Name
Title | ||
|---|---|---|
Deep Multi-Task Learning For Anomalous Driving Detection Using Can Bus Scalar Sensor 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 normal situations (e.g., right turn, going straight), some of which (e.g., U-turn) could be as rare as anomalous ones. 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 (LSTM autoencoder and predictor) 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/existing approaches. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IROS40897.2019.8967753 | 2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) |
Field | DocType | ISSN |
CAN bus,Computer vision,Anomaly detection,Autoencoder,Multi-task learning,Computer science,Scalar (physics),Artificial intelligence,Machine learning | Conference | 2153-0858 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vidyasagar Sadhu | 1 | 15 | 5.96 |
| Teruhisa Misu | 2 | 19 | 5.89 |
| Dario Pompili | 3 | 2807 | 213.48 |