论文标题
基于卡宾和驾驶场景监控的驾驶员意图期望
Driver Intention Anticipation Based on In-Cabin and Driving Scene Monitoring
论文作者
论文摘要
许多汽车事故是由驾驶不当引起的。但是，如果事先检测到此类驾驶操作并相应地协助驾驶员，则可以避免严重伤害。实际上，最近的各种研究集中在基于手工制作的功能的自动预测中，主要是从卡宾内驾驶员视频中提取的。由于来自交通现场的外部视图也可能包含用于驾驶操作预测的内容丰富的功能，因此我们提出了一个基于卡宾和交通现场视频的驾驶员的意图的框架。更具体地说，我们（1）提出了一个基于卷积的LSTM（ConvlstM）自动编码器，以从外部流量中提取运动功能，（2）培训一个分类器，该分类器在机舱内和外部共同考虑手动意向性预期的动作，（3）实验，（3）实验表明，在内外和外部外部图像互补图像具有互补图像的特征。我们基于公开数据集Brain4Car的评估表明，我们的框架以83.98％的准确性和84.3％的精度实现了预测。
Numerous car accidents are caused by improper driving maneuvers. Serious injuries are however avoidable if such driving maneuvers are detected beforehand and the driver is assisted accordingly. In fact, various recent research has focused on the automated prediction of driving maneuver based on hand-crafted features extracted mainly from in-cabin driver videos. Since the outside view from the traffic scene may also contain informative features for driving maneuver prediction, we present a framework for the detection of the drivers' intention based on both in-cabin and traffic scene videos. More specifically, we (1) propose a Convolutional-LSTM (ConvLSTM)-based auto-encoder to extract motion features from the out-cabin traffic, (2) train a classifier which considers motions from both in- and outside of the cabin jointly for maneuver intention anticipation, (3) experimentally prove that the in- and outside image features have complementary information. Our evaluation based on the publicly available dataset Brain4cars shows that our framework achieves a prediction with the accuracy of 83.98% and F1-score of 84.3%.
