3 accepted papers
Large multimodal language models (MLLMs) have revolutionized natural language processing and visual understanding, but often contain outdated or inaccurate information. Current multimodal knowledge editing evaluations are limited in scope and potentially biased, focusing on narrow tasks and failing…
Autonomous driving vehicles play a critical role in intelligent transportation systems and have garnered considerable attention. Currently, the popular approach in autonomous driving systems is to design separate optimal objectives for each independent module. Therefore, a major concern arises from
Multi-agent reinforcement learning (MARL) methods have emerged as a promising solution for multi-agent collaborative driving in the intersection and roundabout scenarios. However, these methods need large amounts of training data obtained from the interaction with the driving simulator, and learning