In the field of autonomous driving, two important features of autonomous
driving car systems are the explainability of decision logic and the accuracy
of environmental perception. This paper introduces DME-Driver, a new autonomous
driving system that enhances the performance and reliability of autonomous
driving system. DME-Driver utilizes a powerful vision language model as the
decision-maker and a planning-oriented perception model as the control signal
generator. To ensure explainable and reliable driving decisions, the logical
decision-maker is constructed based on a large vision language model. This
model follows the logic employed by experienced human drivers and makes
decisions in a similar manner. On the other hand, the generation of accurate
control signals relies on precise and detailed environmental perception, which
is where 3D scene perception models excel. Therefore, a planning oriented
perception model is employed as the signal generator. It translates the logical
decisions made by the decision-maker into accurate control signals for the
self-driving cars. To effectively train the proposed model, a new dataset for
autonomous driving was created. This dataset encompasses a diverse range of
human driver behaviors and their underlying motivations. By leveraging this
dataset, our model achieves high-precision planning accuracy through a logical
thinking process.

DME-Driver 是一种新的自动驾驶系统，通过利用强大的视觉语言模型作为决策者和以规划导向的感知模型作为控制信号生成器，以提高自动驾驶系统的性能和可靠性。