Vehicle Localization Using Convolutional Neural Networks with IMM-EKF for Automated Vertical Parking
- Authors
- Seo, Ju Won; Kim, Jin Sung; Kim, Dae Jung; Quan, Ying Shuai; Chung, Chung Choo
- Issue Date
- Oct-2022
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Citation
- IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, v.2022-October, pp.1976 - 1981
- Indexed
- SCOPUS
- Journal Title
- IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
- Volume
- 2022-October
- Start Page
- 1976
- End Page
- 1981
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/172929
- DOI
- 10.1109/ITSC55140.2022.9922403
- ISSN
- 2153-0009
- Abstract
- This paper proposes a method of vehicle localization using Convolutional Neural Networks (CNN) with Interacting Multiple Model (IMM)-Extended Kalman Filter (EKF) for automated vertical parking. The conventional method for localizing a vehicle in a parking space extracts features from the parking space. It calculates the coordinates of a parking spot. Unlike the conventional methods, CNN provides the pose of the ego-vehicle in this paper. Then, to prevent jittering signals from the CNN, we use a model-based estimator, IMM-EKF, to correct the CNN output. The vehicle state is then corrected using IMM-EKF to prevent jittered estimation results. Although using the IMM-EKF does not noticeably reduce RMS errors in the pose, reductions of the maximum errors are attained up to 50%. From the experiment, the proposed method provides a smooth estimation performance of the vehicle localization compared to another method.
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