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We present an analysis of 6 h oscillations in the thermosphere ranging from 150 km to 400 km. The analysis applies 134 days of data from an incoherent scatter radar located at Arecibo Observatory (18.3°N, 66.7°W) from 1984 to 2015. To our knowledge, the climatological and seasonal characteristics of the 6 h oscillations in the thermosphere were investigated for the first time over Arecibo. The climatological mean amplitude of the 6 h oscillation in the thermosphere is about 11 m/s, and it increases slowly with altitude above 225 km. The climatological mean amplitude of the 6 h oscillation is comparable with semidiurnal and terdiurnal tides at Arecibo above 250 km. The climatological mean phase exhibits limited vertical variation. The 6 h oscillation is the most prominent in autumn, with amplitudes reaching around 20 m/s compared to approximately 10 m/s in other seasons. The phase structure in all seasons exhibits weak vertical variations. The responses of the thermospheric 6 h oscillation to solar and geomagnetic activities are also analyzed in this study. Our results indicate that at low latitude, solar activities have a small impact on the variation in the thermospheric 6 h oscillation, while it appears that the amplitude of the 6 h oscillation increases with increasing geomagnetic activity. Above 250 km, the amplitude of the 6 h oscillation reaches ~20 m/s during strong geomagnetic activity, which is almost twice of that occurring during weak geomagnetic activity. 

Objective This study investigated drivers’ subjective feelings and decision making in mixed traffic by quantifying driver’s driving style and type of interaction. Background Human-driven vehicles (HVs) will share the road with automated vehicles (AVs) in mixed traffic. Previous studies focused on simulating the impacts of AVs on traffic flow, investigating car-following situations, and using simulation analysis lacking experimental tests of human drivers. Method Thirty-six drivers were classified into three driver groups (aggressive, moderate, and defensive drivers) and experienced HV-AV interaction and HV-HV interaction in a supervised web-based experiment. Drivers’ subjective feelings and decision making were collected via questionnaires. Results Results revealed that aggressive and moderate drivers felt significantly more anxious, less comfortable, and were more likely to behave aggressively in HV-AV interaction than in HV-HV interaction. Aggressive drivers were also more likely to take advantage of AVs on the road. In contrast, no such differences were found for defensive drivers indicating they were not significantly influenced by the type of vehicles with which they were interacting. Conclusion Driving style and type of interaction significantly influenced drivers’ subjective feelings and decision making in mixed traffic. This study brought insights into how human drivers perceive and interact with AVs and HVs on the road and how human drivers take advantage of AVs. Application This study provided a foundation for developing guidelines for mixed transportation systems to improve driver safety and user experience. 

In highly and fully automated vehicles (AV), drivers could divert their attention to non-driving-related activities. Drivers may also take over AVs if they do not trust the way AVs drive in specific driving scenarios. Existing models have been developed to predict drivers’ takeover performance in responding to takeover requests initiated by AVs in semi-AVs. However, few models predicted driver-initiated takeover behavior in highly and fully AVs. The present study develops an attention-based multiple-input Convolutional Neural Network (CNN) to predict drivers’ takeover intention in fully AVs. The results indicated that the developed model successfully predicted takeover intentions of drivers with a precision of 0.982 and an F1-Score of.989, which were found to be substantially higher than other machine learning algorithms. The developed CNN model could be applied in improving the driving algorithms of the AV by considering drivers’ driving styles to reduce drivers’ unnecessary takeover behaviors. 

Autonomous Vehicle (AV) technology has the potential to significantly improve driver safety. Unfortunately, driver could be reluctant to ride with AVs due to the lack of trust and acceptance of AV’s driving styles. The present study investigated the impact of driver’s driving style (aggressive/defensive) and the designed driving styles of AVs (aggressive/defensive) on driver’s trust, acceptance, and take-over behavior in fully autonomous vehicles. Thirty-two participants were classified into two groups based on their driving styles using the Aggressive Driving Scale and experienced twelve scenarios in either an aggressive AV or a defensive AV. Results revealed that drivers’ trust, acceptance, and takeover frequency were significantly influenced by the interaction effects between AV’s driving style and driver’s driving style. The findings implied that driver’s individual differences should be considered in the design of AV’s driving styles to enhance driver’s trust and acceptance of AVs and reduce undesired take over behaviors.