As autonomous vehicles (AVs) advance, the integration of Large (Vision) Language Models (L(V)LMs) has emerged as a promising approach to enhance AV capabilities in perception, planning, decision-making, and data generation. However, the practical challenges of incorporating L(V)LMs into AV systems, including computational efficiency, real-time processing, and ethical considerations, remain under-explored. This survey aims to provide a comprehensive review of the current research on L(V)LM applications in AVs, focusing on four key areas: modular integration, end-to-end integration, data generation, and evaluation platforms. Our findings highlight the potential of L(V)LMs to improve AV system performance but emphasise the need for further research in real-world integration, regulatory challenges, and V2X communication. This survey offers valuable insights and guidance for researchers and practitioners aiming to optimise L(V)LMs in autonomous vehicles.