7 papers
Thinking While Driving: A Concurrent Framework for Real-Time, LLM-Based Adaptive Routing
Xiaopei Tan, Muyang Fan
We present Thinking While Driving, a concurrent routing framework that integrates LLMs into a graph-based traffic environment. Unlike approaches that require agents to stop and del…
CoT4Det: A Chain-of-Thought Framework for Perception-Oriented Vision-Language Tasks
Yu Qi, Yumeng Zhang, Chenting Gong +4
Large Vision-Language Models (LVLMs) have demonstrated remarkable success in a broad range of vision-language tasks, such as general visual question answering and optical character…
Fin3R: Fine-tuning Feed-forward 3D Reconstruction Models via Monocular Knowledge Distillation
Weining Ren, Hongjun Wang, Xiao Tan +1
We present Fin3R, a simple, effective, and general fine-tuning method for feed-forward 3D reconstruction models. The family of feed-forward reconstruction model regresses pointmap…
LaneDiffusion: Improving Centerline Graph Learning via Prior Injected BEV Feature Generation
Zijie Wang, Weiming Zhang, Wei Zhang +4
Centerline graphs, crucial for path planning in autonomous driving, are traditionally learned using deterministic methods. However, these methods often lack spatial reasoning and s…
VLDrive: Vision-Augmented Lightweight MLLMs for Efficient Language-grounded Autonomous Driving
Ruifei Zhang, Wei Zhang, Xiao Tan +4
Recent advancements in language-grounded autonomous driving have been significantly promoted by the sophisticated cognition and reasoning capabilities of large language models (LLM…
AdaDrive: Self-Adaptive Slow-Fast System for Language-Grounded Autonomous Driving
Ruifei Zhang, Junlin Xie, Wei Zhang +4
Effectively integrating Large Language Models (LLMs) into autonomous driving requires a balance between leveraging high-level reasoning and maintaining real-time efficiency. Existi…