Towards affect-integrated driving behaviour research

Abstract

This paper aims to systematically explore the relationship between affect, cognition, and driving behaviours. Whereas some driving models partially address drivers' affective factors, the majority of them have focused on cognitive aspects. Thus, it would be helpful to construct an affect-integrated driving behaviour research model. First, I briefly assessed the current driving models and identified critical cognitive elements commonly found among those models. Next, the mechanisms of affective effects on those cognitive processes were described. Then, I showed the effects of affective states on driving performance. Finally, a conceptual model for affect-related driving behaviour research was proposed and followed by discussions from the theoretical and practical perspectives. This review helps researchers construct a more naturalistic and generic driving behaviour model, conceptualise affect detection research with a clearer idea of what affect needs to be detected and why, and develop an affect regulation model and adaptive system for multiple drivers. Relevance to human factors/ergonomics theoryThe present paper reviews affect-related driving research and proposes an affectintegrated driving model based on theoretical and practical evidence. This paper will contribute to conduct more comprehensive driving behaviour research and design an adaptive in-vehicle affect detection and mitigation system.

Improving Driver Emotions with Affective Strategies

“…Although this idea has been proposed before, e.g., by Nass et al [14], most driver models only take into account cognitive load, distraction, and the physical state of the driver but do not consider emotions and their triggers [15]. For adaptive, affect-integrated interaction to become reality, the software behind automotive UIs needs to have a more holistic view of the driver which includes their long-term traits and preferences, as well as short-term states, such as emotions [1,16].…”

“…Affective states have been shown to bias user actions and cognitive processes [17], which is why they can have effects on road safety [1]. In order to understand these emotional states, they are often classified within categories of basic emotions [17] or on continuous scales with the dimensions arousal and valence [18].…”

“…Williams et al explored another facet of emotional interaction in the car with a social robot, which also had positive effects on driving performance [33]. We take this aspect of adaptive and social interaction and connect it to an affect-integrated driver model in order to intervene within the driver-vehicle interaction loop [1,16,34]. Figure 1 shows the driver state taxonomy used in many related studies, which defines dangerous states with extreme values of arousal in combination with negative valence (e.g., anger and sadness) and identifies medium arousal and positive valence as optimal driving state.…”

“…Affective automotive user interfaces show potential for an improvement of driver safety through the detection of performance-influencing states, such as anger, and their mitigation through strategic interaction [1]. Thanks to recent advancements in psychophysiological computing and new technologies that allow for contactless sensing of human emotions, affective interfaces might soon become feasible in everyday environments [2].…”

Recognizing Frustration of Drivers From Face Video Recordings and Brain Activation Measurements With Functional Near-Infrared Spectroscopy

“…Frustration can be seen as an aversive emotional state resulting when goal-directed behavior is blocked (Lazarus, 1991 ) that is associated with negative valence and slightly elevated arousal (Russell, 1980 ; Scherer, 2005 ). As driving is a goal-directed behavior (e.g., reaching the destination in time in the above example), blocking events, as described above, can induce frustration and eventually lead to aggressive (driving) behavior (Ekman and Friesen, 2003 ; Lee, 2010 ; Jeon, 2015 ). In addition, frustration can have negative effects on cognitive processes important for driving such as attention, judgment and decision making (Jeon, 2015 ).…”