What Can Be Predicted from Six Seconds of Driver Glances?

論文URL:http://dl.acm.org/citation.cfm?doid=3025453.3025929

論文アブストラクト:We consider a large dataset of real-world, on-road driving from a 100-car naturalistic study to explore the predictive power of driver glances and, specifically, to answer the following question: what can be predicted about the state of the driver and the state of the driving environment from a 6-second sequence of macro-glances? The context-based nature of such glances allows for application of supervised learning to the problem of vision-based gaze estimation, making it robust, accurate, and reliable in messy, real-world conditions. So, it's valuable to ask whether such macro-glances can be used to infer behavioral, environmental, and demographic variables? We analyze 27 binary classification problems based on these variables. The takeaway is that glance can be used as part of a multi-sensor real-time system to predict radio-tuning, fatigue state, failure to signal, talking, and several environment variables.

日本語のまとめ:

運転者の視線映像(6秒間、4816個)を分析し、運転環境、運転者の状態/行動、運転者の年齢/性別を予測した。運転者の状態/行動の予測精度が高く、視線情報からリアルタイムに運転を支援するシステムの構築を目指していく。

(107文字)

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