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Orthogonal Trajectories

Orthogonal Trajectories

Orthogonal trajectories describe the geometric relationship between two families of curves that intersect each other at right angles. One illustrative case involves a family of parabolas that open sideways along the x-axis. These curves share a common shape but differ by a scaling parameter, resulting in a set of curves that all pass through the origin and widen at different rates.Determining Orthogonal TrajectoriesTo identify the orthogonal trajectories for these parabolas, the first step...

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Related Experiment Video

Updated: May 7, 2026

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions

Published on: May 10, 2012

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

Yanghong Liu, Xingping Dong, Yutian Lin

IEEE Transactions on Pattern Analysis and Machine Intelligence

|December 18, 2025

Summary

This summary is machine-generated.

This study introduces a novel Prior Condition-Guided Diffusion Model (CGD-TraP) for pedestrian trajectory prediction. The model enhances accuracy and diversity by guiding noise estimation with intention and interaction features, improving control over generated samples.

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Last Updated: May 7, 2026

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions

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Published on: May 10, 2012

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Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

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Published on: April 13, 2016

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.