提出新方法提升自动驾驶目标检测的安全评估准确率
Criticality Metrics for Relevance Classification in Safety Evaluation of Object Detection in Automated Driving
- 设计双向评分与多指标融合策略增强关键性判断
- 在DeepAccident数据集上实现关键性分类准确率最高提升100%
- 适合自动驾驶安全评测与感知系统验证的研究者
确保安全性是自动驾驶的首要目标，要求对环境进行全面且准确的感知。尽管已有多种性能评估指标用于衡量感知能力，但引入面向安全的特定指标对于可靠评估目标检测系统至关重要。安全评估的关键在于区分相关与不相关物体，这正是关键性或相关性指标的核心任务。本文首次深入分析了用于自动驾驶目标检测系统安全评估的关键性指标。通过全面回顾现有文献，我们识别并评估了一系列适用指标。其有效性在包含多种安全关键场景的DeepAccident数据集上得到实证验证。为提升评估准确性，我们提出了两种新型应用策略：双向关键性评分和多指标聚合。实验表明，该方法在关键性分类准确率上最高提升达100%，凸显其显著提升自动驾驶目标检测系统安全评估能力的潜力。
原文摘要 · Abstract (English)
Ensuring safety is the primary objective of automated driving, which necessitates a comprehensive and accurate perception of the environment. While numerous performance evaluation metrics exist for assessing perception capabilities, incorporating safety-specific metrics is essential to reliably evaluate object detection systems. A key component for safety evaluation is the ability to distinguish between relevant and non-relevant objects - a challenge addressed by criticality or relevance metrics. This paper presents the first in-depth analysis of criticality metrics for safety evaluation of object detection systems. Through a comprehensive review of existing literature, we identify and assess a range of applicable metrics. Their effectiveness is empirically validated using the DeepAccident dataset, which features a variety of safety-critical scenarios. To enhance evaluation accuracy, we propose two novel application strategies: bidirectional criticality rating and multi-metric aggregation. Our approach demonstrates up to a 100% improvement in terms of criticality classification accuracy, highlighting its potential to significantly advance the safety evaluation of object detection systems in automated vehicles.
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