用证据理论建模激光雷达检测不确定性，提升自动驾驶安全性。
Uncertainty Representation in a SOTIF-Related Use Case with Dempster-Shafer Theory for LiDAR Sensor-Based Object Detection
- 基于证据理论构建检测结果框架，融合多源不确定性。
- 通过敏感性分析量化各因素对检测精度的影响程度。
- 为自动驾驶安全验证提供可解释的不确定性评估方法。
激光雷达目标检测中的不确定性源于环境变化与传感器性能限制。为保障预期功能安全（SOTIF），需准确表征此类不确定性。本文提出一种系统化方法，用于识别、分类并表示激光雷达检测中的不确定性。采用达姆斯特定理（DST）构建辨识框架（FoD）以表达检测结果，根据不确定性来源间的依赖关系设定条件基本概率分配（BPAs）。利用雅格规则（Yager's Rule）融合多源冲突证据，形成结构化框架以评估不确定性对检测准确性的影响。研究进一步应用基于方差的敏感性分析（VBSA），量化并排序各项不确定性，明确其对检测性能的具体影响。
原文摘要 · Abstract (English)
Uncertainty in LiDAR sensor-based object detection arises from environmental variability and sensor performance limitations. Representing these uncertainties is essential for ensuring the Safety of the Intended Functionality (SOTIF), which focuses on preventing hazards in automated driving scenarios. This paper presents a systematic approach to identifying, classifying, and representing uncertainties in LiDAR-based object detection within a SOTIF-related scenario. Dempster-Shafer Theory (DST) is employed to construct a Frame of Discernment (FoD) to represent detection outcomes. Conditional Basic Probability Assignments (BPAs) are applied based on dependencies among identified uncertainty sources. Yager's Rule of Combination is used to resolve conflicting evidence from multiple sources, providing a structured framework to evaluate uncertainties' effects on detection accuracy. The study applies variance-based sensitivity analysis (VBSA) to quantify and prioritize uncertainties, detailing their specific impact on detection performance.
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