Deep Multi-Task Learning for Anomalous Driving Detection Using CAN Bus Scalar Sensor Data
This work addresses safety-critical applications in autonomous driving by improving anomaly detection for imbalanced data, though it appears incremental as it builds on existing multi-task learning methods.
The paper tackles the problem of detecting anomalous driving situations in imbalanced CAN bus data by proposing a semi-supervised multi-task learning approach that leverages maneuver labels, showing improved performance over baselines on 150 hours of real-world driving data.
Corner cases are the main bottlenecks when applying Artificial Intelligence (AI) systems to safety-critical applications. An AI system should be intelligent enough to detect such situations so that system developers can prepare for subsequent planning. In this paper, we propose semi-supervised anomaly detection considering the imbalance of normal situations. In particular, driving data consists of multiple positive/normal situations (e.g., right turn, going straight), some of which (e.g., U-turn) could be as rare as anomalous situations. Existing machine learning based anomaly detection approaches do not fare sufficiently well when applied to such imbalanced data. In this paper, we present a novel multi-task learning based approach that leverages domain-knowledge (maneuver labels) for anomaly detection in driving data. We evaluate the proposed approach both quantitatively and qualitatively on 150 hours of real-world driving data and show improved performance over baseline approaches.