  The utilization of Large Language Models (LLMs) within the realm of
reinforcement learning, particularly as planners, has garnered a significant
degree of attention in recent scholarly literature. However, a substantial
proportion of existing research predominantly focuses on planning models for
robotics that transmute the outputs derived from perception models into
linguistic forms, thus adopting a `pure-language' strategy. In this research,
we propose a hybrid End-to-End learning framework for autonomous driving by
combining basic driving imitation learning with LLMs based on multi-modality
prompt tokens. Instead of simply converting perception results from the
separated train model into pure language input, our novelty lies in two
aspects. 1) The end-to-end integration of visual and LiDAR sensory input into
learnable multi-modality tokens, thereby intrinsically alleviating description
bias by separated pre-trained perception models. 2) Instead of directly letting
LLMs drive, this paper explores a hybrid setting of letting LLMs help the
driving model correct mistakes and complicated scenarios. The results of our
experiments suggest that the proposed methodology can attain driving scores of
49.21%, coupled with an impressive route completion rate of 91.34% in the
offline evaluation conducted via CARLA. These performance metrics are
comparable to the most advanced driving models.
