Abstract
Deep neural networks (DNNs) have become remarkably successful in data prediction, and have even been used to predict future actions based on limited input. This raises the question: do these systems actually “understand” the event similar to humans? Here, we address this issue using videos taken from an accident situation in a driving simulation. In this situation, drivers had to choose between crashing into a suddenly-appeared obstacle or steering their car off a previously indicated cliff. We compared how well humans and a DNN predicted this decision as a function of time before the event. The DNN outperformed humans for early time-points, but had an equal performance for later time-points. Interestingly, spatio-temporal image manipulations and Grad-CAM visualizations uncovered some expected behavior, but also highlighted potential differences in temporal processing for the DNN.
| Original language | English |
|---|---|
| Title of host publication | Pattern Recognition - 6th Asian Conference, ACPR 2021, Revised Selected Papers |
| Editors | Christian Wallraven, Qingshan Liu, Hajime Nagahara |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 127-141 |
| Number of pages | 15 |
| ISBN (Print) | 9783031024436 |
| DOIs | |
| Publication status | Published - 2022 |
| Event | 6th Asian Conference on Pattern Recognition, ACPR 2021 - Virtual, Online Duration: 9 Nov 2021 → 12 Nov 2021 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 13189 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | 6th Asian Conference on Pattern Recognition, ACPR 2021 |
|---|---|
| City | Virtual, Online |
| Period | 9/11/21 → 12/11/21 |
Bibliographical note
Publisher Copyright:© 2022, Springer Nature Switzerland AG.
Keywords
- Decision-making
- Deep learning
- Humans versus machines
- Video analysis
- Video prediction
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