Name
Abstract | ||
|---|---|---|
In this paper, we propose an approach to fuse various available cues, such as vanishing point of the road, moving direction of the moving target, road edges, road textures, and cues with vertical edges, which extracted only from images of the road scene, to predict the moving direction of the autonomous vehicle. Firstly, the Gaussian models of the moving direction of the autonomous vehicle is constructed by using above cues respectively. Then, the prediction results of different cues are fused under the Bayesian framework to estimate the most reasonable moving direction of autonomous vehicle. We test the algorithm in our campus road scene. Compared with the prediction result of single cue, our fusion algorithm effectively improves the robustness of the prediction, and It has certain reference significance for local navigation and path planning for the autonomous vehicle. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICInfA.2018.8812543 | 2018 IEEE International Conference on Information and Automation (ICIA) |
Keywords | Field | DocType |
Multi-cue,Moving direction prediction,Gaussian model,Bayesian framework | Motion planning,Computer vision,Control theory,Computer science,Fusion,Robustness (computer science),Gaussian,Artificial intelligence,Gaussian network model,Fuse (electrical),Vanishing point,Bayesian probability | Conference |
ISBN | Citations | PageRank |
978-1-5386-8070-4 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weizhong Jiang | 1 | 0 | 0.34 |
| Tao Wu | 2 | 58 | 11.53 |
| Zhipeng Xiao | 3 | 32 | 2.48 |
| Shaowei Li | 4 | 0 | 0.34 |
| Shuai Zhang | 5 | 0 | 0.34 |