arXiv:2608. 14586v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are becoming a promising paradigm for autonomous driving, but their deployment on existing vehicle platforms remains difficult because they introduce both high inference latency and strong GPU-side resource pressure.
By Haibo HU, Lianming Huang, Qiao Li, Nan Guan, Chun Jason Xue
Visual Perception Engine (VPEngine) is a modular framework that enables efficient GPU usage for robotic vision tasks by sharing a foundation model backbone across multiple specialized task heads. It eliminates redundant feature extraction, supports dynamic task prioritization, and achieves up to 3× speedup over sequential execution. The open‑source Python implementation, with ROS2 C++ bindings, delivers real‑time performance (≥50 Hz) on NVIDIA Jetson Orin AGX using TensorRT‑optimized models.
By Jakub {\L}ucki, Jonathan Becktor, Georgios Georgakis, Rob Royce, Shehryar Khattak
arXiv:2608. 11770v1 Announce Type: cross Abstract: Edge-deployed vision systems in target recognition, surveillance, autonomous vehicles, and drone domains require hierarchical inference pipelines where a detection model identifies objects of interest and downstream classifiers provide fine-grained attribute analysis.
By Vaishnav Raju
Fast-BEV++ tackles the trade‑off between accuracy and efficiency in vision‑only Bird’s‑Eye‑View perception by adopting two guiding principles: Fast by Algorithm and Deployable by Design. The method decomposes view transformation into a hardware‑oriented Index‑Gather‑Reshape pipeline, removing the need for custom kernels and delivering at least a three‑fold speedup over baseline approaches. On the nuScenes benchmark, Fast‑BEV++ achieves 0.488 NDS while running in real time at over 134 FPS, with depth supervision providing consistent accuracy gains and the architecture enabling seamless deployment on production‑level platforms.
By Yuanpeng Chen, Hui Song, Sheng Yang, Wei Tao, Shanhui Mo, Shuang Zhang, Xiao Hua, Tiankun Zhao
arXiv:2606. 31938v1 Announce Type: cross Abstract: Deploying Vision Transformer (ViT) models on edge platforms remains challenging due to their high computational demands and the architectural heterogeneity of modern hybrid ViT models, which incorporate both fully connected and convolutional layers.
By Hubert Dymarkowski, Xingjian Fu, Rappy Saha, Jude Haris, Jos\'e Cano
arXiv:2603. 06576v2 Announce Type: replace-cross Abstract: The integration of Large Language Models (LLMs) into autonomous driving has attracted growing interest for their strong reasoning and semantic understanding abilities, which are essential for handling complex decision-making and long-tail scenarios.
By Thomas Monninger, Shaoyuan Xie, Qi Alfred Chen, Sihao Ding