Preliminary Results on Reducing the Workload of Assistive Vehicle Users: A Collaborative Driving Approach

Nowadays, physically impaired people still struggle with daily tasks when using mobility aid devices, whether for crossing doors, parking or manoeuvring in their homes. In this context, assistive robotics can offer solutions to those problems, thus increasing the users’ quality of life. However, studies must be performed to determine the best architecture for human–robot interaction. In this work, we propose a collaborative navigation strategy for improving users’ skills for driving assistive vehicles. We present four navigation modes: manual, assisted manual, autonomous and assisted autonomous. In particular in the two assisted modes, the system is able to predict the user’s motion intentions, reducing his/her workload. The system was validated in a real world environment with a population of twenty volunteers. Objective and subjective metrics were used to asses the system’s performance and usability, with special consideration to human factors. Results show that the system aids users to perform navigation tasks in a clear and compliant manner using a robotic assistive vehicle, while decreasing their perceived workload by 15% for the assisted manual, 41% for the autonomous and 40% for the assisted autonomous, when compared to the manual mode. Additionally, it is shown that if autonomous navigation sets a lower bound for user workload, the system approximates this bound while improving performance.