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This SpringerBrief presents the fundamentals of driver drowsiness detection systems, provides examples of existing products, and offers guides for practitioners interested in developing their own solutions to the problem. Driver drowsiness causes approximately 7% of all road accidents and up to 18% of fatal collisions. Proactive systems that are capable of preventing the loss of lives combine techniques, methods, and algorithms from many fields of engineering and computer science such as sensor design, image processing, computer vision, mobile application development, and machine learning which is covered in this brief. The major concepts addressed in this brief are: the need for such systems, the different methods by which drowsiness can be detected (and the associated terminology), existing commercial solutions, selected algorithms and research directions, and a collection of examples and case studies. These topics equip the reader to understand this critical field and its applications. Detection Systems and Solutions: Driver Drowsiness is an invaluable resource for researchers and professionals working in intelligent vehicle systems and technologies. Advanced-level students studying computer science and electrical engineering will also find the content helpful.



Chapter 1. Introduction

This chapter provides an introduction to the topic of driver drowsiness detection. Through crash statistics and other related data, it demonstrates the importance and seriousness of the subject. Moreover, it introduces some of the terminology and concepts related to this topic.

Aleksandar Čolić, Oge Marques, Borko Furht

Chapter 2. Driver Drowsiness Detection and Measurement Methods

This chapter provides an overview of driver drowsiness detection (DDD) and measurement methods and organizes them by category. The five main categories are: subjective, physiological, vehicle-based, behavioral, and hybrid. Most DDD systems being developed today rely on either vehicle-based measures—notably the steering wheel movement (SWM) and the standard deviation of lane position (SDLP)—or methods based on the detection of behavioral clues, e.g., closing of the eyes, yawning and nodding of the head.

Aleksandar Čolić, Oge Marques, Borko Furht

Chapter 3. Commercial Solutions

This chapter provides an overview of commercial solutions—developed by either automobile manufacturers or independent companies—whose goals are to increase driver’s safety and detect potentially dangerous driving situations that may have been caused by driver drowsiness.

Aleksandar Čolić, Oge Marques, Borko Furht

Chapter 4. Research Aspects

This chapter presents an overview of the research aspects associated with the development of driver drowsiness detection solutions, particularly vehicle-mounted solutions that monitor the driver’s head and face for behavioral signs of potential drowsiness, such as nodding, yawning, or blinking. It summarizes relevant technologies, popular algorithms, and design challenges associated with such systems.

Aleksandar Čolić, Oge Marques, Borko Furht

Chapter 5. Examples

This chapter provides examples of recent research work on driver drowsiness detection solutions. The systems and methods discussed in this chapter complement the commercial solutions surveyed in Chap.


. The chapter also includes highlights of the authors’ ongoing work on the topic.

Aleksandar Čolić, Oge Marques, Borko Furht
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