Name
Abstract | ||
|---|---|---|
With multi-sensor-based collision avoidance systems (CASs) being adopted in today's automobiles, a new method that enables collaborative decision-making with preceding vehicle detection under various external environments is needed. In this paper, spatial-temporal correlations of multi-channel signals that are collected by multiple sensors on the host vehicle are considered, and a multi-channel detection technique with a stochastic model is introduced for automobile collision avoidance. We propose an accurate and robust multi-channel, generalized likelihood ratio test (GLRT)-based detection and collaborative decision-making scheme, with a vehicle kinematic analysis for avoiding rear-end collisions. The results of simulations and physical experiments demonstrated that our detector expands the detection range with a high detection rate and that our proposed scheme obtains good performance under varying operating and environmental conditions. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TITS.2019.2930731 | IEEE Transactions on Intelligent Transportation Systems |
Keywords | DocType | Volume |
Collision avoidance,rear-end collisions,multi-channel detection,generalized likelihood ratio test (GLRT),spatial-temporal correlation | Journal | 21 |
Issue | ISSN | Citations |
8 | 1524-9050 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Xiang | 1 | 0 | 0.68 |
| Sida Huang | 2 | 0 | 0.34 |
| Min Li | 3 | 0 | 0.34 |
| Jin Li | 4 | 1 | 1.04 |
| Wenyong Wang | 5 | 35 | 4.05 |