# Deep Multi-Task Learning for Anomalous Driving Detection Using CAN Bus Scalar Sensor Data

## TL;DR

This paper introduces a semi-supervised multi-task learning approach leveraging domain knowledge to improve anomaly detection in imbalanced driving data, enhancing safety in autonomous driving systems.

## Contribution

It proposes a novel multi-task learning method that incorporates maneuver labels for better anomaly detection in driving data with imbalanced normal and abnormal situations.

## Key findings

- Improved detection performance over baseline methods.
- Effective handling of imbalanced normal and anomalous driving data.
- Validated on 150 hours of real-world driving data.

## Abstract

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.