4 accepted papers
Multimodal Large Language Models (MLLMs) have recently emerged as a promising paradigm for vehicle-to-vehicle (V2V) cooperative autonomous driving, enabling language-based joint perception, prediction, and decision-making in safety-critical scenarios with severe occlusions. However, existing V2V–MLL…
Subset selection under budget constraints is critical in applications like multi-robot patrolling, crime deterrence, and targeted marketing, where multiple agents must jointly select targets and plan feasible routes. We formalize this challenge as Multi-Subset Selection with Budget-Constrained Routi
Vision-and-Language Navigation (VLN) plays a critical role in tasks of embodied AI, particularly in unseen environments following natural language instructions. Recent advancements leverage large language models (LLMs) to improve the accuracy and generalizability of VLN systems by encoding image seq
Camera-LiDAR fusion models significantly enhance perception performance in autonomous driving. The fusion mechanism leverages the strengths of each modality while minimizing their weaknesses. Moreover, in practice, camera-LiDAR fusion models utilize pre-trained backbones for efficient training. Howe…