Assessing the Effect of Drivers’ Gender on Their Intention to Use Fully Automated Vehicles

Analysing the effect of gender on the human-machine interaction in level 3 automated vehicles

The emergence of the level 3 automated vehicles (L3 AVs) can enable drivers to be completely disengaged from driving and safely perform other non-driving related tasks, but sometimes their takeover of control of the vehicle is required. The takeover of control is an important human–machine interaction in L3 AVs. However, little research has focused on investigating the effect of gender on takeover performance. In order to fill this research gap, a driving simulator study with 76 drivers (33 females and 43 males) was conducted. The participants took over control from L3 AVs, and the timing and quality of takeover were measured. The results show that although there was no significant difference in most of the measurements adopted to quantify takeover performance between female and male. Gender did affect takeover performance slightly, with women exhibited slightly better performance than men. Compared to men, women exhibited a smaller percentage of hasty takeovers and slightly faster reaction times as well as slightly more stable operation of the steering wheel. The findings highlight that it is important for both genders to recognise they can use and interact with L3 AVs well, and more hands-on experience and teaching sessions could be provided to deepen their understanding of L3 AVs. The design of the car interiors of L3 AVs should also take into account gender differences in the preferences of users for different non-driving related tasks.

Insights into the relationship between usability and willingness to use a robot in the future workplaces: Studying the mediating role of trust and the moderating roles of age and STARA

Background and aim

Human–robot collaboration is the key component of the fourth industrial revolution concept. Workers’ willingness to collaborate with industrial robots is a basic requirement for an efficient and effective interaction. The roles of human-robot trust and technology affinity as mediators in the relationship between robot usability and worker willingness were analyzed in this study. As other critical variables, the mediator roles of Age and STARA were also calculated.

Materials and methods

This study included 400 workers from a car company who interacted with industrial robots in their daily work activities. After examining the questionnaires’ validity and reliability, the main variables were determined to be willingness to use robots and robot usability. AMOS software also considered human-robot trust and worker technology affinity as mediators. The bootstrapping method was used to evaluate indirect relationships. A set of goodness-of-fit indices were presented to determine the adequacy of the goodness of fit between the proposed model and the data.

Results

Based on model fit indices, an overall satisfactory model fit was obtained for the direct/indirect relationship between robot usability and worker willingness to use it (with mediating role of human-robot trust). Workers’ age and fear of Smart Technology, Artificial Intelligence, Robotics, and Algorithms (STARA) were identified as moderators in the relationship between usability and willingness.

Conclusion

Attention to the robot usability and the role of workers’ trust in robots appears to be required to ensure workers’ willingness to use robots and the success of human-robot collaboration in future workplaces. As the workers age and their fear of robots grows, usability can play a larger role in increasing their willingness to put robots to work.

Safety Verification of Driving Resource Occupancy Rules Based on Functional Language

Autonomous driving is a safety-critical system, and the occupancy of its environmental resources affects the safety of autonomous driving. In view of the lack of safety verification of environmental resource occupation rules in autonomous driving, this paper proposes a verification method of automatic driving model based on functional language through CSPM. Firstly, the modeling and verification framework of an autopilot model based on CSPM is given. Secondly, the process algebra definition of CSPM is given. Thirdly, the typical single loop environment model in automatic driving is abstracted, and the mapping method from automatic driving model to CSP is described in detail for the automatic driving environment and the typical collision, overtaking, lane change and other scenes involved. Finally, the autopilot model of the single loop is mapped to CSPM, and the application effect of this method is discussed by using FDR tool. Experiments show that this method can verify the safety of autonomous driving resources, thereby improving the reliability of the autonomous driving model.