Name
Abstract | ||
|---|---|---|
Vehicles of higher automation levels require the creation of situation awareness. One important aspect of this situation awareness is an understanding of the current risk of a driving situation. In this work, we present a novel approach for the dynamic risk assessment of driving situations based on images of a front stereo camera using deep learning. To this end, we trained a deep neural network with recorded monocular images, disparity maps and a risk metric for diverse traffic scenes. Our approach can be used to create the aforementioned situation awareness of vehicles of higher automation levels and can serve as a heterogeneous channel to systems based on radar or lidar sensors that are used traditionally for the calculation of risk metrics. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-99229-7_48 | COMPUTER SAFETY, RELIABILITY, AND SECURITY, SAFECOMP 2018 |
DocType | Volume | ISSN |
Conference | 11094 | 0302-9743 |
Citations | PageRank | References |
0 | 0.34 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Patrik Feth | 1 | 6 | 2.19 |
| Mohammed Naveed Akram | 2 | 0 | 0.34 |
| René Schuster | 3 | 14 | 5.44 |
| Oliver Wasenmüller | 4 | 21 | 6.24 |