A Novel Fuzzy Vehicle Detection and Tracking Framework Using Fuzzy Edge Membership and Motion Similarity Relations

Engineering notes

Key topics: autonomous driving. 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

Abstract - Vehicle detection and tracking are essential components of intelligent transportation systems, traffic surveillance, and autonomous driving applications. Conventional detection and tracking approaches employ crisp decision-making mechanisms that often suffer from uncertainty caused by illumination variations, occlusions, shadows, camera vibrations, and complex traffic scenarios. To overcome these limitations, this paper proposes a novel Fuzzy Vehicle Detection and Tracking Framework (FVDTF) based on fuzzy edge membership, fuzzy motion similarity, and fuzzy association relations. The proposed framework incorporates fuzzy set theory to model uncertainty during vehicle detection, feature extraction, object association, and trajectory estimation. A Cartesian fuzzy vehicle relation is defined to measure the similarity between vehicle observations across consecutive frames. Several theoretical properties of the proposed model, including symmetry, boundedness, monotonicity, and closure under Cartesian products, are derived and proved mathematically. The proposed framework provides a strong theoretical foundation for the development of intelligent vehicle monitoring systems and future fuzzy-based traffic surveillance models. The methodology can be extended to intuitionistic fuzzy, Pythagorean fuzzy, and q-rung Ortho pair fuzzy environments for enhanced decision-making under uncertainty.

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