Research indicates that monotonous automated driving increases the incidence of fatigued driving. Although many prediction models based on advanced machine learning techniques were proposed to monitor driver fatigue, especially in manual driving, little is known about how these black-box machine learning models work. In this paper, we proposed a combination of eXtreme Gradient Boosting (XGBoost) and SHAP (SHapley Additive exPlanations) to predict driver fatigue with explanations due to their efficiency and accuracy. First, in order to obtain the ground truth of driver fatigue, PERCLOS (percentage of eyelid closure over the pupil over time) between 0 and 100 was used as the response variable. Second, we built a driver fatigue regression model using both physiological and behavioral measures with XGBoost and it outperformed other selected machine learning models with 3.847 root-mean-squared error (RMSE), 1.768 mean absolute error (MAE) and 0.996 adjusted $R^2$. Third, we employed SHAP to identify the most important predictor variables and uncovered the black-box XGBoost model by showing the main effects of most important predictor variables globally and explaining individual predictions locally. Such an explainable driver fatigue prediction model offered insights into how to intervene in automated driving when necessary, such as during the takeover transition period from automated driving to manual driving.
School of Automation, Southeast University（东南大学自动化学院）
Researches show that fatigue driving is one of the important causes of road traffic accidents, so it is of great significance to study the driver fatigue recognition algorithm to improve road traffic safety. In recent years, with the development of deep learning, the field of pattern recognition has made great development. This paper designs a real-time fatigue state recognition algorithm based on spatio-temporal feature sequence, which can be mainly applied to the scene of fatigue driving recognition. The algorithm is divided into three task networks: face detection network, facial landmark d…
Electroencephalogram (EEG) has become increasingly popular in driver fatigue monitoring systems. Several decomposition methods have been attempted to analyze the EEG signals that are complex, nonlinear and non-stationary and improve the EEG decoding performance in different applications. However, it remains challenging to extract more distinguishable features from different decomposed components for driver fatigue recognition. In this work, we propose a novel decomposition-based hybrid ensemble convolutional neural network (CNN) framework to enhance the capability of decoding EEG signals. Four…
In this paper, we explore different deep learning based approaches to detect driver fatigue. Drowsy driving results in approximately 72,000 crashes and 44,000 injuries every year in the US and detecting drowsiness and alerting the driver can save many lives. There have been many approaches to detect fatigue, of which eye closedness detection is one. We propose a framework to detect eye closedness in a captured camera frame as a gateway for detecting drowsiness. We explore two different datasets to detect eye closedness. We develop an eye model by using new Eye-blink dataset and a face model by…
Exact Fit Attention in Node-Holistic Graph Convolutional Network for Improved EEG-Based Driver Fatigue Detection
Minnan Normal University（闽南师范大学）；Nanjing University of Chinese Medicine（南京中医药大学）；Nanyang Technological University（南洋理工大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Zhengzhou University of Light Industry（郑州轻工业大学）；Tsinghua University（清华大学）；Eastern Institute of Technology（宁波工程学院）；Ningbo Institute of Digital Twin（宁波数字孪生研究院）；Xiamen University（厦门大学）
Massachusetts Institute of Technology（麻省理工学院）；Toyota Collaborative Safety Research Center（丰田合作安全研究中心）；Touchstone Evaluations
We consider a large dataset of real-world, on-road driving from a 100-car naturalistic study to explore the predictive power of driver glances and, specifically, to answer the following question: what can be predicted about the state of the driver and the state of the driving environment from a 6-second sequence of macro-glances? The context-based nature of such glances allows for application of supervised learning to the problem of vision-based gaze estimation, making it robust, accurate, and reliable in messy, real-world conditions. So, it's valuable to ask whether such macro-glances can be …
Michigan State University（密歇根州立大学）；CSAA Insurance Group（CSAA保险集团）；New York University（纽约大学）
In recent years it has become possible to collect GPS data from drivers and to incorporate this data into automobile insurance pricing for the driver. This data is continuously collected and processed nightly into metadata consisting of mileage and time summaries of each discrete trip taken, and a set of behavioral scores describing attributes of the trip (e.g, driver fatigue or driver distraction) so we examine whether it can be used to identify periods of increased risk by successfully classifying trips that occur immediately before a trip in which there was an incident leading to a claim fo…
