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IntroductionDrivers' emotions have been widely investigated in transportation due to their significant effects on driving behaviors and traffic accidents. Appraisal theory posits that emotional reactions are influenced by individuals' attitudes toward current circumstances and events, thereby shaping their driving attitudes and styles. However, In the study of emotional driving, research often focuses on the impact of single factors such as age, gender, and personality, while the interplay between these multiple factors is a challenge. This study aims to explore the impact of age, personality, and driving experience on driving behaviors, and to investigate the interaction effect between these factors, particularly the role of emotional arousal.MethodUsing moderated moderation and mediated moderation analyses, we examined how these individual factors interact and influence driving behaviors, including acceleration, speed stability, and steering performance. Data were collected from a driving simulation experiment involving 40 Chinese participants in various emotional states.ResultsOur findings revealed that higher-age drivers and experienced drivers displayed lower maximum acceleration and better speed stability. Extraversion significantly mediated the relationship between age and driving behaviors, with this relationship being moderated by arousal states. Additionally, Neuroticism moderated the relationship between driving experience and driving behaviors.ConclusionThis study highlights how individual factors influence the trajectory of personality development in relation to driving behaviors. These findings have practical implications for improving traffic safety and driver education programs by incorporating emotional and personality-based interventions. Further long-term and individualized studies are needed to better understand these interactions and develop targeted interventions.

This paper presents a state-of-the-art estimation technique by cross-combining a number n of filters for high-precision, reliable and robust vehicle sideslip angle state estimation, over a full range of vehicle operations irrespective of the driving mission and disruptions that may occur in the system. A machine-learning algorithm based on decision trees connects several filters together to switch between them according to the driving context, ensuring the best possible state estimate for relatively small and large sideslip angle values. In conjunction with the above-mentioned aspects, a seamless transition between different vehicle models is attained by observing the key parameters characterizing the lateral motion of the vehicle. The tests conducted using a prototype vehicle on a snow-covered track confirm the effectiveness and reliability of the proposed approach.