Trajectory planning method based on multi-stage optimization strategy and mixed diffusion model

By employing a multi-stage optimization strategy and a hybrid diffusion model, the intention anchor point trajectory is screened and the modeling spatial environment and temporal motion dependencies are decoupled. This solves the problems of high computational complexity and poor scene adaptability in existing technologies, and achieves safe and efficient multimodal trajectory generation.

CN121834433APending Publication Date: 2026-04-10DALIAN UNIV OF TECH
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Patent Information

Application Number
CN202511911266.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-17
Publication Date
2026-04-10

AI Technical Summary

Technical Problem

Existing multimodal trajectory planning methods suffer from high computational complexity and poor scene adaptability in complex scenarios, making it difficult to generate safe and diverse driving trajectories. Furthermore, traditional diffusion models are computationally expensive and lack scene adaptability.

Method used

Employing a multi-stage optimization strategy and a hybrid diffusion model, high-quality initialization is provided through intent anchor point trajectory filtering. Combined with a spatiotemporally decoupled hybrid diffusion model, multimodal trajectory generation is achieved, outputting safe, diverse, and scenario-consistent driving trajectories.

Benefits of technology

It reduces computational complexity, improves scene adaptability and the safety and efficiency of trajectory generation, and ensures the diversity and dynamic feasibility of generated trajectories.

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Abstract

The invention discloses a trajectory planning method based on a multi-stage optimization strategy and a mixed diffusion model. The method comprises the following steps: fusing multi-source sensor data and extracting features; screening and fusing intention anchor point tracks; and optimizing the fine-grained trajectory based on the Transform-Mamba mixed diffusion model. According to the method, an intention anchor point track screening mechanism is provided, the anchor point track matched with the scene is selected from the compact vocabulary through the dynamic screening module and fused with the static prior anchor points, the calculation complexity is reduced, the diversity of the initial track and the scene consistency are guaranteed, and the problem that a traditional fixed anchor point set is insufficient in flexibility is solved. According to the method, a mixed diffusion decoder is designed, space environment dependence is efficiently modeled through a cross attention mechanism, a bidirectional Mama module captures time sequence motion dependence with linear complexity, global perception ability and long sequence modeling efficiency are both considered, and safety and dynamics feasibility of generated tracks are ensured.
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