This article presents a synthetic distracted driving (SynDD2 - a continuum of SynDD1) dataset for machine learning models to detect and analyze drivers' various distracted behavior and different gaze zones. We collected the data in a stationary vehicle using three in-vehicle cameras positioned at locations: on the dashboard, near the rearview mirror, and on the top right-side window corner. The dataset contains two activity types: distracted activities and gaze zones for each participant, and each activity type has two sets: without appearance blocks and with appearance blocks such as wearing a hat or sunglasses. The order and duration of each activity for each participant are random. In addition, the dataset contains manual annotations for each activity, having its start and end time annotated. Researchers could use this dataset to evaluate the performance of machine learning algorithms to classify various distracting activities and gaze zones of drivers.
Nagoya University（名古屋大学）；Navier, Inc.（Navier公司）；The University of Tokyo（东京大学）；Ritsumeikan University（立命馆大学）；Hitotsubashi University（一桥大学）
The number of traffic accidents has been continuously increasing in recent years worldwide. Many accidents are caused by distracted drivers, who take their attention away from driving. Motivated by the success of Convolutional Neural Networks (CNNs) in computer vision, many researchers developed CNN-based algorithms to recognize distracted driving from a dashcam and warn the driver against unsafe behaviors. However, current models have too many parameters, which is unfeasible for vehicle-mounted computing. This work proposes a novel knowledge-distillation-based framework to solve this problem.…
Technical University of Munich（慕尼黑工业大学）；The American University in Cairo（开罗美国大学）
In this paper, we present a new dataset for "distracted driver" posture estimation. In addition, we propose a novel system that achieves 95.98% driving posture estimation classification accuracy. The system consists of a genetically-weighted ensemble of Convolutional Neural Networks (CNNs). We show that a weighted ensemble of classifiers using a genetic algorithm yields in better classification confidence. We also study the effect of different visual elements (i.e. hands and face) in distraction detection and classification by means of face and hand localizations. Finally, we present a thinned…
MIFI: MultI-camera Feature Integration for Roust 3D Distracted Driver Activity Recognition
High Magnetic Field Laboratory, HFIPS, Chinese Academy of Sciences（中国科学院合肥物质科学研究院强磁场科学中心）；University of Science and Technology of China（中国科学技术大学）；High Magnetic Field Laboratory of Anhui Province（安徽省强磁场实验室）
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 automatic detection using multiple modalities and especially the contribution of using the speech modality to improve accuracy has received little attention. This paper introduces a new multimodal dataset for distracted driving behavior and discusses automatic distraction detection using features from three modalities: facial expression, speech and car signals. Detailed multimoda…
ALERT Open Dataset and Input-Size-Agnostic Vision Transformer for Driver Activity Recognition using IR-UWB
Department of ICE, Hanbat National University, Daejeon, Republic of Korea（信息电子工程系，翰 bam国立大学）；Department of ECE and INMC, Seoul National University, Seoul, Republic of Korea（电子工程系和智能纳米材料中心，首尔国立大学）；Department of ECE and INMC, Seoul National University（电子工程系和智能纳米材料中心，首尔国立大学）
Monash University（莫纳什大学）；Indian Institute of Technology Ropar（罗帕尔印度理工学院）；Kroop AI；University of Trento（特伦托大学）
Labelling of human behavior analysis data is a complex and time consuming task. In this paper, a fully automatic technique for labelling an image based gaze behavior dataset for driver gaze zone estimation is proposed. Domain knowledge is added to the data recording paradigm and later labels are generated in an automatic manner using Speech To Text conversion (STT). In order to remove the noise in the STT process due to different illumination and ethnicity of subjects in our data, the speech frequency and energy are analysed. The resultant Driver Gaze in the Wild (DGW) dataset contains 586 rec…
Enhancing Cross-Dataset Performance of Distracted Driving Detection With Score Softmax Classifier And Dynamic Gaussian Smoothing Supervision
State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body（汽车车身先进设计与制造国家重点实验室）；College of Mechanical and Vehicle Engineering, Hunan University（湖南大学机械与运载工程学院）；Faculty of Engineering and IT, University of Technology Sydney（悉尼科技大学工程与信息技术学院）；College of Automotive and Mechanical Engineering, ChangSha University of Science Technology（长沙理工大学汽车与机械工程学院）
The American University in Cairo（开罗美国大学）；Technical University of Munich（慕尼黑工业大学）；Ain Shams University（艾因夏姆斯大学）
The World Health Organization (WHO) reported 1.25 million deaths yearly due to road traffic accidents worldwide and the number has been continuously increasing over the last few years. Nearly fifth of these accidents are caused by distracted drivers. Existing work of distracted driver detection is concerned with a small set of distractions (mostly, cell phone usage). Unreliable ad-hoc methods are often used.In this paper, we present the first publicly available dataset for driver distraction identification with more distraction postures than existing alternatives. In addition, we propose a rel…
