Decision Tree-Dominated Robotic Intelligent Systems: A Review of Feature Dimensionality Reduction and Explainable Architectures in Perception and Navigation

Engineering notes

Key topics: autonomous driving, perception. See the paper for implementation details and experimental results.

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

With advances in autonomous navigation, decision-tree based intelligent systems have gained popularity because of their well-defined structure and clearly interpretable reasoning process. We investigate here the recent progress in key challenges faced by the decision-tree model for robotic perception and decision making such as handling high-dimensional sensor data online, being expressive enough in complex nonlinear environments, etc. Trade-offs are found between the interpretability of various learned models and their performance. Furthermore, how robust they are when put into practice. And apart from tackling these challenges, in this section we outline future directions on research, such as: integrating decision trees and deep neural networks or graph-based models; proposing adaptive FCMs to speed up sample traversal; developing hierarchical interpretability; constructing neuro-symbolic hybrid systems; and building self-evolving DMs for open-world navigation.

Links and sources

• PDF from original source

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