DriveX explores the integration of foundation models and V2X-based cooperative systems to improve perception, planning, and decision-making in autonomous vehicles. While traditional single-vehicle systems have advanced tasks like 3D object detection, emerging challenges like holistic scene understanding and 3D occupancy prediction require more comprehensive solutions. Collaborative driving systems, utilizing V2X communication and roadside infrastructure, extend sensory range, provide hazard warnings, and improve decision-making through shared data. Simultaneously, Vision-Language Models (VLMs) offer generalization abilities, enabling zero-shot learning, open-vocabulary recognition, and scene explanation for novel scenarios. DriveX aims to bring together experts to explore these technologies, address challenges, and advance road safety.