TL;DR
A lightweight multi-task learning framework for safety-critical autonomous driving perception that strikes a balance between accuracy and real-time performance in multi-task perception, thereby supporting safety-critical perception by reducing perception latency and improving perception reliability.
A unified multi-attribute framework based on a Vision Transformer, which is enhanced with lightweight, parameter-efficient adapters and exhibits consistent behavior–context relationships and demonstrates robustness under varied environmental conditions is proposed.
With the large-scale implementation of scenarios such as logistics warehousing and park inspections, the demand for autonomous environmental perception by mobile robots continues to grow. Low-cost pure vision semantic perception has become a core technology for ensuring autonomous safe navigation of these robots. Tradi...
This study puts forward a resilient cross-task modeling architecture to overcome the precision-versus-latency dilemma in comprehensive traffic scene understanding. The backbone employs a C2f module to enhance gradient flow and small-object detection, while structural re-parameterization via repVGG blocks enables multi-...
Autonomous vehicles depend on fast and reliable perception systems to detect surrounding vehicles, pedestrians, cyclists, traffic signs, and other road objects in real time. This paper presents a comprehensive survey and analysis of one-stage object detectors for autonomous driving rather than an implementation of a ne...
The results presented in this study show promising potential to integrate into a driver-assistance system, although the data used here is limited to a proof-of-concept validation on the KITTI dataset.
Highlights What are the main findings? A latent-space LM-JEPA framework enables resource-efficient multi-modal object detection and scene perception for connected and autonomous vehicles, achieving higher perception accuracy with lower inference latency compared to conventional LLM and VLM-based methods. Context-aware...
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