# Shared eHMI: Bridging Human-Machine Understanding in Autonomous Wheelchair Navigation
## 2 Related Works
### Shared-Control Wheelchair  
Similar to the classification of autonomous driving, smart wheelchairs can be categorized into fully-autonomous and semi-autonomous control modes based on the degree of allocation of driving rights [30; 31]. Different autonomous driving levels have distinct control strategies, influencing user interaction and eHMI designs; this impacts the communication of wheelchair information to users, user control mechanisms, and the information shared with other road users. In a fully-autonomous control mode, there are three possible scenarios for managing the allocation of driving rights for wheelchairs. Firstly, the driving right is completely handed over to the autonomous system, users simply indicate their destination, and the wheelchair navigates there without collisions [32]. Secondly, external collaborative control allows experienced individuals to remotely or directly control the wheelchair. Additionally, the driving rights of wheelchairs may also be controlled by a centralized system at the upper level, which uniformly allocates and manages the control of multiple wheelchairs [33; 34]. This approach facilitates large-scale management, ideal for settings like hospitals and nursing homes that need centralized wheelchair oversight. Fully-autonomous wheelchairs have been found to overlook user capabilities and intentions. On the other hand, in semi-autonomous wheelchairs, users tend to favor maintaining control during operation and high-level decision-making processes [35]. The shared-control wheelchair employs a semi-autonomous control mode where the user and the autonomous system collaboratively determine the final motion of the wheelchair, aiming for a safer, more efficient, and comfortable driving experience [36; 37]. The shared-control wheelchair will transfer part of the driving rights to the autonomous system. When the autonomous system is unable to determine or the user has a unique preference, the driving rights will be transferred back to the user for specific operations. [PERSON] et al. discussed the human driver model and interaction strategy in vehicle sharing. This protects user experience when technology applications are limited [38]. [PERSON] et al. [39] proposed a reinforcement learning-based control method that can adjust the control weights between users and wheelchairs to meet the requirements of fully utilizing user operational capabilities. High automation makes it suitable for wheelchair users who are unable to perform manual operations, such as those with high paraplegia and cognitive impairment. Shared control encompasses two primary strategies. In the first, known as hierarchical shared control, users dictate high-level actions while the wheelchair autonomously handles collision avoidance [40]. The second strategy merges inputs from both the user and the motion planner, moving the wheelchair only when commands from both are present, thereby enhancing user control and collaboration [31; 37; 41; 42]. Guided by these strategies to foster a more suitable human-machine collaboration for shared-control wheelchairs, the user retains higher-level decision-making while allowing the automated system to handle simple driving tasks. The wheelchair autonomously moves only when the user activatesthe autonomous mode. It automatically handles basic navigation by planning and selecting optimal routes based on user-specified destinations. Users can influence driving settings and motion planning with advanced directives in autonomous mode, such as adding temporary waypoints, yielding to pedestrians, or adjusting speed. Likewise, users have the authority to pause autonomous control for more detailed adjustments to the wheelchair's behaviors. These control methods ensure a harmonious balance between automation and user preferences in human-machine interaction, optimizing safety and user experience.