Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Multi-Task Learning for Anomalous Driving Detection Using CAN Bus
  Scalar Sensor 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.

7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
19
The categories of this paper are: eess
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph