6 papers
Who Responds When the Driver Is Gone? A Framework for Holistic Passenger Intent Understanding
Xuewen Luo, Ding Fan, Ruiqi Chen +5
As autonomous vehicles advance toward driverless mobility, understanding and responding to passenger needs and intentions becomes increasingly important in the absence of a human d…
CIVIC: End-to-End Sequence Compactness for Efficient Vision-Language Models
Fengze Yang, Bo Yu, Xuewen Luo +2
Vision-Language Models (VLMs) face severe memory and latency bottlenecks due to high-resolution visual tokens. While current token reduction methods theoretically save FLOPs, post-…
Locatability-Guided Adaptive Reasoning for Image Geo-Localization with Vision-Language Models
Bo Yu, Fengze Yang, Yiming Liu +6
The emergence of Vision-Language Models (VLMs) has introduced new paradigms for global image geo-localization through retrieval-augmented generation (RAG) and reasoning-driven infe…
Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance
Fengze Yang, Bo Yu, Yang Zhou +3
Autonomous driving (AD) systems relying solely on onboard sensors may fail to detect distant or obstacle hazards, potentially causing preventable collisions; however, existing tran…
AirV2X: Unified Air-Ground Vehicle-to-Everything Collaboration
Xiangbo Gao, Yuheng Wu, Fengze Yang +7
While multi-vehicular collaborative driving demonstrates clear advantages over single-vehicle autonomy, traditional infrastructure-based V2X systems remain constrained by substanti…
A Comprehensive LLM-powered Framework for Driving Intelligence Evaluation
Shanhe You, Xuewen Luo, Xinhe Liang +3
Evaluation methods for autonomous driving are crucial for algorithm optimization. However, due to the complexity of driving intelligence, there is currently no comprehensive evalua…