[time]
2023-09-01T16:10:49+02:00

[track]
6

[team_name]
BJTU-DiDi

[team_institution]
School of Software Engineering, Beijing Jiaotong University

[logolink]


[team_members]


[reference_person]
Shuli Zhu, Yufei Su, Feng Liu, Haitao Li, Xuan Xiao, Yuqin Jiang, Ruipeng Gao

[reference_email]
zhushuli@bjtu.edu.cn

[description_short]
Different from the traditional speed estimation method based on attitude calculation and acceleration integration, we designed a supervised deep learning model (Speed DNN Model) for estimating vehicle speed, and estimated the vehicle heading (Bearing Estimation) decoupled from the vehicle speed estimation function in indoor environments. Meanwhile, in outdoor environments, we utilize Extended Kalman Filter (EKF) in order to improve the location of vehicle when GNSS signals and IMU in the smartphone are valid.

The overall workflow of DNN model is divided into two lines: model training and model inference. During model training, we use the provided testing data with high-quality GNSS information to build a training dataset with ground-truth speeds. Among them, we have designed two schemes for the use of inertial data. The first solution is to input the original sampled data (Raw Data) into the deep learning model without additional processing of the inertial data. The second s

[description_long_link]
https://github.com/Juderer/IPIN22_Track6/tree/dev-zhushuli/application2023

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true

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