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As semi-automated vehicles become more available to the general public, it is important to investigate human factors, including both the driver side and the interface side. Despite much research on semi-automated vehicles, little research has conducted considering both driver states and takeover request display design. The present study investigated the effects of drivers’ affective states and auditory display urgency on takeover response time and performance quality. Thirty-six participants experienced takeover scenarios in a semi-automated vehicle using a driving simulator, while playing an online game. For takeover quality, angry drivers drove faster, took longer to change lanes and had lower steering wheel angles than neutral drivers, which made riskier driving. However, there was no difference in eye glance behaviors. Higher frequency and more repetitions of the auditory displays led to faster takeover reaction times, but there was no time difference between angry and neutral drivers. Drivers’ response time to takeover displays from both affect groups was modelled using the QN-MHP framework, which resulted in a R2 of 0.505 with the empirical data collected. In sum, results suggest that drivers’ anger state influenced takeover quality, while display urgency influenced takeover response time. This study is expected to make a significant contribution to research on the influence of emotion, specifically, anger on takeover performance in semi-automated vehicles as well as to the takeover display design.

Research shows that anger has a negative impact on cognition due to the rumination effect and in the context of driving, anger negatively impacts situation awareness, driving performance, and road safety. In-vehicle agents are capable of mitigating the effects of anger and subsequent effects on driving behavior. Language is another important aspect that influences information processing and human behavior during social interactions. This thesis aims to explore the effects of the language of in-vehicle agents on angry drivers' situation awareness, driving performance, and subjective perception. The three conditions explored are the native language agent condition (Hindi or Chinese), secondary language agent condition (English), and no agent condition. Results indicate that driving performance is better in the case of the native language agent condition when compared to the no agent condition. Higher levels of situational awareness were affected by the agent condition, favoring the native language condition over the secondary language condition. The participants preferred native language agents over the other conditions and the perceived workload was higher in the no-agent condition than the native agent condition. Drivers also expressed the need to control the state of the in-vehicle agent. The study results have practical design implications and the results are expected to help foster future work in this domain.

As autonomy in vehicles increases, the role of the driver will diminish, moving on to more non-driving related tasks. We are at a juncture at which cars have the ability to drive themselves, but only if the driver is ready to take over control of the vehicle when required (e.g., Tesla autopilot). Therefore, it is important that adequate alerts are used to warn drivers in various contexts to take control back from these semi-automated vehicles. Considerable research has been conducted to design the safest alerts for the takeover transition. However, more systematic research is still required to accurately predict driver responses to different parameters of the alerts. Also, takeover research has not considered drivers' states (e.g., emotions). Anger is one of the emotions that has been shown to impair driver judgment and performance. There is limited research on how anger might influence takeover performance in semi-automated driving. This study aimed to investigate the influence of anger on takeover reaction time and safety by comparing angry and neutral drivers. Additionally, the effects of increased perceived urgency of auditory alarms on takeover reaction time were measured. Data from this research was used to help test mathematical driver behavior modeling using the QN-MHP cognitive architecture. Using a motion-based simulator, 36 participants performed takeovers in semi-automated vehicle on a 3-lane highway. Between takeovers, participants performed a secondary task (i.e., online game) on a tablet. There were no significant differences in takeover reaction time between angry and neutral drivers. However, angry drivers drove faster which can lead to dangerous collisions. Angry drivers took longer to change lanes with lower steering wheel angles. Neutral drivers' slower speeds and higher steering wheel angles indicated that they initiated the lane change earlier, and thus, made safer lane changes. As expected, higher frequency and more repetitions of the auditory

In the present study possible associations between driver characteristics and aggressive driving were examined. 210 participants responded to a questionnaire consisting of self-report measures of emotion regulation ability, personality traits, and attitudes towards traffic behaviours in a Swedish translation of the Propensity for Angry Driving Scale (PADS). The main results showed that females, older age, agreeableness, openness, and social desirability were negatively correlated with angry driving behaviour as measured by the PADS. Impulsivity, attention seeking, trait irritability, verbal trait aggression, positive attitude towards speeding, and a high self-reported car manoeuvring ability were positively correlated with angry driving. Partial correlations showed that social desirability, trait irritability, and a positive attitude towards speeding explained most of the unique variance. Multiple regression analysis showed that trait irritability, positive attitude towards speeding, and a high self-reported car manoeuvring ability were the most important predictors of angry driving.

In the present study possible associations between driver characteristics and aggressive driving were examined. 210 participants responded to a questionnaire consisting of self-report measures of emotion regulation ability, personality traits, and attitudes towards traffic behaviours in a Swedish translation of the Propensity for Angry Driving Scale (PADS). The main results showed that females, older age, agreeableness, openness, and social desirability were negatively correlated with angry driving behaviour as measured by the PADS. Impulsivity, attention seeking, trait irritability, verbal trait aggression, positive attitude towards speeding, and a high self-reported car manoeuvring ability were positively correlated with angry driving. Partial correlations showed that social desirability, trait irritability, and a positive attitude towards speeding explained most of the unique variance. Multiple regression analysis showed that trait irritability, positive attitude towards speeding, and a high self-reported car manoeuvring ability were the most important predictors of angry driving.

Road rage is a topic that receives consistent attention in both the road safety literature and media. Before Australian research can address the underlying factors associated with road rage, there is a need for a valid instrument appropriate for use in this context. The present program of research consisted of two studies. Study 1 used a university sample to adjust the scoring technique and response options of a 19-item American measure of the propensity for angry driving with acceptable reliability and validity. In Study 2, Factor Analysis confirmed a one-factor solution and resulted in a 15-item scale, the Australian Propensity for Angry Driving Scale (Aus-PADS), with a coefficient alpha of .82 (N = 433). The Aus-PADS may be used in future research to broaden the Australian road rage literature and to improve our understanding of the underlying processes associated with road rage in order to prevent the problem. Future research should also confirm the factor structure and generate normative data with a more representative sample.