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.
Vehicle Localization Using Convolutional Neural Networks with IMM-EKF for Automated Vertical Parking
08.10.2022
1415955 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Automated Vehicle Recognition with Deep Convolutional Neural Networks
Transportation Research Record | 2017
|Automated parking system, automated parking vehicle, and automated parking method
Europäisches Patentamt | 2021
|AUTOMATED PARKING SYSTEM, AUTOMATED PARKING VEHICLE, AND AUTOMATED PARKING METHOD
Europäisches Patentamt | 2020
|AUTOMATED PARKING SYSTEM, AUTOMATED PARKING VEHICLE, AND AUTOMATED PARKING METHOD
Europäisches Patentamt | 2018
|