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Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillation

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arxiv 2503.12820 v1 pith:RLY5MJ6Y submitted 2025-03-17 cs.CV

Kailin Li , Zhenxin Li , Shiyi Lan , Yuan Xie , Zhizhong Zhang , Jiayi Liu , Zuxuan Wu , Zhiding Yu
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Jose M.Alvarez
This is my paper
classification cs.CV
keywords drivinghydra-mdpcomponentsend-to-endframeworkwithoutabilityachieves
verification ladder T0 review T1 audit T2 compute T3 formal
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Hydra-MDP++ introduces a novel teacher-student knowledge distillation framework with a multi-head decoder that learns from human demonstrations and rule-based experts. Using a lightweight ResNet-34 network without complex components, the framework incorporates expanded evaluation metrics, including traffic light compliance (TL), lane-keeping ability (LK), and extended comfort (EC) to address unsafe behaviors not captured by traditional NAVSIM-derived teachers. Like other end-to-end autonomous driving approaches, \hydra processes raw images directly without relying on privileged perception signals. Hydra-MDP++ achieves state-of-the-art performance by integrating these components with a 91.0% drive score on NAVSIM through scaling to a V2-99 image encoder, demonstrating its effectiveness in handling diverse driving scenarios while maintaining computational efficiency.

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Cited by 6 Pith papers

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

  1. 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.

  2. HAD: Combining Hierarchical Diffusion with Metric-Decoupled RL for End-to-End Driving

    cs.RO 2026-04 conditional novelty 5.5 of 10

    Hierarchical diffusion plus polar structure-preserving expansion and metric-decoupled RL yields SOTA open- and closed-loop planning scores on NAVSIM and HUGSIM.

  3. LADY: Linear Attention for Autonomous Driving Efficiency without Transformers

    cs.AI 2025-12 conditional novelty 5.0 of 10

    LADY shows that an end-to-end driving model using only linear attention can match transformer-based planners on NAVSIM/Bench2Drive while fusing arbitrary-length historical sensor frames at constant per-frame cost.

  4. DeMo++: Motion Decoupling for Autonomous Driving

    cs.CV 2025-07 conditional novelty 5.0 of 10

    A decoupled mode/state query representation with hybrid Attention+Mamba and cross-scene interaction achieves top results on Argoverse 2, nuScenes, nuPlan, and NAVSIM, but the Argoverse 2 and nuPlan evaluations use a r...

  5. DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving

    cs.CV 2025-07 unverdicted novelty 5.0 of 10

    DIVER uses RL-guided diffusion to produce diverse feasible trajectories from one ground-truth path, addressing mode collapse in imitation learning for autonomous driving.

  6. Drive-JEPA: Video JEPA Meets Multimodal Trajectory Distillation for End-to-End Driving

    cs.CV 2026-01 reject novelty 4.0 of 10

    A video-pretrained encoder plus simulator-distilled multimodal trajectory proposals scores 93.3 PDMS on NAVSIM v1 and 87.8 EPDMS on v2, but the v1 number is not the highest in the paper's own table.

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