차량 간 상호작용을 고려한 주변 차량 경로 예측Path prediction of surrounding vehicle considering vehicle interaction
- Other Titles
- Path prediction of surrounding vehicle considering vehicle interaction
- Authors
- 김기훈; 이준호; 안윤용; 백종윤; 허건수
- Issue Date
- Jul-2020
- Publisher
- 한국자동차공학회
- Keywords
- Deep learning(딥러닝); Interaction(상호작용); Attention module(주의 모듈); Hierarchical Structure(계층 구조); Masking(차폐)
- Citation
- 2020 한국자동차공학회 춘계학술대회, pp.490 - 493
- Indexed
- OTHER
- Journal Title
- 2020 한국자동차공학회 춘계학술대회
- Start Page
- 490
- End Page
- 493
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/4447
- ISSN
- 2713-7163
- Abstract
- In certain situations where lane changes take place frequently, such as at highway junctions or urban driveways, the dynamic interactions between neighboring and self-driving vehicles occur in a complex way. Therefore, it is necessary to consider both static elements such as lanes, and dynamic elements such as surrounding vehicles, for the trajectory planning of autonomous vehicles. This study suggests a deep learning network that predicts the trajectory of surrounding vehicles including ego-vehicles by considering the interactions between static and dynamic factors in the surroundings. The proposed network employed Encoder-Decorder structure, and applied additional Multi-head attention module, which is directly connected to the encoder, in effort to consider the interactions between self-driving and neighboring vehicles. Also, by applying masking technique, the situations where the number of vehicles observed and history are changing are taken into a consideration. Lastly, the proposed network has been verified through highD (Highway Drone) open dataset.
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