An End to End Learning based Ego Vehicle Speed Estimation System

Estimating speed of a vehicle from only the video captured from within the vehicle without relying on any other supplementary information such as GPS coordinates or LIDAR data, is a challenging task. A deep neural network-based technique is proposed in this work that uses only the recordings from on...

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Bibliographic Details
Published in2021 IEEE International Power and Renewable Energy Conference (IPRECON) pp. 1 - 8
Main Authors Bandari, Hitesh Linganna, Nair, Binoy B
Format Conference Proceeding
LanguageEnglish
Published IEEE 24.09.2021
Subjects
Benchmark testing
Cameras
CNN-LSTM
Conferences
Data models
Ego vehicle speed
End-to-End Learning
Estimation
Laser radar
Renewable energy sources
Sequence-to-Sequence Regression
Speed Estimation
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Summary:Estimating speed of a vehicle from only the video captured from within the vehicle without relying on any other supplementary information such as GPS coordinates or LIDAR data, is a challenging task. A deep neural network-based technique is proposed in this work that uses only the recordings from onboard dash camera, to estimate speed of the ego car. Three models are designed using various combinations of LSTM and CNN and evaluated on two benchmark datasets. From the results, it is observed that the proposed systems are well capable of generating estimates of the ego vehicle speeds with good accuracy
DOI:10.1109/IPRECON52453.2021.9640927