# Shared eHMI: Bridging Human-Machine Understanding in Autonomous Wheelchair Navigation
## 1 Introduction  
Wheelchairs are essential for people with mobility impairments, providing health and independence [1]. However, operating a wheelchair can be challenging for the elderly and those with upper limb injuries. Moreover, wheelchair users have a higher probability of upper limb injuries due to the use of their arms [2]. Additionally, the joystick controllers of electric wheelchairs are difficult for users with hand impairments [3]. If autonomous driving functionality is applied to electric wheelchairs, users could move to their desired destinations without the need to manually operate the wheelchair [2]. This would reduce their travel burden and enhance their independence in mobility. The emergence of automated driving system (ADS) technology signifies a transformative epoch in intelligent autonomous mobility, especially in personal devices such as electric wheelchairs [2, 4, 5]. The adoption of autonomous driving technology enhances the convenience and independence of wheelchair users; however, these advancements introduce novel paradigms and conundrums in human-machine interface dynamics [6]. Contrary to conventional manual joystick mechanisms, autonomous wheelchair users often exhibit apprehension towards machine-dictated trajectories, which can appear as an opaque decision-making algorithm [7, 8]. This transparency refers to \"the degree of sharedintent and shared awareness between a human and a machine [9].\" Therefore, to achieve such transparency, the automated driving system needs to convey its capabilities, motion intentions, and anticipated actions to the user. Notably, the terrains navigated by wheelchairs tend to be more complex than conventional vehicular roads [7; 10]. The human visual system excels at interpreting these terrain nuances, often surpassing the discernment of standard machine sensors. Such intricacies, when amalgamated with instantaneous path determination, occasionally necessitate human interference, either through minor AI route adjustments or by re-assuming control. As shown in Figure 1, despite the commendable performance of ADS technology in many scenarios, there are still inherent challenges when confronting intricate road conditions and social interactions. This highlights the significant cognitive burden on users to comprehend the anticipated behaviors. It also reflects how transparent communication aids users in making decisions and intervening in the actions of the autonomous driving system. Moreover, it is essential for pedestrians to predict the autonomous wheelchair's anticipated actions, facilitating their own appropriate adjustments to prevent potential mishaps [11; 12]. Thus, further study and exploration are required on how to effectively visualize the driving status and intentions of autonomous wheelchairs. This is crucial for enhancing the understanding of wheelchair users and other road users about the behaviors of autonomous wheelchairs and thus promoting transparent communication. Currently, there is a gap in the design and research specifically aimed at the visualization of driving status and intentions for autonomous wheelchairs. Additionally, there is a need to evaluate the impact of this human-machine interaction method on the attitudes and acceptance levels of both wheelchair users and other road users. In this research, we introduce a novel shared external human-machine interaction (eHMI) approach for autonomous wheelchairs, based on on-ground projection. This approach, which combines real-world and virtual elements, communicates the motion intentions of the autonomous wheelchair to the users and pedestrians around them. It aims to enhance the trust of wheelchair users in the autonomous wheelchair and improve safety among all road users, and fostering friendly interactions between them. The contributions of this paper include the following:  
1. Proposal and development of a unique eHMI approach: We developed an on-ground projection-based shared eHMI system for autonomous wheelchairs.