According to the World Health Organization, distracted driving is one of the leading cause of motor accidents and deaths in the world. In our study, we tackle the problem of distracted driving by aiming to build a robust multi-class classifier to detect and identify different forms of driver inattention using the State Farm Distracted Driving Dataset. We utilize combinations of pretrained image classification models, classical data augmentation, OpenCV based image preprocessing and skin segmentation augmentation approaches. Our best performing model combines several augmentation techniques, in…
Distracted drivers are more likely to fail to anticipate hazards, which result in car accidents. Therefore, detecting anomalies in drivers' actions (i.e., any action deviating from normal driving) contains the utmost importance to reduce driver-related accidents. However, there are unbounded many anomalous actions that a driver can do while driving, which leads to an 'open set recognition' problem. Accordingly, instead of recognizing a set of anomalous actions that are commonly defined by previous dataset providers, in this work, we propose a contrastive learning approach to learn a metric to …
Great Learning/Northwestern University（Great Learning/西北大学）；Great Learning
With over 50 million car sales annually and over 1.3 million deaths every year due to motor accidents we have chosen this space. India accounts for 11 per cent of global death in road accidents. Drivers are held responsible for 78% of accidents. Road safety problems in developing countries is a major concern and human behavior is ascribed as one of the main causes and accelerators of road safety problems. Driver distraction has been identified as the main reason for accidents. Distractions can be caused due to reasons such as mobile usage, drinking, operating instruments, facial makeup, social…
Continuous estimation the driver's take-over readiness is critical for safe and timely transfer of control during the failure modes of autonomous vehicles. In this paper, we propose a data-driven approach for estimating the driver's take-over readiness based purely on observable cues from in-vehicle vision sensors. We present an extensive naturalistic drive dataset of drivers in a conditionally autonomous vehicle running on Californian freeways. We collect subjective ratings for the driver's take-over readiness from multiple human observers viewing the sensor feed. Analysis of the ratings in t…
Intel Deutschland GmbH（英特尔德国有限公司）；Technical University of Munich（慕尼黑工业大学）
Many road accidents occur due to distracted drivers. Today, driver monitoring is essential even for the latest autonomous vehicles to alert distracted drivers in order to take over control of the vehicle in case of emergency. In this paper, a spatio-temporal approach is applied to classify drivers' distraction level and movement decisions using convolutional neural networks (CNNs). We approach this problem as action recognition to benefit from temporal information in addition to spatial information. Our approach relies on features extracted from sparsely selected frames of an action using a pr…
In order to increase road safety, among the visual and manual distractions, modern intelligent vehicles need also to detect cognitive distracted driving (i.e., the drivers mind wandering). In this study, the influence of cognitive processes on the drivers gaze behavior is explored. A novel image-based representation of the driver's eye-gaze dispersion is proposed to estimate cognitive distraction. Data are collected on open highway roads, with a tailored protocol to create cognitive distraction. The visual difference of created shapes shows that a driver explores a wider area in neutral drivin…
The study and modeling of driver's gaze dynamics is important because, if and how the driver is monitoring the driving environment is vital for driver assistance in manual mode, for take-over requests in highly automated mode and for semantic perception of the surround in fully autonomous mode. We developed a machine vision based framework to classify driver's gaze into context rich zones of interest and model driver's gaze behavior by representing gaze dynamics over a time period using gaze accumulation, glance duration and glance frequencies. As a use case, we explore the driver's gaze dynam…
A 20% rise in car crashes in 2021 compared to 2020 has been observed as a result of increased distraction and drowsiness. Drowsy and distracted driving are the cause of 45% of all car crashes. As a means to decrease drowsy and distracted driving, detection methods using computer vision can be designed to be low-cost, accurate, and minimally invasive. This work investigated the use of the vision transformer to outperform state-of-the-art accuracy from 3D-CNNs. Two separate transformers were trained for drowsiness and distractedness. The drowsy video transformer model was trained on the National…
University of California, San Diego（加利福尼亚大学圣地亚哥分校）
