Abstract
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.
Highlights
The massive development in automobile industry has enhanced vehicle technology to ensure safe and secure travel
This paper focuses on the most recent deep learning based systems, algorithms and techniques for the detection of Human Driver Inattentive and Aggressive Driving Behavior (HIADB) by classifying human Inattentive driving behavior (HIDB) into two major categories; Distraction and Fatigue/Drowsiness
Machine Learning (ML) models can be categorized into two major categories; models based on the depth and models based on learning styles; Models that are based on learning styles are of four kinds in which deep learning models are trained with data; supervised learning, unsupervised learning, semisupervised learning and reinforcement learning
Summary
The massive development in automobile industry has enhanced vehicle technology to ensure safe and secure travel. We categorize detection of Human driver distraction into two types of measures, human driver’s visual behavior and vehicle’s dynamics or vehicle related features (driving performance metrics). Most recently the authors in [71] presented a two-stage clustering approach for the detection of unsafe driving styles by utilizing driving data and information on mobile usage, harsh events occurrence, speeding and acceleration profile with increasing importance with respect to safety In this way, trips have been categorized into six distinct groups (Aggressive trips include Aggressive trips, Distracted trips and Risky trips). By grouping the drivers in relation to the trips, the authors have analyzed that drivers cannot maintain a stable driving profile through time, but exhibit a strong volatile behavior per-trip
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