Name
Title | ||
|---|---|---|
End-to-end Uncertainty-based Mitigation of Adversarial Attacks to Automated Lane Centering |
Abstract | ||
|---|---|---|
In the development of advanced driver-assistance systems (ADAS) and autonomous vehicles, machine learning techniques that are based on deep neural networks (DNNs) have been widely used for vehicle perception. These techniques offer significant improvement on average perception accuracy over traditional methods, however have been shown to be susceptible to adversarial attacks, where small perturbat... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IV48863.2021.9575549 | 2021 IEEE Intelligent Vehicles Symposium (IV) |
Keywords | DocType | ISSN |
Deep learning,Adaptation models,Uncertainty,Perturbation methods,Estimation,Data models,Planning | Conference | 1931-0587 |
ISBN | Citations | PageRank |
978-1-7281-5394-0 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruochen Jiao | 1 | 0 | 0.34 |
| Hengyi Liang | 2 | 11 | 4.35 |
| Takami Sato | 3 | 0 | 0.34 |
| Junjie Shen | 4 | 2 | 4.46 |
| Qi Alfred Chen | 5 | 0 | 0.34 |
| Qi Zhu | 6 | 7 | 1.86 |