D$^3$-MoE:Dual Disentangled Diffusion Mixture-of-Experts for Style-Controllable End-to-End Autonomous Driving

• URL: http://arxiv.org/abs/2606.04884v1
• Date: Wed, 03 Jun 2026 13:46:46 GMT
• Title: D$^3$-MoE:Dual Disentangled Diffusion Mixture-of-Experts for Style-Controllable End-to-End Autonomous Driving
• Abstract summary: D$3$-MoE disentangles trajectory modeling along two complementary axes.<n>On the behavioral axis, generation is decoupled from selection.<n>On the physical axis, decoupled longitudinal and lateral routers activate their respective experts.
• Score: 22.70855312181222
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional end-to-end autonomous driving frameworks frequently suffer from the "style-averaging" dilemma when trained on high-variance human demonstrations, yielding homogenized, style-uncontrollable, and even kinematically unsafe policies. To overcome this limitation, we present D$^3$-MoE (Dual Disentangled Diffusion Mixture-of-Experts), which disentangles trajectory modeling along two complementary axes. On the behavioral axis, generation is decoupled from selection: a style-conditioned diffusion process synthesizes multi-style candidate trajectories in parallel within a single scene, allowing a downstream module to select the optimal trajectory based on user preference or an evaluation score. On the physical axis, decoupled longitudinal and lateral routers activate their respective experts during inference time, trained without manual labels using self-supervised targets from orthogonal ground-truth kinematics. These activated experts, architected as Diffusion Transformers (DiT) and equipped with style-conditioned AdaLN and asymmetric lateral-fusion cross-attention, independently predict their corresponding physical state before being reassembled into a unified, kinematically coherent trajectory. Extensive evaluations on the challenging NAVSIM benchmark demonstrate that D$^3$-MoE achieves state-of-the-art planning performance, reaching 88.2 PDMS and 84.3 EPDMS by default. Moreover, our Best-of-Three ensemble strategy effectively broadens the multi-modal solution space, raising performance to 91.3 PDMS and 87.5 EPDMS. Both quantitative and qualitative analyses jointly confirm the framework's advantages in planning quality and style controllability.

