Prolonged driving on monotonous roads often leads to a reduction in task load that causes drivers passive fatigue. Passive fatigue results in loss of driver alertness and is detrimental to driver safety. This paper focuses on the effect of a haptic guidance steering system on improving behaviors of passively fatigued drivers. By continuously exerting active torque on a steering wheel, the haptic system guides drivers to follow the centerline of a lane; meanwhile, the drivers sense the torque and interact with it while operating the steering wheel. An experiment was conducted with 12 healthy participants in a high-fidelity driving simulator. A monotonous driving course was designed, and vehicle speed was fixed in order to induce drivers' passive fatigue. A treatment session was arranged with the haptic guidance steering system, and a control session was conducted as a comparison. Driving performance, assessed by standard deviation of lane position, mean absolute lateral error, and time-to-lane crossing, was significantly improved when haptic guidance was activated. Results of physiological measures, including heart rate variability and percentage of eye closure, revealed that passively fatigued drivers were aroused when they were aware of the active torque on the steering wheel. In conclusion, the activation of haptic guidance can be regarded as an effective countermeasure for the passively fatigued drivers who have performed a prolonged monotonous driving task.

Fast and accurate long-term trajectory prediction of surrounding vehicles (SVs) is critical to autonomous driving systems. In high-density traffic flows, strongly correlated vehicle behaviors require considering the interactions among multiple SVs when predicting their future trajectories. However, existing interactive prediction methods, most based on Long Short-Term Memory (LSTM), are suffering from slow prediction because they analyze SVs one by one and analyze trajectory sequence node by node. This paper presents a fast interactive trajectory prediction method called Structural Transformer which learns both spatial and temporal dependencies among multiple SVs in parallel. Specifically, our model first removes the internal states and loops of LSTM and replaces with a weighted self-reference mapping to realize parallel computation. Then, it embeds the relative spatial information of multiple SVs into trajectory states and reorganizes the self-reference mapping with neighbor-only interaction masks to achieve interactive prediction. Results on the NGSIM dataset show satisfyingly speed and accuracy performance on long-term trajectory prediction of multiple SVs. The longitudinal and lateral errors are reduced to 2.67m and 0.25m over 5s time horizon. The computational time of each step is only 12ms on a 2080ti GPU, which is over 4 times faster than the Structural LSTM.

Haptic guidance in a shared steering assistance system has drawn significant attention in intelligent vehicle fields, owing to its mutual communication ability for vehicle control. By exerting continuous torque on the steering wheel, both the driver and support system can share lateral control of the vehicle. However, current haptic guidance steering systems demonstrate some deficiencies in assisting lane changing. This study explored a new steering interaction method, including the design and evaluation of an intention-based haptic shared steering system. Such an intention-based method can support both lane keeping and lane changing assistance, by detecting a driver's lane change intention. By using a deep learning-based method to model a driver's decision timing regarding lane crossing, an adaptive gain control method was proposed for realizing a steering control system. An intention consistency method was proposed to detect whether the driver and the system were acting towards the same target trajectories and to accurately capture the driver's intention. A driving simulator experiment was conducted to test the system performance. Participants were required to perform six trials with assistive methods and one trial without assistance. The results demonstrated that the supporting system decreased the lane departure risk in the lane keeping tasks and could support a fast and stable lane changing maneuver.

Humans need to accurately infer the intentions and feelings of others to engage in successful social interaction. However, the application of artificial intelligence technology in Education (AIEd) forms a human–machine collaborative environment which changed the interaction relationship of individuals, it may have an affect on them. This study aimed to explore whether AIEd affects adolescents’ emotional perception. Combined with the actual teaching situation and the result of the questionnaire, 1332 students recruited through random sampling from AI Curriculum Reform Demonstration Schools in Guangzhou participated in this study. Different emotional priming stimulative materials (sentences and situational pictures) were used in the experiments. The task was designed to investigate adolescents’ reaction time to emotional faces (positive, negative). After eliminating blank data and invalid data with response time greater than 150 ms, 977 and 962 valid data were included in the statistical analysis in experiment 1 and experiment 2 respectively. Results show that AIEd has a negative effect on adolescents’ emotional perception. Prior research has focused on theory to the exclusion of practical applications and the psychological impact of AIEd, thus this study makes an innovative contribution in exploring the impact of the application of artificial intelligence technology in education on adolescents’ physical and mental development by using empirical research methods.

This study aimed to investigate the influence of artificial intelligence in education (AIEd) on adolescents’ social adaptability, as well as to identify the relevant psychosocial factors that can predict adolescents’ social adaptability. A total of 1328 participants (mean_age = 13.89, SD = 2.22) completed the survey. A machine-learning algorithm was used to find out whether AIEd may influence adolescents’ social adaptability as well as the relevant psychosocial variables, such as teacher–student relations, peer relations, interparental relations, and loneliness that may be significantly related to social adaptability. Results showed that it has a positive influence of AIEd on adolescents’ social adaptability. In addition, the four most important factors in the prediction of social adaptability among AI group students are interpersonal relationships, peer relations, academic emotion, and loneliness. A high level of interpersonal relationships and peer relations can predict a high level of social adaptability among the AI group students, while a high level of academic emotion and loneliness can predict a low level of social adaptability. Overall, the findings highlight the need to focus interventions according to the relation between these psychosocial factors and social adaptability in order to increase the positive influence of AIEd and promote the development of social adaptability.