A Dual-Cameras-Based Driver Gaze Mapping System With an Application on Non-Driving Activities Monitoring
A Dual-Cameras-Based Driver Gaze Mapping System With an Application on Non-Driving Activities Monitoring
复制标题
DOI：
10.1109/tits.2019.2939676
复制
发表时间：
2020-10
影响因子：
8.5
通讯作者：
Lichao Yang;Kuo Dong;A. Dmitruk;J. Brighton;Yifan Zhao
中科院分区：
文献类型：
--
作者：
Lichao Yang;Kuo Dong;A. Dmitruk;J. Brighton;Yifan Zhao
驾驶员非驾驶活动的特征化对于3级自动化中的接管控制策略的设计具有重要意义。视线估计是一种典型的方法来监测驾驶员的行为，因为眼睛注视通常与人类活动。然而，目前的眼睛注视跟踪技术要么是昂贵的，要么是侵入性的，这限制了它们在车辆中的适用性。本文提出了一种低成本和非侵入性的双摄像头为基础的凝视映射系统，可视化驾驶员的目光使用热图。通过提出一个非线性多项式模型来建立模拟驾驶员视图上的面部特征和眼睛注视之间的关系，解决了NDA期间复杂的头部运动和相机失真带来的挑战。该系统在车载实验中，X、Y方向的均方根误差分别为7.80± 5.99pixel和4.64± 3.47pixel，图像分辨率为1440x1080pixel。该系统已成功地证明，以评估三个新的发展与视觉注意。这种技术，作为一个通用的工具，以监测驾驶员的视觉注意力，将有广泛的应用NDA表征的智能设计的接管策略和驾驶环境意识的当前和未来的自动驾驶汽车。
Characterisation of the driver’s non-driving activities (NDAs) is of great importance to the design of the take-over control strategy in Level 3 automation. Gaze estimation is a typical approach to monitor the driver’s behaviour since the eye gaze is normally engaged with the human activities. However, current eye gaze tracking techniques are either costly or intrusive which limits their applicability in vehicles. This paper proposes a low-cost and non-intrusive dual-cameras based gaze mapping system that visualises the driver’s gaze using a heat map. The challenges introduced by complex head movement during NDAs and camera distortion are addressed by proposing a nonlinear polynomial model to establish the relationship between the face features and eye gaze on the simulated driver’s view. The Root Mean Square Error of this system in the in-vehicle experiment for the X and Y direction is 7.80±5.99 pixel and 4.64±3.47 pixel respectively with the image resolution of $1440 \times 1080$ pixels. This system is successfully demonstrated to evaluate three NDAs with visual attention. This technique, acting as a generic tool to monitor driver’s visual attention, will have wide applications on NDA characterisation for intelligent design of take over strategy and driving environment awareness for current and future automated vehicles.