↗First posted 24 Jan 2022cs.LG cs.AI cs.ROcited by 13

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Abstract (arXiv)

The task of predicting stochastic behaviors of road agents in diverse environments is a challenging problem for autonomous driving. To best understand scene contexts and produce diverse possible future states of the road agents adaptively in different environments, a prediction model should be probabilistic, multi-modal, context-driven, and general. We present Conditionalizing Variational AutoEncoders via Hypernetworks (CVAE-H); a conditional VAE that extensively leverages hypernetwork and performs generative tasks for high-dimensional problems like the prediction task. We first evaluate CVAE-H on simple generative experiments to show that CVAE-H is probabilistic, multi-modal, context-driven, and general. Then, we demonstrate that the proposed model effectively solves a self-driving prediction problem by producing accurate predictions of road agents in various environments.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 35 of its 49 references that are in the map. First posted on arXiv 24 Jan 2022.

Builds on

35 of 49 references.

References · 35

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23 more
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Citing papers per year: 2022: 1; 2023: 4; 2024: 6; 2025: 2.

13 papers.

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  1. 2023MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusioncited by 210
  2. 2023A Brief Review of Hypernetworks in Deep Learningcited by 102
  3. 2024WcDT: World-centric Diffusion Transformer for Traffic Scene Generationcited by 31
  4. 2023SceneDM: Scene-level Multi-agent Trajectory Generation with Consistent Diffusion Modelscited by 24
  5. 2022Neuro-Symbolic Learning: Principles and Applications in Ophthalmologycited by 7
  6. 2023A Survey of Generative AI for Intelligent Transportation Systems: Road Transportation Perspectivecited by 6
  7. 2024DragTraffic: Interactive and Controllable Traffic Scene Generation for Autonomous Drivingcited by 3
  8. 2024Learning to Forget using Hypernetworkscited by 3
  9. 2024Disentangled Representation Learning for Parametric Partial Differential Equationscited by 2
  10. 2024Scene-Aware Explainable Multimodal Trajectory Predictioncited by 2
  11. 2024Hyper-Transformer for Amodal Completioncited by 1
  12. 2025GDR-learners: Orthogonal Learning of Generative Models for Potential Outcomescited by 1
1 more
  1. 2025IrisNet: Infrared Image Status Awareness Meta Decoder for Infrared Small Targets Detectioncited by 0