DMD: A Large-Scale Multi-modal Driver Monitoring Dataset for Attention and Alertness Analysis

Ortega, Juan Diego; Köse, Neslihan; Cañas, Paola; Chao, Min-An; Unnervik, Alexander; Nieto, Marcos; Otaegui, Oihana; Salgado, Luís

doi:10.1007/978-3-030-66823-5_23

Cited by 56 publications

(29 citation statements)

References 46 publications

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“…In 2020, a new Driver Monitoring Dataset (DMD) was presented in [13]. All recordings were made from three invehicle perspectives with three cameras positioned to capture the driver's face, hands, and body.…”

Section: A Data Sourcesmentioning

confidence: 99%

Robust Deep Learning-Based Driver Distraction Detection and Classification

et al. 2021

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Driver distraction is a major cause of road accidents. Distracting activities while driving include text messaging and talking on the phone. In this paper, we propose a robust driver distraction detection system that extracts the driver's state from the recordings of an onboard camera using Deep Learning. We consider ten driving activities, which consist of one normal driving and nine distracted driving behaviors. Nine drivers were included in the experiments, and each one was asked to perform the ten activities in naturalistic and simulated driving situations. The main feature of the proposed solution is the extraction of the driver's body parts, using deep learning-based segmentation, before performing the distraction detection and classification task. Experimental results show that the segmentation module significantly improves the classification performance. The average accuracy of the proposed solution exceeds 96% on our dataset and 95% on the public AUC dataset.

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Section: A Data Sourcesmentioning

confidence: 99%

Robust Deep Learning-Based Driver Distraction Detection and Classification

et al. 2021

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“…As researchers gave more attention to this field, more distraction actions were considered. More datasets were made available with more distraction actions are being taken into account [ 8 , 15 , 16 , 17 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Optimally-Weighted Image-Pose Approach (OWIPA) for Distracted Driver Detection and Classification

Koay

Chuah

Chow

et al. 2021

Sensors

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Distracted driving is the prime factor of motor vehicle accidents. Current studies on distraction detection focus on improving distraction detection performance through various techniques, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). However, the research on detection of distracted drivers through pose estimation is scarce. This work introduces an ensemble of ResNets, which is named Optimally-weighted Image-Pose Approach (OWIPA), to classify the distraction through original and pose estimation images. The pose estimation images are generated from HRNet and ResNet. We use ResNet101 and ResNet50 to classify the original images and the pose estimation images, respectively. An optimum weight is determined through grid search method, and the predictions from both models are weighted through this parameter. The experimental results show that our proposed approach achieves 94.28% accuracy on AUC Distracted Driver Dataset.

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“…. ⊆ {{u, v}|u, v and u = v} (12) where u and v represent the endpoints in the edges. For all the forwarding vehicles, the warning message is forwarded.…”

Section: Multimodal Features-based Driver Behavior Modellingmentioning

confidence: 99%

“…Most of the previous works in this field lack in identifying the various driver and vehicle parameters [11]. Such driver behavior monitoring suffers by not alerting roadside pedestrians and other vehicles that are traveling on the non-safe driver's path [12]. Some of the input parameters for monitoring driver behavior are gravity, throttle, speed, revolutions per minute, and acceleration.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Feature-Assisted Continuous Driver Behavior Analysis and Solving for Edge-Enabled Internet of Connected Vehicles Using Deep Learning

et al. 2021

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The emerging technology of internet of connected vehicles (IoCV) introduced many new solutions for accident prevention and traffic safety by monitoring the behavior of drivers. In addition, monitoring drivers’ behavior to reduce accidents has attracted considerable attention from industry and academic researchers in recent years. However, there are still many issues that have not been addressed due to the lack of feature extraction. To this end, in this paper, we propose the multimodal driver analysis internet of connected vehicles (MODAL-IoCV) approach for analyzing drivers’ behavior using a deep learning method. This approach includes three consecutive phases. In the first phase, the hidden Markov model (HMM) is proposed to predict vehicle motion and lane changes. In the second phase, SqueezeNet is proposed to perform feature extraction from these classes. Lastly, in the final phase, tri-agent-based soft actor critic (TA-SAC) is proposed for recommendation and route planning, in which each driver is precisely handled by an edge node for personalized assistance. Finally, detailed experimental results prove that our proposed MODAL-IoCV method can achieve high performance in terms of latency, accuracy, false alarm rate, and motion prediction error compared to existing works.

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DMD: A Large-Scale Multi-modal Driver Monitoring Dataset for Attention and Alertness Analysis

Cited by 56 publications

References 46 publications

Robust Deep Learning-Based Driver Distraction Detection and Classification

Robust Deep Learning-Based Driver Distraction Detection and Classification

Optimally-Weighted Image-Pose Approach (OWIPA) for Distracted Driver Detection and Classification

Multimodal Feature-Assisted Continuous Driver Behavior Analysis and Solving for Edge-Enabled Internet of Connected Vehicles Using Deep Learning

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