Abstract
The development of the automobile industry and civilian infrastructures improved the lifestyles of everyone in the world. In parallel to the rise in quality of life of everyone, the number of road accidents also rose. The major reason behind road accidents is emotional factors of the drivers. The emotional imbalance will influence the drivers to abandon the traffic rules, neglect speed limits, cross the signals, cross the lane, etc. Recently automobile industries have extended their researches to the development of emotion sensing systems and embedding them inside the vehicles using affective computing technology to mitigate the road accidents. These emotion sensing systems will be decisive and act as human-like driver-assistive systems in alarming the drivers. This chapter focuses on bringing out the feasibility and existing challenges of affective computing in sensing the emotional factors of drivers for improved road safety.
Top1 Introduction
We are currently through an information revolution during which technology improves people's work and daily activities, allowing them to be more productive; however, it is critical to assess whether the progress of technology is actually beneficial to people. In some circumstances, technology is not always effectively embraced; for example, if something new or novel is misappropriated, resulting in disinformation and misunderstanding, it could have the opposite effect. (Morley & Parker, 2013).
Emotions are important in almost every aspect of our everyday lives, including decision-making, motivation, and interpersonal interactions (Eyben et al., 2010), and driving is no exception (Jeon, 2016; Jeon et al., 2011). Some of the most common emotional triggers include a loss of control, journey delays, the risk of accidents, and the greater intellectual load required. Drivers who rely substantially on riding as a based-on job activity may be more susceptible to these triggers. (e.g., cab drivers, package transport). Although little stress might assist people in achieving their objectives, such as being on time at their destination, too many or too few factors can have a significant impact on driving effectiveness and general well-being (Ding et al., 2014).
As a result, future vehicles that can detect and respond to the emotional states of riders and drivers would be successful in improving not only highway safety but also mental health. Recent technological advancements, such as portable tech, have made it possible to investigate emotions in the real world, resulting in a slew of publications studying the harmful effects of particular emotions while driving.
Key Terms in this Chapter
ADAS: Advanced driver assistance systems (ADAS) are a collection of electronic technologies that help drivers with driving and parking. ADAS improves automotive and road safety by using a safe human-machine interface.
Annotation Tool: An annotation is additional information that is associated with a specific location in a document or other piece of data. It could be a message with a comment or explanation attached. Annotations appear in the margins of book pages on occasion.
Human-Machine Interface: A user interface or dashboard that connects a machine, system, or device to a human. While the word HMI can theoretically refer to any screen that allows a user to interact with a device, it is most usually associated with industrial processes.
Emotion: A conscious mental reaction (such as rage or terror) is a strong feeling that is usually focused on a single object and is usually accompanied by physiological and behavioural changes in the body.
Inertial Measurement Units (IMUs): An inertial measurement unit (IMU) is an electronic device that uses a combination of accelerometers, gyroscopes, and magnetometers to detect and report the specific force, angular rate, and sometimes the orientation of a body.
User-Centered Design (UCD): During each phase of the design process, designers focus on the users and their demands.