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Updated: May 31, 2025

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DynamicVLN: Incorporating Dynamics into Vision-and-Language Navigation Scenarios.

Yanjun Sun1,2, Yue Qiu2, Yoshimitsu Aoki1

  • 1Department of Electronics and Electrical Engineering, Faculty of Science and Technology, Keio University, 3-14-1, Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan.

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Summary
This summary is machine-generated.

This study introduces Dynamic Vision-and-Language Navigation (DynamicVLN) to train agents for real-world driving. The new approach enhances agent adaptability in complex, changing road conditions beyond static environments.

Keywords:
decision-makingdynamic changevision-and-language navigation

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Vision

Background:

  • Traditional Vision-and-Language Navigation (VLN) models struggle with dynamic real-world environments.
  • Static environments do not reflect complexities like moving vehicles, pedestrians, and changing traffic signals.

Purpose of the Study:

  • Introduce Dynamic Vision-and-Language Navigation (DynamicVLN) to address limitations of static VLN tasks.
  • Enhance agent decision-making and adaptability in dynamic navigation scenarios.

Main Methods:

  • Developed ten dynamic navigation scenarios.
  • Created a dataset of 11,261 instances using the CARLA simulator and large language models.
  • Introduced a baseline model with advanced perception and decision-making modules.

Main Results:

  • The baseline model effectively navigates and interprets dynamic road conditions.
  • Demonstrated agent ability to follow instructions and adapt to environmental changes.
  • Established a benchmark for dynamic navigation agents.

Conclusions:

  • DynamicVLN is crucial for developing agents capable of real-world navigation.
  • The proposed approach moves beyond static VLN limitations towards practical applications.
  • Agents need reasoning and adaptability for unforeseen events in dynamic environments.