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

Detecting available road space is a fundamental task for autonomous driving vehicles, requiring robust image feature extraction methods that operate reliably across diverse sensor-captured scenarios. However, existing approaches process each input independently without leveraging Accumulated Experiential Knowledge (AEK), limiting their adaptability and reliability. In order to explore the impact of AEK, we introduce MemRoadNet, a Memory-Augmented (MA) semantic segmentation framework that integrates human-inspired cognitive architectures with deep-learning models for free road space detection. Our approach combines an InternImage-XL backbone with a UPerNet decoder and a Human-like Memory Bank system implementing episodic, semantic, and working memory subsystems. The memory system stores road experiences with emotional valences based on segmentation performance, enabling intelligent retrieval and integration of relevant historical patterns during training and inference. Experimental validation on the KITTI road, Cityscapes, and R2D benchmarks demonstrates that our single-modality RGB approach achieves competitive performance with complex multimodal systems while maintaining computational efficiency and achieving top performance among single-modality methods. The MA framework represents a significant advancement in sensor-based computer vision systems, bridging computational efficiency and segmentation quality for autonomous driving applications.