DME-Driver: Integrating Human Decision Logic and 3D Scene Perception in Autonomous Driving

• URL: http://arxiv.org/abs/2401.03641v1
• Date: Mon, 8 Jan 2024 03:06:02 GMT
• Title: DME-Driver: Integrating Human Decision Logic and 3D Scene Perception in Autonomous Driving
• Authors: Wencheng Han, Dongqian Guo, Cheng-Zhong Xu, Jianbing Shen
• Abstract summary: This paper introduces 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. By leveraging this dataset, our model achieves high-precision planning accuracy through a logical thinking process.
• Score: 65.04871316921327
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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