Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Lane-keeping assistance systems for vehicles may be more acceptable to users if the assistance was adaptive to the driver's state. To adapt systems in this way, a method for detection of driver distraction is needed. Thus,  propose a novel technique for online detection of driver's distraction, modeling the long-range temporal context of driving and head tracking data.  show that long short-term memory (LSTM) recurrent neural networks enable a reliable subject-independent detection of inattention with an accuracy of up to 96.6%. Thereby, our LSTM framework significantly outperforms conventional approaches such as support vector machines (SVMs).
['Martin Wöllmer', 'Christoph Blaschke', 'Thomas Schindl', 'Björn W. Schuller', 'Berthold Färber', 'Stefan Mayer', 'B Trefflich']
I am writing a research paper on a idea Online Driver Distraction Detection Using Long Short-Term Memory
[{"from": "human", "value": "I am writing a research paper on a idea Online Driver Distraction Detection Using Long Short-Term Memory"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Lane-keeping assistance systems for vehicles may be more acceptable to users if the assistance was adaptive to the driver's state. To adapt systems in this way, a method for detection of driver distraction is needed. Thus,  propose a novel technique for online detection of driver's distraction, modeling the long-range temporal context of driving and head tracking data.  show that long short-term memory (LSTM) recurrent neural networks enable a reliable subject-independent detection of inattention with an accuracy of up to 96.6%. Thereby, our LSTM framework significantly outperforms conventional approaches such as support vector machines (SVMs)."}]