Name
Abstract | ||
|---|---|---|
The existing end-to-end methods make driving decisions mainly based on the vehicle's own perceived data, which cannot avoid hazards in blind zones. To fill this gap, vehicles should cooperate to construct a comprehensive environment perception by sharing information among each other, equipping each vehicle with see-through ability. While bringing more perceived information, data from other sources may also interfere feature selection and make decision making more difcult. To solve this problem, we propose a dual-attention-based neural network by utilizing two different attention modules. The first module is designed for each source to eliminate redundant features in perception and generate cognitive information for sharing. Since the influences of different cognition on the decision making are different under different circumstances, the second module is used to discriminate the importance of different cognition and focus on the dominant one as needed. Guided by the dual-attention-weighted features, the proposed network extracts the most salient features from the multi-source data, which leads to a signicant reduction of false response in steering angle controlling. Extensive experiments have demonstrated the superior performance of our proposed method, as compared with several state-of-the-arts. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/VTC2020-Fall49728.2020.9348588 | 2020 IEEE 92ND VEHICULAR TECHNOLOGY CONFERENCE (VTC2020-FALL) |
Keywords | DocType | Citations |
Cooperative autonomous driving, decision making, multi-source data, dual attention | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fanqi Xu | 1 | 0 | 0.34 |
| Jinglin Li | 2 | 150 | 30.39 |
| Quan Yuan | 3 | 55 | 11.07 |
| Guiyang Luo | 4 | 23 | 4.35 |