Detecting Driver's Distraction using Long-term Recurrent Convolutional Network
In this study we demonstrate a novel Brain Computer Interface (BCI) approach to detect driver distraction events to improve road safety. We use a commercial wireless headset that generates EEG signals from the brain. We collected real EEG signals from participants who undertook a 40-minute driving simulation and were required to perform different tasks while driving. These signals are segmented into short windows and labelled using a time series classification (TSC) model. We studied different TSC approaches and designed a Long-term Recurrent Convolutional Network (LCRN) model for this task. Our results showed that our LRCN model performs better than the state of the art TSC models at detecting driver distraction events.
Code (0)
등록된 구현이 없습니다.
Tasks
Brain Computer InterfaceEEGElectroencephalogram (EEG)Time SeriesTime Series AnalysisTime Series ClassificationSimilar Papers 제목 키워드 기반
Driver distraction detection and recognition using RGB-D sensor
Driver inattention assessment has become a very active field in intelligent transportation systems. Based on active sensor Kinect and computer vision tools, we have built an efficient module for detecting driver distract…
Drive-Net: Convolutional Network for Driver Distraction Detection
To help prevent motor vehicle accidents, there has been significant interest in finding an automated method to recognize signs of driver distraction, such as talking to passengers, fixing hair and makeup, eating and drin…
ViT-DD: Multi-Task Vision Transformer for Semi-Supervised Driver Distraction Detection
Ensuring traffic safety and mitigating accidents in modern driving is of paramount importance, and computer vision technologies have the potential to significantly contribute to this goal. This paper presents a multi-mod…
Self-LearningWhen Vehicles See Pedestrians with Phones:A Multi-Cue Framework for Recognizing Phone-based Activities of Pedestrians
The intelligent vehicle community has devoted considerable efforts to model driver behavior, and in particular to detect and overcome driver distraction in an effort to reduce accidents caused by driver negligence. Howev…
Activity RecognitionDecision MakingPose EstimationMultimodal Polynomial Fusion for Detecting Driver Distraction
Distracted driving is deadly, claiming 3,477 lives in the U.S. in 2015 alone. Although there has been a considerable amount of research on modeling the distracted behavior of drivers under various conditions, accurate au…