ConsistencyPlanner: Real-time Planning with Fast-Sampling Consistency Models

• URL: http://arxiv.org/abs/2606.11569v1
• Date: Wed, 10 Jun 2026 01:51:51 GMT
• Title: ConsistencyPlanner: Real-time Planning with Fast-Sampling Consistency Models
• Abstract summary: Closed-loop planning in complex, real-world driving scenarios presents a critical challenge for autonomous driving systems.<n>Traditional rule-based methods are interpretable, their predefineds lack the adaptability for dynamic traffic environments.<n>We propose Consistency Planner, a real-time planning framework with fast-sampling consistency models.
• Score: 17.576850682759012
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Closed-loop planning in complex, real-world driving scenarios presents a critical challenge for autonomous driving systems. While traditional rule-based methods are interpretable, their predefined heuristics lack the adaptability for dynamic traffic environments. Learning-based approaches have shown considerable promise. Conversely, learning-based approaches, despite their promise, struggle to balance the modeling diverse and multimodal driving behaviors and real-time planning, often leading to indecisive or unsafe actions. To address this limitation, we propose Consistency Planner, a real-time planning framework with fast-sampling consistency models. Our approach is built upon two key technical contributions. Efficient Multimodal Sampling: We employ fast-sampling consistency models to generate a diverse set of plausible future trajectories. This enables efficient, real-time exploration of multimodal actions, overcoming the computational bottlenecks of previous iterative generative methods. Heterogeneous Feature Fusion: We introduce an attention-enhanced decoder that dynamically integrates heterogeneous input features (including scene feature and action token) into a cohesive representation for robust planning. Extensive evaluation in the Waymax simulator demonstrates superior performance in safety metrics compared to existing methods, with particularly strong results in challenging dynamic scenarios.

Related papers

• SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation [65.81476183180527]
  We introduce SegDiff, a closed-loop visuomotor policy that integrates the strengths of both paradigms.<n>SegDiff decomposes demonstrations into motion segments between keyposes and learns to predict the continuous trajectory from the current state to the next keypose.<n>SegDiff demonstrates significant performance gains over existing approaches across various simulated and real-world scenarios.
  arXiv  Detail & Related papers  (2026-07-13T02:48:03Z)
• World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning [60.88859946006306]
  Adversa World Modeling is a theoretically grounded multi-agent self-play fine-tuning framework.<n>In inner minimization, the planner's predictive world model is converted into a role-conditioned adversary that learns sparse, scene-adaptive attack coalitions.<n> Experiments on the nuPlan and InterPlan benchmarks demonstrate that our method generates transferable adversarial interactions.
  arXiv  Detail & Related papers  (2026-07-12T07:44:16Z)
• Proposal-Conditioned Latent Diffusion for Closed-Loop Traffic Scenario Generation [1.6711881128691604]
  We present a diffusion-based scenario generation framework conditioned on instance-centric scene context and multimodal proposal priors.<n>Experiments on the Open Motion dataset demonstrate a favorable balance among realism, safety, and controllability across diverse interactive scenarios.
  arXiv  Detail & Related papers  (2026-06-25T15:01:31Z)
• Scaling Up Occupancy-centric Driving Scene Generation: Dataset and Method [54.461213497603154]
  Occupancy-centric methods have recently achieved state-of-the-art results by offering consistent conditioning across frames and modalities.<n>Nuplan-Occ is the largest occupancy dataset to date, constructed from the widely used Nuplan benchmark.<n>We develop a unified framework that jointly synthesizes high-quality occupancy, multi-view videos, and LiDAR point clouds.
  arXiv  Detail & Related papers  (2025-10-27T03:52:45Z)
• Flow Matching-Based Autonomous Driving Planning with Advanced Interactive Behavior Modeling [26.71028572181775]
  Modeling interactive driving behaviors in complex scenarios remains a fundamental challenge for autonomous driving planning.<n>We propose Flow Planner, which tackles these problems through coordinated innovations in data modeling, model architecture, and learning scheme.<n>Flow Planner achieves state-of-the-art performance among learning-based approaches while effectively modeling interactive behaviors in complex driving scenarios.
  arXiv  Detail & Related papers  (2025-10-13T07:25:13Z)
• ImagiDrive: A Unified Imagination-and-Planning Framework for Autonomous Driving [64.12414815634847]
  Vision-Language Models (VLMs) and Driving World Models (DWMs) have independently emerged as powerful recipes addressing different aspects of this challenge.<n>We propose ImagiDrive, a novel end-to-end autonomous driving framework that integrates a VLM-based driving agent with a DWM-based scene imaginer.
  arXiv  Detail & Related papers  (2025-08-15T12:06:55Z)
• VLMPlanner: Integrating Visual Language Models with Motion Planning [18.633637485218802]
  VLMPlanner is a hybrid framework that combines a learning-based real-time planner with a vision-language model (VLM) capable of reasoning over raw images.<n>We develop the Context-Adaptive Inference Gate mechanism that enables the VLM to mimic human driving behavior.
  arXiv  Detail & Related papers  (2025-07-27T16:15:21Z)
• Predictive Planner for Autonomous Driving with Consistency Models [5.966385886363771]
  Trajectory prediction and planning are essential for autonomous vehicles to navigate safely and efficiently in dynamic environments.<n>Recent diffusion-based generative models have shown promise in multi-agent trajectory generation, but their slow sampling is less suitable for high-frequency planning tasks.<n>We leverage the consistency model to build a predictive planner that samples from a joint distribution of ego and surrounding agents, conditioned on the ego vehicle's navigational goal.
  arXiv  Detail & Related papers  (2025-02-12T00:26:01Z)
• Diffusion-Based Planning for Autonomous Driving with Flexible Guidance [19.204115959760788]
  We propose a novel transformer-based Diffusion Planner for closed-loop planning.<n>Our model supports joint modeling of both prediction and planning tasks.<n>It achieves state-of-the-art closed-loop performance with robust transferability in diverse driving styles.
  arXiv  Detail & Related papers  (2025-01-26T15:49:50Z)
• Merging Models on the Fly Without Retraining: A Sequential Approach to Scalable Continual Model Merging [75.93960998357812]
  Deep model merging represents an emerging research direction that combines multiple fine-tuned models to harness their capabilities across different tasks and domains.<n>Current model merging techniques focus on merging all available models simultaneously, with weight matrices-based methods being the predominant approaches.<n>We propose a training-free projection-based continual merging method that processes models sequentially.
  arXiv  Detail & Related papers  (2025-01-16T13:17:24Z)
• SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries [94.84458417662407]
  We introduce SAFE-SIM, a controllable closed-loop safety-critical simulation framework. Our approach yields two distinct advantages: 1) generating realistic long-tail safety-critical scenarios that closely reflect real-world conditions, and 2) providing controllable adversarial behavior for more comprehensive and interactive evaluations. We validate our framework empirically using the nuScenes and nuPlan datasets across multiple planners, demonstrating improvements in both realism and controllability.
  arXiv  Detail & Related papers  (2023-12-31T04:14:43Z)

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.