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

arXiv: https://arxiv.org/abs/1907.00749 · HTML: https://mlsanity.com/paper/1907.00749/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2019.

## Built on by (most cited first, 2 of 2)

- [Memory-augmented Adversarial Autoencoders for Multivariate Time-series Anomaly Detection with Deep Reconstruction and Prediction](https://mlsanity.com/paper/2110.08306/) (2021)
- [Rare Life Event Detection via Mobile Sensing Using Multi-Task Learning](https://mlsanity.com/paper/2305.20056/) (2023)

## Abstract (arXiv)

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.