Utilizing Large Language Models for Machine Learning Explainability
Centre for Research \& Technology, Hellas School of Architecture Technology；Engineering, University of Brighton, BN2 4GJ, United Kingdom University of Nicosia, 2417 Cyprus
Hybrid Approach for Driver Behavior Analysis with Machine Learning, Feature Optimization, and Explainable AI
Department of CSE, DUET Gazipur, Bangladesh；Department of IST, George Mason University Fairfax, USA；AI \& Big Data Department, Woosong University Daejeon, South Korea
The University of Michigan（密歇根大学）；University of Pittsburgh（匹兹堡大学）；University of Michigan（密歇根大学）
It is extremely important to ensure a safe takeover transition in conditionally automated driving. One of the critical factors that quantifies the safe takeover transition is takeover time. Previous studies identified the effects of many factors on takeover time, such as takeover lead time, non-driving tasks, modalities of the takeover requests (TORs), and scenario urgency. However, there is a lack of research to predict takeover time by considering these factors all at the same time. Toward this end, we used eXtreme Gradient Boosting (XGBoost) to predict the takeover time using a dataset from…
Institute for Advanced Studies in Basic Sciences (IASBS)（基础科学高级研究学院）；Charles University（查理大学）；Institute for Research in Fundamental Sciences (IPM)（基础科学研究所）
Nowadays, automobile manufacturers make efforts to develop ways to make cars fully safe. Monitoring driver's actions by computer vision techniques to detect driving mistakes in real-time and then planning for autonomous driving to avoid vehicle collisions is one of the most important issues that has been investigated in the machine vision and Intelligent Transportation Systems (ITS). The main goal of this study is to prevent accidents caused by fatigue, drowsiness, and driver distraction. To avoid these incidents, this paper proposes an integrated safety system that continuously monitors the d…
National Observatory of Athens（雅典国家天文台）；National Technical University of Athens（雅典国立技术大学）
Helicoverpa Armigera, or cotton bollworm, is a serious insect pest of cotton crops that threatens the yield and the quality of lint. The timely knowledge of the presence of the insects in the field is crucial for effective farm interventions. Meteo-climatic and vegetation conditions have been identified as key drivers of crop pest abundance. In this work, we applied an interpretable classifier, i.e., Explainable Boosting Machine, which uses earth observation vegetation indices, numerical weather predictions and insect trap catches to predict the onset of bollworm harmfulness in cotton fields i…
University of Oxford（牛津大学）；Umeå University（于默奥大学）；University of Nottingham（诺丁汉大学）；Oxford Robotics Institute（牛津机器人研究所）
In commentary driving, drivers verbalise their observations, assessments and intentions. By speaking out their thoughts, both learning and expert drivers are able to create a better understanding and awareness of their surroundings. In the intelligent vehicle context, automated driving commentary can provide intelligible explanations about driving actions, thereby assisting a driver or an end-user during driving operations in challenging and safety-critical scenarios. In this paper, we conducted a field study in which we deployed a research vehicle in an urban environment to obtain data. While…
University of Bamberg（班贝格大学）；IBM Deutschland GmbH（IBM德国有限公司）；fortiss GmbH（福蒂斯有限公司）
Artificial Intelligence and Digital Twins play an integral role in driving innovation in the domain of intelligent driving. Long short-term memory (LSTM) is a leading driver in the field of lane change prediction for manoeuvre anticipation. However, the decision-making process of such models is complex and non-transparent, hence reducing the trustworthiness of the smart solution. This work presents an innovative approach and a technical implementation for explaining lane change predictions of layer normalized LSTMs using Layer-wise Relevance Propagation (LRP). The core implementation includes …
Machine Learning-Based Manufacturing Cost Prediction from 2D Engineering Drawings via Geometric Features
Institute for Data Science \& Artificial Intelligence, Bo g azi c i University, \. I stanbul, T\" u rkiye；ArtificaX Technologies, Bo g azi c i Teknopark, \. I stanbul, T\" u rkiye；Teknorot Automative Products Inc., D\" u zce, T\" u rkiye；\. I stanbul Ticaret University, Faculty of Engineering, Industrial Engineering, \. I stanbul, T\" u rkiye
Purpose: To explore the potential of Explainable Machine Learning in the prediction and detection of drivers of cleared homicides at the national- and state-levels in the United States. Methods: First, nine algorithmic approaches are compared to assess the best performance in predicting cleared homicides country-wise, using data from the Murder Accountability Project. The most accurate algorithm among all (XGBoost) is then used for predicting clearance outcomes state-wise. Second, SHAP, a framework for Explainable Artificial Intelligence, is employed to capture the most important features in e…