Related papers

• Grasp, Handover, Rotate: Bimanual Object Reorientation via Compositional Diffusion and Energy-Based Optimization [28.020553566509523]
  BiCompoDiff is a compositional diffusion and energy-based framework that jointly optimize grasp selection, handover, regrasp, and motion planning.<n> Experiments across diverse simulated household reorientation tasks demonstrate that BiCompoDiff achieves over 20% higher success rates.<n>Real-world validation confirms effective sim-to-real transfer and robust performance on challenging scenes.
  arXiv  Detail & Related papers  (2026-07-23T14:11:14Z)
• PG-MAP: Joint MAP Optimization for Inference-Time Alignment of Diffusion and Flow-Matching Models [0.8594140167290097]
  Inference-time alignment of pretrained text-to-image models is typically performed along a single control axis.<n>We propose PG-MAP, a training-free framework that formulates inference-time alignment as a trajectory-level Gibbs-MAP / proximal energy optimization.
  arXiv  Detail & Related papers  (2026-06-22T07:36:36Z)
• Traits Run Deeper: Trait-Specific Asymmetric Fusion for Personality Assessment [53.50824432699408]
  Traits Run Deeper is a novel personality assessment framework.<n>It consists of three components: the Multimodal Foundation Representation (MFR), the Trait-Specific Modality Fusion (TSMF) and the Distribution-Calibrated Personality Regression (DCPR)
  arXiv  Detail & Related papers  (2026-06-09T06:38:36Z)
• CLEAR: Cognition and Latent Evaluation for Adaptive Routing in End-to-End Autonomous Driving [19.474428775260034]
  We propose CLEAR, a framework that combines ultra-fast generative planning with deep semantic reasoning.<n> CLEAR employs Drive-JEPA as the visual encoder and replaces the multi-step denoising chain with a single-step conditional drift in a VAE latent space.<n>On the NAVSIM v1 benchmark, CLEAR achieves a state-of-the-art PDMS of 93.7.
  arXiv  Detail & Related papers  (2026-06-04T14:32:10Z)
• Hierarchically Decoupled Mixture-of-Experts for Robust Traffic Sign Recognition in Complex Driving Scenarios [6.121863091287242]
  CBDES MoE TSR is a hierarchically decoupled heterogeneous mixture-of-experts(MoE) framework for traffic sign recognition.<n>Based on the semantic characteristics of the input image, the gating module selectively activates the most suitable expert model from the expert pool.<n> Experimental results demonstrate that the proposed method achieves a remarkable balance between detection accuracy and efficiency.
  arXiv  Detail & Related papers  (2026-06-01T07:39:44Z)
• HAD: Combining Hierarchical Diffusion with Metric-Decoupled RL for End-to-End Driving [51.268878540511054]
  We propose HAD, an end-to-end planning framework with a Hierarchical Diffusion Policy.<n>We show that HAD achieves new state-of-the-art performance on both NAVSIM and HUGSIM.
  arXiv  Detail & Related papers  (2026-04-04T04:12:47Z)
• Coordinated Manipulation of Hybrid Deformable-Rigid Objects in Constrained Environments [3.915966095774856]
  This work presents a quasi-static optimization-based manipulation planner that employs a strain-based Cosserat rod model.<n>It exploits the compliance of deformable links to maneuver through constraints while achieving task-space objectives for the object that are unreachable with rigid tools.<n>The proposed algorithm is validated in simulation on various hDLO systems and experimentally on a three-link hDLO manipulated in a constrained environment using a dual-arm robotic system.
  arXiv  Detail & Related papers  (2026-03-13T12:34:49Z)
• Model-Based Diffusion Sampling for Predictive Control in Offline Decision Making [48.998030470623384]
  offline decision-making requires reliable behaviors from fixed datasets without further interaction.<n>We propose a compositional model-based diffusion framework consisting of: (i) a planner that generates diverse, task-aligned trajectories; (ii) a dynamics model that enforces consistency with the underlying system dynamics; and (iii) a ranker module that selects behaviors aligned with the task objectives.
  arXiv  Detail & Related papers  (2025-12-09T06:26:02Z)
• DiffusionDriveV2: Reinforcement Learning-Constrained Truncated Diffusion Modeling in End-to-End Autonomous Driving [65.7087560656003]
  Generative diffusion models for end-to-end autonomous driving often suffer from mode collapse.<n>We propose DiffusionDriveV2, which leverages reinforcement learning to constrain low-quality modes and explore for superior trajectories.<n>This significantly enhances the overall output quality while preserving the inherent multimodality of its core Gaussian Mixture Model.
  arXiv  Detail & Related papers  (2025-12-08T17:29:52Z)
• Optimization-Guided Diffusion for Interactive Scene Generation [52.23368750264419]
  We present OMEGA, an optimization-guided, training-free framework that enforces structural consistency and interaction awareness during diffusion-based sampling.<n>We show that OMEGA improves generation realism, consistency, and controllability, increasing the ratio of physically and behaviorally valid scenes.<n>Our approach can also generate $5times$ more near-collision frames with a time-to-collision under three seconds.
  arXiv  Detail & Related papers  (2025-12-08T15:56:18Z)
• Optimal Control Meets Flow Matching: A Principled Route to Multi-Subject Fidelity [35.95129874095729]
  Text-to-image (T2I) models excel on single-entity prompts but struggle with multi-subject descriptions.<n>We introduce the first theoretical framework with principled optimizable objective for steering sampling dynamics toward multi-subject fidelity.
  arXiv  Detail & Related papers  (2025-10-02T17:59:58Z)
• EvaDrive: Evolutionary Adversarial Policy Optimization for End-to-End Autonomous Driving [17.57364638932072]
  EvaDrive is a novel reinforcement learning framework for autonomous driving.<n>It provides a closed-loop adversarial framework for human-like iterative decision-making.<n>Extensive experiments on NAVSIM and Bench2Drive benchmarks demonstrate SOTA performance.
  arXiv  Detail & Related papers  (2025-08-05T11:26:28Z)
• DIMM: Decoupled Multi-hierarchy Kalman Filter for 3D Object Tracking [50.038098341549095]
  State estimation is challenging for 3D object tracking with high maneuverability.<n>We propose a novel framework, DIMM, to effectively combine estimates from different motion models in each direction.<n>DIMM significantly improves the tracking accuracy of existing state estimation methods by 31.61%99.23%.
  arXiv  Detail & Related papers  (2025-05-18T10:12:41Z)
• Haar Wavelet based Block Autoregressive Flows for Trajectories [129.37479472754083]
  Prediction of trajectories such as that of pedestrians is crucial to the performance of autonomous agents. We introduce a novel Haar wavelet based block autoregressive model leveraging split couplings. We illustrate the advantages of our approach for generating diverse and accurate trajectories on two real-world datasets.
  arXiv  Detail & Related papers  (2020-09-21T13:57:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.