Action Conditioned Response Prediction with Uncertainty for Automated Vehicles
- Title
- Action Conditioned Response Prediction with Uncertainty for Automated Vehicles
- Author
- 허건수
- Keywords
- action conditioned prediction; mixture density network; response prediction; autonomous vehicle
- Issue Date
- 2019-12
- Publisher
- IEEE
- Citation
- 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), Page. 1-5
- Abstract
- Interaction-aware prediction is a critical component for realistic path planning that prevents automated vehicles from overly cautious driving. It requires to consider internal states of other driver such as driving style and intention, which the automated vehicle cannot directly measure. This paper proposes a probabilistic driver model for response prediction given the planned future actions of automated vehicle. The drivers internal states are considered in an unsupervised manner. The prediction model utilizes mixture density network to estimate future acceleration and yaw-rate profile of interacting vehicles. The proposed method is evaluated by using real-world trajectory data.
- URI
- https://ieeexplore.ieee.org/document/8986322https://repository.hanyang.ac.kr/handle/20.500.11754/157926
- ISBN
- 978-1-7281-3038-5
- ISSN
- 2642-3529
- DOI
- 10.1109/ISPACS48206.2019.8986322
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > AUTOMOTIVE ENGINEERING(미래자동차공학과) > Articles
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