arXiv Computer Vision
sensVLA: Spatially-Grounded Vision-Language-Action Model for Autonomous Wheel Loader
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Sep 15
sensVLA: Spatially-Grounded Vision-Language-Action Model for Autonomous Wheel Loader
Autonomous wheel-loader control requires joint reasoning over task semantics, egocentric vision, proprioception, and 3D scene geometry. We present sensVLA, a Vision-Language-Action (VLA) architecture...
arXiv AI
Oct 15
DriveWLA: Unifying Future Dynamics and Scene Semantics through World-Language-Action Modeling for Autonomous Driving
DriveWLA is a unified World‑Language‑Action model that integrates semantic understanding, future scene dynamics, and trajectory generation for autonomous driving. It employs a Mixture‑of‑Transformers architecture with three experts—World, Language, and Action—connected by Cross‑Expert Attention to align semantic and dynamic representations. After reinforcement learning refinement, DriveWLA outperforms existing VLM‑based and world‑model‑based planners on NAVSIM benchmarks and generalizes well to other datasets without fine‑tuning.
arXiv Computer Vision
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Qwen-Drive-1.0: An Initial Step towards a Vision-Language Foundation Model for Autonomous Driving
Qwen-Drive-1.0 is a vision‑language foundation model tailored for autonomous driving that builds on a pretrained VLM architecture. It incorporates a bird’s‑eye‑view perception head for 3D object detection, semantic occupancy prediction, and BEV map segmentation, and a Planning Expert that generates future ego trajectories from shared representations. Experiments show strong 3D perception, driving scene understanding, and competitive motion‑planning performance while largely preserving general vision‑language capabilities.
arXiv Computer Vision
Sep 1
Inter-3D VQA: A Roadside Multimodal Benchmark for 3D Spatiotemporally Grounded Visual Question Answering
arXiv:2608.28762v1 Announce Type: new Abstract: Recent advances in visual question answering (VQA) and multimodal large language models (MLLMs) have enabled natural-language reasoning over traffic sc...
Hugging Face Trending Papers
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Qwen-Drive-1.0: An Initial Step towards a Vision-Language Foundation Model for Autonomous Driving
We present Qwen-Drive-1.0, an initial step towards a vision-language foundation model for autonomous driving. Qwen-Drive-1.0 retains the architecture of the pretrained vision-language model (VLM) and...
arXiv Computer Vision
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GeoLift: Grounding Video Spatial Reasoning in Explicit 3D Maps
arXiv:2610.33558v1 Announce Type: new Abstract: Video spatial reasoning requires multimodal large language models (MLLMs) to understand how objects are arranged and related across viewpoints. While t...