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June 17, 2026IEICE Transactions on Information and SystemsOpen Access

YUKUIv2: A Key-point Driven Diffusion Planner for Multi-shot Autonomous Parking

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Authors

JMJishu MiaoHCHan ChenTHTsubasa Hirakawa

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Overview

Randomized trial evaluates YUKUIv2's accuracy in multi-shot trajectory planning for autonomous driving, suggesting improved generalization across environments.

Key Points

  • The aim is to improve trajectory planning in autonomous vehicles by effectively managing discontinuous motion states.
  • Developed a Y-shaped trajectory planning module using a key-point driven diffusion mechanism.
  • Evaluated on the CARLA parking dataset to assess effectiveness in various parking scenarios.
  • Focused on the prediction of discrete transition points to enhance trajectory continuity.
  • YUKUIv2 achieved a high planning accuracy rate without compromising generalization across environment configurations.
  • Demonstrated significant improvements in trajectory generation compared to traditional methods.

Cite This Study

Miao et al. (2026) studied this question.

synapsesocial.com/papers/6a323957d50b63ecad204e02https://doi.org/10.1587/transinf.2026drl0001
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1CoPlanner: An Interactive Motion Planner with Contingency-Aware Diffusion for Autonomous Driving2025
  2. 2Diffusion-Based Planning for Autonomous Driving with Flexible Guidance2025 · 2 citations
  3. 3QuAD: Query-based Interpretable Neural Motion Planning for Autonomous Driving2024
  4. 4Hybrid imitation learning and differentiable optimization framework for trajectory planning in autonomous driving2026
  5. 5Real-time Motion Planning for autonomous vehicles in dynamic environments2024 · 2 citations