6 accepted papers
Cooperatively utilizing both ego-vehicle and infrastructure sensor data via V2X communication has emerged as a promising approach for advanced autonomous driving. However, current research mainly focuses on improving individual modules, rather than taking end-to-end learning to optimize final planni…
Whole Slide Image (WSI) classification is often formulated as a Multiple Instance Learning (MIL) problem. Recently Vision-Language Models (VLMs) have demonstrated remarkable performance in WSI classification. However existing methods leverage coarse-grained pathogenetic descriptions for visual repre…
Motion forecasting is an essential task for autonomous driving, and utilizing information from infrastructure and other vehicles can enhance forecasting capabilities. Existing research mainly focuses on leveraging single-frame cooperative information to enhance the limited perception capability of t…
Cooperative perception can effectively enhance individual perception performance by providing additional viewpoint and expanding the sensing field. Existing cooperation paradigms are either interpretable (result cooperation) or flexible (feature cooperation). In this paper, we propose the concept of…
The value of roadside perception which could extend the boundaries of autonomous driving and traffic management has gradually become more prominent and acknowledged in recent years. However existing roadside perception approaches only focus on the single-infrastructure sensor system which cannot rea…
Utilizing infrastructure and vehicle-side information to track and forecast the behaviors of surrounding traffic participants can significantly improve decision-making and safety in autonomous driving. However, the lack of real-world sequential datasets limits research in this area. To address this…