Unsupervised Learning for Detection of Rare Driving Scenarios

2025-12-29

AI Summary

To address the safety challenge of detecting rare hazardous driving scenarios—where supervised methods suffer from insufficient labeled data—this paper proposes an unsupervised anomaly detection framework. Methodologically, it introduces Deep Isolation Forest (DIF) to driving scene anomaly detection for the first time, integrating neural representation learning with the isolation mechanism and leveraging t-SNE for interpretable visualization. Additionally, it incorporates a sliding-window feature extraction strategy and multi-source spatiotemporal sensor data fusion. Experiments on real-world naturalistic driving data demonstrate high efficacy in identifying critical rare events, enabling both quantitative evaluation and video-level qualitative validation, while maintaining detection accuracy and engineering scalability. The core contributions are: (i) the pioneering application of DIF to autonomous driving anomaly detection, and (ii) an end-to-end, interpretable, and deployable unsupervised paradigm.

Technology Category

• Computer Vision: Scene Analysis & Understanding
• Machine Learning: Unsupervised & Self-Supervised Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Detects rare hazardous driving scenarios using unsupervised learning
• Identifies complex anomalies with Deep Isolation Forest algorithm
• Evaluates detection via proxy ground truth and visualization techniques

Innovation

• Unsupervised learning with Deep Isolation Forest for anomaly detection
• Preprocessing data into structured features from sliding windows
• Using t-SNE for dimensionality reduction and visualization