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GenAD: Generalized Predictive Model for Autonomous Driving

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arxiv 2403.09630 v2 pith:GX27KLV6 submitted 2024-03-14 cs.CV

Jiazhi Yang , Shenyuan Gao , Yihang Qiu , Li Chen , Tianyu Li , Bo Dai , Kashyap Chitta , Penghao Wu
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Jia Zeng Ping Luo Jun Zhang Andreas Geiger Yu Qiao Hongyang Li
This is my paper · ORCID
classification cs.CV
keywords drivingmodelgenadpredictionautonomousdatadiversevideo
verification ladder T0 review T1 audit T2 compute T3 formal
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In this paper, we introduce the first large-scale video prediction model in the autonomous driving discipline. To eliminate the restriction of high-cost data collection and empower the generalization ability of our model, we acquire massive data from the web and pair it with diverse and high-quality text descriptions. The resultant dataset accumulates over 2000 hours of driving videos, spanning areas all over the world with diverse weather conditions and traffic scenarios. Inheriting the merits from recent latent diffusion models, our model, dubbed GenAD, handles the challenging dynamics in driving scenes with novel temporal reasoning blocks. We showcase that it can generalize to various unseen driving datasets in a zero-shot manner, surpassing general or driving-specific video prediction counterparts. Furthermore, GenAD can be adapted into an action-conditioned prediction model or a motion planner, holding great potential for real-world driving applications.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Orbis 2: A Hierarchical World Model for Driving

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A hierarchical driving world model — planning in compressed DINO space at 2 Hz and rendering detailed frames at 10 Hz — achieves state-of-the-art long-horizon stability, steering response, and representation quality.

  2. DRIFT: Drift and Aggregation for Motion Planning

    cs.RO 2026-07 conditional novelty 6.0 of 10

    DRIFT achieves 89.6 PDMS and 90.4 EPDMS on NAVSIM navtest by generating proposal features via one-step latent drift and aggregating them label-free.

  3. AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models

    cs.RO 2026-03 conditional novelty 6.0 of 10

    AutoWorld learns a self-supervised LiDAR occupancy world model and conditions a diffusion-based motion generator on its forecasts, reporting the top Waymo Sim Agents realism score.

  4. HySafe-AI: Hybrid Safety Architectural Analysis Framework for AI Systems: A Case Study

    cs.AI 2025-07 conditional novelty 4.0 of 10

    A hybrid FMEA/FTA safety-analysis framework for foundation-model-based autonomous driving, illustrated on a GenAD and GAIA-2 style reference architecture.

  5. 2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model

    cs.CV 2025-09 conditional novelty 3.0 of 10

    A single-camera vision-language-model system scored 0.8747 on the CVPR 2024 E2E driving benchmark, the best camera-only result.

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