実世界シナリオからの自動運転車ドライバーモニタリングデータセット
Automated Vehicle Driver Monitoring Dataset from Real-World Scenarios
自動運転中のドライバー活動を認識するための、様々な照明・天候条件下で収集された実世界データセットを公開し、そのベンチマーク性能を示した。
著者: Mohamed Sabry, Walter Morales-Alvarez, Cristina Olaverri-Monreal
分類: cs.RO
原文アブストラクト
From SAE Level 3 of automation onwards, drivers are allowed to engage in activities that are not directly related to driving during their travel. However, in level 3, a misunderstanding of the capabilities of the system might lead drivers to engage in secondary tasks, which could impair their ability to react to challenging traffic situations. Anticipating driver activity allows for early detection of risky behaviors, to prevent accidents. To be able to predict the driver activity, a Deep Learning network needs to be trained on a dataset. However, the use of datasets based on simulation for training and the migration to real-world data for prediction has proven to be suboptimal. Hence, this paper presents a real-world driver activity dataset, openly accessible on IEEE Dataport, which encompasses various activities that occur in autonomous driving scenarios under various illumination and weather conditions. Results from the training process showed that the dataset provides an excellent benchmark for implementing models for driver activity recognition.
