Condition-Guided Diffusion for Multi-Modal Pedestrian Trajectory Prediction Incorporating Intention and Interaction...

Area of Science:

• Computer Vision
• Artificial Intelligence
• Robotics

Background:

• Pedestrian behavior is inherently multi-modal, requiring trajectory predictions that are both accurate and diverse for complex scenarios.
• Traditional diffusion models struggle with unguided noise addition, leading to inefficient sampling and lack of control.
• Existing methods often fail to capture the nuanced interplay of internal intentions and external interactions in pedestrian movement.

Purpose of the Study:

• To develop a novel diffusion model for multi-modal pedestrian trajectory prediction that addresses the limitations of conventional methods.
• To enhance the accuracy, diversity, and controllability of pedestrian trajectory predictions.
• To improve the efficiency of the diffusion process for trajectory generation.

Main Methods:

• Propose a Prior Condition-Guided Diffusion Model (CGD-TraP) that guides noise estimation using internal intention and external interaction features.
• Design specialized modules for extracting and aggregating intention and interaction features.
• Employ adaptive spatial-temporal fusion based on selective state space for controllable noisy trajectory distribution estimation.

Main Results:

• CGD-TraP demonstrates superior performance compared to state-of-the-art diffusion-based and generative methods on ETH-UCY, SDD, and NBA datasets.
• The proposed method achieves significant improvements in prediction accuracy and sample diversity.
• Experiments confirm the enhanced efficiency and controllability of the CGD-TraP model.

Conclusions:

• The Prior Condition-Guided Diffusion Model (CGD-TraP) offers a more effective approach to multi-modal pedestrian trajectory prediction.
• Guiding the noise estimation process with intention and interaction features leads to more accurate, diverse, and controllable predictions.
• CGD-TraP represents a significant advancement in generative models for understanding and predicting complex human behaviors in dynamic environments.