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.
The article comprehensively outlines the development trajectory of visual semantic perception technology for mobile vehicles, providing a systematic theoretical reference for the design and embedded deployment optimization of visual algorithms for mobile robots in warehousing and inspection tasks.
A resilient cross-task modeling architecture to overcome the precision-versus-latency dilemma in comprehensive traffic scene understanding, the model's empirical results indicate it surpasses YOLOP as well as HybridNets in detecting objects, segmenting drivable zones, and delineating lane markings.
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.
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.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