Driver gaze has been shown to be an excellent surrogate for driver attention in intelligent vehicles. With the recent surge of highly autonomous vehicles, driver gaze can be useful for determining the handoff time to a human driver. While there has been significant improvement in personalized driver gaze zone estimation systems, a generalized system which is invariant to different subjects, perspectives and scales is still lacking. We take a step towards this generalized system using Convolutional Neural Networks (CNNs). We finetune 4 popular CNN architectures for this task, and provide extens…
Weather-Dependent Variations in Driver Gaze Behavior: A Case Study in Rainy Conditions
Connected & Automated Vehicles National Research Council Canada（连接与自动化车辆国家研究委员会加拿大）；National Research Council Canada（加拿大国家研究委员会）；Transport Canada（加拿大运输部）
Massachusetts Institute of Technology (MIT)（麻省理工学院（MIT））
Automated estimation of the allocation of a driver's visual attention may be a critical component of future Advanced Driver Assistance Systems. In theory, vision-based tracking of the eye can provide a good estimate of gaze location. In practice, eye tracking from video is challenging because of sunglasses, eyeglass reflections, lighting conditions, occlusions, motion blur, and other factors. Estimation of head pose, on the other hand, is robust to many of these effects, but cannot provide as fine-grained of a resolution in localizing the gaze. However, for the purpose of keeping the driver sa…
Northwestern University（西北大学）；Toyota Research Institute（丰田研究院）；Woven Planet
We propose a computational model to estimate a person's attended awareness of their environment. We define attended awareness to be those parts of a potentially dynamic scene which a person has attended to in recent history and which they are still likely to be physically aware of. Our model takes as input scene information in the form of a video and noisy gaze estimates, and outputs visual saliency, a refined gaze estimate, and an estimate of the person's attended awareness. In order to test our model, we capture a new dataset with a high-precision gaze tracker including 24.5 hours of gaze se…
Vicomtech Foundation, Basque Research and Technology Alliance (BRTA)（维科姆技术基金会，巴斯克研究与技术联盟）；ETS Ingenieros de Telecomunicación, Universidad Politécnica de Madrid（马德里理工大学电信工程学院）；Dependability Research Lab, Intel Labs Europe, Intel Deutschland GmbH（英特尔德国有限公司欧洲英特尔实验室可靠性研究实验室）
Vision is the richest and most cost-effective technology for Driver Monitoring Systems (DMS), especially after the recent success of Deep Learning (DL) methods. The lack of sufficiently large and comprehensive datasets is currently a bottleneck for the progress of DMS development, crucial for the transition of automated driving from SAE Level-2 to SAE Level-3. In this paper, we introduce the Driver Monitoring Dataset (DMD), an extensive dataset which includes real and simulated driving scenarios: distraction, gaze allocation, drowsiness, hands-wheel interaction and context data, in 41 hours of…
EraW-Net: Enhance-Refine-Align W-Net for Scene-Associated Driver Attention Estimation
School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）
Hong Kong University of Science and Technology（香港科技大学）
Imitation learning by behavioral cloning is a prevalent method that has achieved some success in vision-based autonomous driving. The basic idea behind behavioral cloning is to have the neural network learn from observing a human expert's behavior. Typically, a convolutional neural network learns to predict the steering commands from raw driver-view images by mimicking the behaviors of human drivers. However, there are other cues, such as gaze behavior, available from human drivers that have yet to be exploited. Previous researches have shown that novice human learners can benefit from observi…
Massachusetts Institute of Technology (MIT)（麻省理工学院）；Chalmers University of Technology（查尔姆斯理工大学）；SAFER
Accurate, robust, inexpensive gaze tracking in the car can help keep a driver safe by facilitating the more effective study of how to improve (1) vehicle interfaces and (2) the design of future Advanced Driver Assistance Systems. In this paper, we estimate head pose and eye pose from monocular video using methods developed extensively in prior work and ask two new interesting questions. First, how much better can we classify driver gaze using head and eye pose versus just using head pose? Second, are there individual-specific gaze strategies that strongly correlate with how much gaze classific…
LISA: Language-guided Interference-aware Spatial-Frequency Attention for Driver Gaze Estimation
School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications（北京邮电大学计算机科学学院（国家级试点软件工程学院））；Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education（教育部可信分布式计算与服务重点实验室（BUPT））；School of Electrical Engineering, Guangxi University（广西大学电气工程学院）；Zhejiang University（浙江大学）
University of Modena and Reggio Emilia（摩德纳和雷焦艾米利亚大学）
Despite the advent of autonomous cars, it's likely - at least in the near future - that human attention will still maintain a central role as a guarantee in terms of legal responsibility during the driving task. In this paper we study the dynamics of the driver's gaze and use it as a proxy to understand related attentional mechanisms. First, we build our analysis upon two questions: where and what the driver is looking at? Second, we model the driver's gaze by training a coarse-to-fine convolutional network on short sequences extracted from the DR(eye)VE dataset. Experimental comparison agains…
