← Search

Chenglie Du

2 accepted papers

2026

Ask Less, See More: Communication-Conditioned Token Pruning for Vehicle-to-Vehicle Cooperative Autonomous Driving with Multimodal Large Language Models

ICML 2026poster

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…

Cited by 0SourceScholar
2026

STEP-Nav: Spatial-Temporal Efficient Visual Token Pruning for Vision-and-Language Navigation with Large Language Models

AAAI 2026technical

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

Cited by 0SourcePDFScholar