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Related Experiment Video

Updated: Jun 9, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

MulCPred: Learning Multi-Modal Concepts for Explainable Pedestrian Action Prediction.

Yan Feng¹, Alexander Carballo^2,3,4, Keisuke Fujii¹

¹Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan.

Sensors (Basel, Switzerland)

|October 26, 2024

Summary

This summary is machine-generated.

MulCPred enhances pedestrian action prediction by providing explainable, multi-modal concept-based insights. This framework improves trustworthiness and generalization in autonomous driving systems.

Keywords:

autonomous driving computer vision explainable AI multi-modal learning neural networks pedestrian action prediction

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Area of Science:

Computer Vision
Artificial Intelligence
Robotics

Background:

Pedestrian action prediction is vital for autonomous driving safety.
Current methods lack the explainability required for reliable predictions.
Existing concept-based approaches struggle with multi-modal data and input details.

Purpose of the Study:

To introduce MulCPred, a novel framework for explainable pedestrian action prediction.
To address limitations of previous concept-based methods in multi-modal scenarios.
To enhance the trustworthiness and generalization capabilities of action prediction models.

Main Methods:

MulCPred utilizes a linear aggregator for multi-modal concept integration and explanation.
A channel-wise recalibration module enables attention to local spatiotemporal details.
Feature regularization loss promotes diverse concept learning to prevent mode collapse.

Main Results:

MulCPred demonstrates improved explainability in pedestrian action prediction.
The framework shows promising results without significant performance degradation.
Removing unrecognized concepts enhances cross-dataset prediction performance and generalization.

Conclusions:

MulCPred offers a promising solution for explainable pedestrian action prediction.
The method effectively handles multi-modal data and local input details.
The framework shows potential for improved generalization in real-world applications.