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Arxiv
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Prediction of Lane Change Intentions of Human Drivers using an LSTM, a CNN and a Transformer
- The study focuses on predicting lane change intentions of human drivers using LSTM, CNN, and Transformer networks.
- Lane changes of preceding vehicles significantly impact automated vehicle motion planning in complex traffic situations.
- Transformer networks outperformed LSTM and CNN in predicting lane change intentions and showed less susceptibility to overfitting.
- The accuracy of the method ranged from 82.79% to 96.73% for different input configurations, demonstrating promising performance in predicting human drivers' lane change intentions.
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