== Abstract ==

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dvanced Driver Assistance Systems (ADAS) improve driving safety significantly. They alert drivers from unsafe traffic conditions when a dangerous maneuver appears. Traditional methods to predict driving maneuvers are mostly based on data-driven models alone. However, existing methods to understand the driver's intention remain an ongoing challenge due to a lack of intersection of human cognition and data analysis. To overcome this challenge, we propose a novel method that combines both the cognition-driven model and the data-driven model. We introduce a model named Cognitive Fusion-RNN (CF-RNN) which fuses the data inside the vehicle and the data outside the vehicle in a cognitive way. The CF-RNN model consists of two Long Short-Term Memory (LSTM) branches regulated by human reaction time. Experiments on the Brain4Cars benchmark dataset demonstrate that the proposed method outperforms previous methods and achieves state-of-the-art performance.

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== Original document ==

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The different versions of the original document can be found in:

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* [http://arxiv.org/abs/1805.02895 http://arxiv.org/abs/1805.02895]

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* [http://arxiv.org/pdf/1805.02895 http://arxiv.org/pdf/1805.02895]

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* [http://xplorestaging.ieee.org/ielx7/8694905/8698608/08698695.pdf?arnumber=8698695 http://xplorestaging.ieee.org/ielx7/8694905/8698608/08698695.pdf?arnumber=8698695],

: [http://dx.doi.org/10.1109/vcip.2018.8698695 http://dx.doi.org/10.1109/vcip.2018.8698695]

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* [https://dblp.uni-trier.de/db/journals/corr/corr1805.html#abs-1805-02895 https://dblp.uni-trier.de/db/journals/corr/corr1805.html#abs-1805-02895],

: [https://arxiv.org/abs/1805.02895 https://arxiv.org/abs/1805.02895],

: [https://arxiv.org/pdf/1805.02895.pdf https://arxiv.org/pdf/1805.02895.pdf],

: [https://ui.adsabs.harvard.edu/abs/2018arXiv180502895Z/abstract https://ui.adsabs.harvard.edu/abs/2018arXiv180502895Z/abstract],

: [https://academic.microsoft.com/#/detail/2964056922 https://academic.microsoft.com/#/detail/2964056922]

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