Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges
Human drivers have different driving styles, experiences, and emotions due to unique driving characteristics, exhibiting their own driving behaviors and habits. Various research efforts have approached the problem of detecting abnormal human driver behavior with the aid of capturing and analyzing th...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9107077/ |
| _version_ | 1818618548059111424 |
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| author | Monagi H. Alkinani Wazir Zada Khan Quratulain Arshad |
| author_facet | Monagi H. Alkinani Wazir Zada Khan Quratulain Arshad |
| author_sort | Monagi H. Alkinani |
| collection | DOAJ |
| description | Human drivers have different driving styles, experiences, and emotions due to unique driving characteristics, exhibiting their own driving behaviors and habits. Various research efforts have approached the problem of detecting abnormal human driver behavior with the aid of capturing and analyzing the face of driver and vehicle dynamics via image and video processing but the traditional methods are not capable of capturing complex temporal features of driving behaviors. However, with the advent of deep learning algorithms, a significant amount of research has also been conducted to predict and analyze driver's behavior or action related information using neural network algorithms. In this paper, we contribute to first classify and discuss Human Driver Inattentive Driving Behavior (HIDB) into two major categories, Driver Distraction (DD), Driver Fatigue (DF), or Drowsiness (DFD). Then we discuss the causes and effects of another human risky driving behavior called Aggressive Driving behavior (ADB). Aggressive driving Behavior (ADB) is a broad group of dangerous and aggressive driving styles that lead to severe accidents. Human abnormal driving behaviors DD, DFD, and ADB are affected by various factors including driver experience/inexperience of driving, age, and gender or illness. The study of the effects of these factors that may lead to deterioration in the driving skills and performance of a human driver is out of the scope of this paper. After describing the background of deep learning and its algorithms, we present an in-depth investigation of most recent deep learning-based systems, algorithms, and techniques for the detection of Distraction, Fatigue/Drowsiness, and Aggressiveness of a human driver. We attempt to achieve a comprehensive understanding of HIADB detection by presenting a detailed comparative analysis of all the recent techniques. Moreover, we highlight the fundamental requirements. Finally, we present and discuss some significant and essential open research challenges as future directions. |
| first_indexed | 2024-12-16T17:23:20Z |
| format | Article |
| id | doaj.art-4eecb44e4ba84e27b380ba5676f51f07 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:23:20Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4eecb44e4ba84e27b380ba5676f51f072022-12-21T22:23:07ZengIEEEIEEE Access2169-35362020-01-01810500810503010.1109/ACCESS.2020.29998299107077Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open ChallengesMonagi H. Alkinani0https://orcid.org/0000-0002-7658-7085Wazir Zada Khan1https://orcid.org/0000-0003-0819-4236Quratulain Arshad2https://orcid.org/0000-0002-4878-3564Department of Computer Science and Artificial Intelligence, College of Computer and Engineering, University of Jeddah, Jeddah, Saudi ArabiaFarasan Networking Research Laboratory, Faculty of CS & IT, Jazan University, Jazan, Saudi ArabiaDepartment of CS, COMSATS University, Islamabad, PakistanHuman drivers have different driving styles, experiences, and emotions due to unique driving characteristics, exhibiting their own driving behaviors and habits. Various research efforts have approached the problem of detecting abnormal human driver behavior with the aid of capturing and analyzing the face of driver and vehicle dynamics via image and video processing but the traditional methods are not capable of capturing complex temporal features of driving behaviors. However, with the advent of deep learning algorithms, a significant amount of research has also been conducted to predict and analyze driver's behavior or action related information using neural network algorithms. In this paper, we contribute to first classify and discuss Human Driver Inattentive Driving Behavior (HIDB) into two major categories, Driver Distraction (DD), Driver Fatigue (DF), or Drowsiness (DFD). Then we discuss the causes and effects of another human risky driving behavior called Aggressive Driving behavior (ADB). Aggressive driving Behavior (ADB) is a broad group of dangerous and aggressive driving styles that lead to severe accidents. Human abnormal driving behaviors DD, DFD, and ADB are affected by various factors including driver experience/inexperience of driving, age, and gender or illness. The study of the effects of these factors that may lead to deterioration in the driving skills and performance of a human driver is out of the scope of this paper. After describing the background of deep learning and its algorithms, we present an in-depth investigation of most recent deep learning-based systems, algorithms, and techniques for the detection of Distraction, Fatigue/Drowsiness, and Aggressiveness of a human driver. We attempt to achieve a comprehensive understanding of HIADB detection by presenting a detailed comparative analysis of all the recent techniques. Moreover, we highlight the fundamental requirements. Finally, we present and discuss some significant and essential open research challenges as future directions.https://ieeexplore.ieee.org/document/9107077/Deep learninghuman inattentive driving behaviorconnected vehiclesroad accident avoidanceabnormal behavior detectiondistraction or aggressiveness detection |
| spellingShingle | Monagi H. Alkinani Wazir Zada Khan Quratulain Arshad Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges IEEE Access Deep learning human inattentive driving behavior connected vehicles road accident avoidance abnormal behavior detection distraction or aggressiveness detection |
| title | Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges |
| title_full | Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges |
| title_fullStr | Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges |
| title_full_unstemmed | Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges |
| title_short | Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges |
| title_sort | detecting human driver inattentive and aggressive driving behavior using deep learning recent advances requirements and open challenges |
| topic | Deep learning human inattentive driving behavior connected vehicles road accident avoidance abnormal behavior detection distraction or aggressiveness detection |
| url | https://ieeexplore.ieee.org/document/9107077/ |
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