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Predicting vehicle merging behavior for automated driving

Paper Authors:

Ni Dong,

Shuming Chen,

Yina Wu,

Yiheng Feng,

Xiaobo Liu


Key Details

Classifies human driver styles as aggressive or normal with clustering

Predicts if a merging vehicle will drive aggressively or normally

Uses a transformer model to predict merging trajectories 30 seconds ahead

Plans automated vehicle motions earlier based on predictions

Increases time-to-collision between vehicles by using predictions

AI generated summary

Predicting vehicle merging behavior for automated driving

This paper proposes a model to predict surrounding vehicles' merging behavior and trajectories when entering a highway. The model classifies driving styles and forecasts long-term paths using machine learning. It enables automated vehicles to plan motions farther in advance for safety.

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