用无监督学习识别自动驾驶中的罕见危险场景。
Unsupervised Learning for Detection of Rare Driving Scenarios
- 基于深度隔离森林检测复杂异常，融合神经网络与统计方法。
- 在自然驾驶数据上验证，有效发现罕见高危驾驶事件。
- 适合关注自动驾驶安全性的研究者与工程师。
自动驾驶系统中罕见且危险驾驶场景的检测是保障安全可靠性的关键挑战。本研究提出一种基于自然驾驶数据（NDD）的无监督学习框架，利用近期提出的深度隔离森林（DIF）算法——结合神经网络特征表示与隔离森林（IFs），识别非线性复杂异常。将感知模块输出的车辆动态与环境条件数据，通过滑动窗口预处理为结构化统计特征。框架引入t分布随机邻域嵌入（t-SNE）进行降维与可视化，提升异常检测结果的可解释性。评估采用代理真实标签，结合定量指标与定性视频帧分析。结果表明，该方法能有效识别罕见且危险的驾驶场景，为自动驾驶系统提供可扩展的异常检测方案。然而，研究依赖代理真实标签及人工定义的特征组合，未能涵盖所有真实世界驾驶异常及其细微情境依赖关系。
原文摘要 · Abstract (English)
The detection of rare and hazardous driving scenarios is a critical challenge for ensuring the safety and reliability of autonomous systems. This research explores an unsupervised learning framework for detecting rare and extreme driving scenarios using naturalistic driving data (NDD). We leverage the recently proposed Deep Isolation Forest (DIF), an anomaly detection algorithm that combines neural network-based feature representations with Isolation Forests (IFs), to identify non-linear and complex anomalies. Data from perception modules, capturing vehicle dynamics and environmental conditions, is preprocessed into structured statistical features extracted from sliding windows. The framework incorporates t-distributed stochastic neighbor embedding (t-SNE) for dimensionality reduction and visualization, enabling better interpretability of detected anomalies. Evaluation is conducted using a proxy ground truth, combining quantitative metrics with qualitative video frame inspection. Our results demonstrate that the proposed approach effectively identifies rare and hazardous driving scenarios, providing a scalable solution for anomaly detection in autonomous driving systems. Given the study's methodology, it was unavoidable to depend on proxy ground truth and manually defined feature combinations, which do not encompass the full range of real-world driving anomalies or their nuanced contextual dependencies.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。