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Related Experiment Video

Updated: Jan 11, 2026

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MemRoadNet: Human-like Memory Integration for Free Road Space Detection.

Sidra Shafiq1,2, Abdullah Aman Khan1,2, Jie Shao1,2

  • 1School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

Sensors (Basel, Switzerland)
|November 13, 2025
PubMed
Summary
This summary is machine-generated.

MemRoadNet enhances autonomous driving by using Accumulated Experiential Knowledge (AEK) through a novel Memory-Augmented (MA) framework. This approach improves free road space detection by learning from past experiences, boosting reliability and efficiency.

Keywords:
autonomous drivingfeature matchingimage feature extractionrepresentation learningroad segmentation

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Autonomous Driving Systems

Background:

  • Reliable road space detection is crucial for autonomous vehicles.
  • Current methods lack adaptability due to independent processing of sensor data.
  • Accumulated Experiential Knowledge (AEK) is underexplored in this domain.

Purpose of the Study:

  • To introduce MemRoadNet, a Memory-Augmented (MA) semantic segmentation framework.
  • To investigate the impact of AEK on free road space detection.
  • To enhance the adaptability and reliability of autonomous driving perception systems.

Main Methods:

  • Developed MemRoadNet, integrating InternImage-XL backbone, UPerNet decoder, and a human-like memory bank.
  • Implemented episodic, semantic, and working memory subsystems for storing and retrieving road experiences.
  • Incorporated emotional valences based on segmentation performance to guide memory retrieval.

Main Results:

  • MemRoadNet achieved competitive performance against complex multimodal systems using only single-modality RGB data.
  • The MA framework demonstrated top performance among single-modality methods on KITTI road, Cityscapes, and R2D benchmarks.
  • The approach maintained computational efficiency while improving segmentation quality.

Conclusions:

  • The MA framework significantly advances sensor-based computer vision for autonomous driving.
  • MemRoadNet effectively bridges computational efficiency and segmentation quality.
  • Leveraging AEK offers a promising direction for more robust and reliable autonomous systems.