The application of Bayesian filter and neural networks in lane changing prediction

DOI

10.2991/iccet-15.2015.375How to use a DOI?

Keywords

Lane change intent; BP neural network; Bayesian filter; Prediction; Correction

Abstract

In order to improve safety during lane change, we proposed lane change intent prediction method based on neural networks and Bayesian filters. The method uses the lane line sensor, steering wheel angle sensor and in-vehicle CAN bus acquisition characterization parameters. The above acquisition parameters as the neural network input data, driver’s lane change intention preliminary forecast, take the output of BP neural network as the input of Bayesian filters, and then amendments the results of BP neural network. Using real vehicle lane changing data training and testing the model. The results show that prediction accuracy rate of BP neural network and Bayesian filters reaches 91.38%. The forecast accuracy increased by 6 percentage points compare to single BP neural network and has better versatility.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Li Li
AU  - Mingfang Zhang
AU  - Rui Liu
PY  - 2015/11
DA  - 2015/11
TI  - The application of Bayesian filter and neural networks in lane changing prediction
BT  - Proceedings of the 5th International Conference on Civil Engineering and Transportation 2015
PB  - Atlantis Press
SP  - 2004
EP  - 2007
SN  - 2352-5401
UR  - https://doi.org/10.2991/iccet-15.2015.375
DO  - 10.2991/iccet-15.2015.375
ID  - Li2015/11
ER  -