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

Engineering notes

By leveraging this dataset, our system achieves high-precision planning accuracy through a logical thinking process.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

There are two crucial aspects of reliable autonomous driving systems: the reasoning behind decision-making and the precision of environmental perception. This paper introduces DME-Driver, a new autonomous driving system that enhances performance and robustness by fully leveraging the two crucial aspects. This system comprises two main models. The first, the Decision Maker, is responsible for providing logical driving instructions. The second, the Executor, receives these instructions and generates precise control signals for the vehicles. To ensure explainable and reliable driving decisions, we build the Decision-Maker based on a large vision language model. This model follows the logic employed by experienced human drivers and simulates making decisions in a safe and reasonable manner. On the other hand, the generation of accurate control signals relies on precise and detailed environmental perception, where 3D scene perception models excel. Therefore, a planning-oriented perception model is employed as the Executor. It translates the logical decisions made by the Decision-Maker into accurate control signals for the self-driving cars. To effectively train the proposed system, a new dataset named Human-driver Behavior and Decision-making (HBD) dataset has been collected. This dataset encompasses a diverse range of human driver behaviors and their underlying motivations. By leveraging this dataset, our system achieves high-precision planning accuracy through a logical thinking process.

Links and sources

• Official / arXiv page
• PDF from original source

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