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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. Master of Science People are deeply influenced by emotions. Anger while driving is shown to negatively impact people's perception and understanding of what is going on in the driving context and prediction about what will happen. As a result, this influences driving performance and road safety. Intelligent agents (such as Siri or Alexa) built into vehicles can help regulate the emotions of the drivers and can positively impact driving performance. Language is another important aspect that influences human behavior during social interactions. The current thesis aims to leverage the positive impacts of in-vehicle agents and language to design in-vehicle agent interactions capable of mitigating the negative effects of anger to ensure better driving performance and increased situation awareness. The three conditions explored are the native language agent condition (Hindi or Chinese), secondary language agent condition (English), and no agent condition. The effects on angry drivers' situation awareness, driving performance, and subjective perception are studied. Results indicate that the driving performance is better in the case of the native language agent condition when compared to the no agent condition. Participants preferred native language agents over the other conditions. People's understanding and prediction capability in the driving context was better in the native agent condition over the other conditions. The study results have practical design implications in designing in-vehicle agent interfaces and the results are expected to help foster future work.

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 takeover displays led to faster takeover reaction times. QN-MHP model predictions of takeover reaction times resulted in a 68.92% correlation with the empirical data collected. The results of this study suggest that angry drivers perform riskier than neutral drivers when taking over control of a semi-automated vehicle. 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 takeover display design. Master of Science Over the last decade, there has been an increasing shift towards the automation of cars. But, this is only made possible in situations where the driver is ready to take over control of the vehicle when required (e.g., Tesla autopilot). Therefore, it is important to use the right alert sounds to warn drivers to take control back from their self-driving cars. There has been a lot of research in designing the safest alerts for taking over control of the vehicle. However, research has not considered the driver's emotions while taking over control of their vehicle. Anger has been shown to be one of the emotions that can impair driver judgment and performance. Limited research has been performed to measure how anger can influence takeover performance. This study compared how angry drivers are different from non-angry (neutral) drivers in their takeover reaction time and safety. Additionally, the effects of a more urgent sounding alert on reaction time were also measured. The data from this research help to validate the predictions of a mathematical model of driver behavior. Thirty-six participants performed takeovers in a self-driving car simulator. While they were driving in the simulator, they also played a game on a tablet. The results showed that angry drivers and neutral drivers took the same time to takeover. But, angry drivers drove faster which can lead to dangerous collisions. Angry drivers took longer to change lanes with lower steering wheel angles. Neutral drivers started changing lanes earlier because they drove slower and turned more. This meant they drove safer than angry drivers. A more urgent sounding alert led to faster takeover reaction times from both drivers. The mathematical model predictions of takeover reaction time were nearly 70% close to the actual data collected. The results of this study suggest that angry drivers perform worse takeovers than neutral drivers. The findings will help design safer alerts in self-driving cars and also contribute to the design of self-driving cars that consider the drivers' emotional states.

COSTA, Thicianne Malheiros da. Correlatos disposicionais e situacionais da propensão à agressividade no trânsito. 2016. 98f. – Dissertação (Mestrado) – Universidade Federal do Ceará, Programa de Pós-graduação em Psicologia, Fortaleza (CE), 2016. A presente dissertação teve como objetivo avaliar o poder preditivo dos contextos de trânsito (capital e interior), da idade, do sexo, do tempo de habilitação, dos traços de personalidade e da ansiedade traço sobre a propensão à agressividade no trânsito. Para tanto, desenvolveram-se dois estudos: o Estudo 1 teve como objetivo adaptar e validar a escala de propensão à agressividade no trânsito (EPAT) para o contexto brasileiro; e no Estudo 2, buscou-se testar a influência dos contextos de trânsito (capital e interior), da idade, do sexo, do tempo de habilitação, dos traços de personalidade e da ansiedade traço sobre a propensão à agressividade no trânsito. Especificamente, o primeiro estudo, foi realizado em três etapas. Na primeira, efetivaram-se a tradução e a validação semântica dos itens da escala; contou-se com o auxílio de dois colaboradores brasileiros bilíngues, utilizando o método do backtranslation, para obter uma única versão apropriada do instrumento em português; posteriormente, dez pessoas, escolhidas aleatoriamente da população alvo do estudo, avaliaram os itens e as instruções da medida, indicando que estavam inteligíveis. Na segunda, escalonaram-se os diferentes níveis de reação emocional e retaliação das opções de respostas apresentadas em cada um dos 19 cenários da EPAT; nessa etapa, contou-se com uma amostra não probabilística de 41 estudantes universitários da cidade de Fortaleza (CE), com idade média de 21 anos (dp = 4,61). Os resultados indicaram que a escala abarca os distintos graus de reação de agressividade. A terceira parte contou com 271 motoristas de Fortaleza, idade média de 35,1 (dp = 13,5). Os participantes responderam aos instrumentos: EPAT, Inventário de Busca de Sensações, Escala de Autocontrole, Questionário de Agressão de Buss-Perry, Inventário dos Cinco Grandes Fatores de Personalidade e perguntas biossociodemográficas. Os resultados apontaram que a EPAT apresenta uma estrutura unifatorial, e adequada validade convergente. O Estudo 2, foco desta dissertação, contou com uma amostra não probabilística de 256 motoristas das cidades de Fortaleza e Sobral (CE), com idades variando entre 18 e 80 anos (m = 31,87 dp = 12,99) e a maioria com Carteira Nacional de Habilitação na categoria B (39,8%). Os participantes responderam a quatro instrumentos: Inventário dos Cinco Grandes Fatores de Personalidade; EPAT; Inventário de Ansiedade Traço-Estado; e Perfil do condutor. Os resultados indicaram que não houve diferenças significativas entre as pontuações dos motoristas da capital e do interior, bem como entre gênero; nas correlações, foram significativas apenas idade, neuroticismo e amabilidade com a propensão à agressividade no trânsito; por fim, a partir da análise de regressão linear múltipla observou-se que apenas a amabilidade se apresentou como variável preditora de tal construto. Não obstante algumas limitações, ressalta-se que os objetivos foram alcançados, sendo propostos estudos futuros que contribuam para a prevenção de comportamentos agressivos e acidentes de trânsito.