# Shared eHMI: Bridging Human-Machine Understanding in Autonomous Wheelchair Navigation
## 3 Our Method  
### Featured Challenges and Solutions  
#### 3.1.1 Sensory Capability Limitations and Human-in-the-Loop Integration  
Autonomous wheelchairs predominantly utilize robotics technology, integrating millimeter-wave radar and visual cameras to interpret road conditions. Although these sensors proficiently identify upright structures, their capability to decipher intricate road surfaces remains circumscribed. For instance, detecting deformities like depressions on mud-paved roads poses a significant challenge [54]. However, Human decision-making typically relies on a myriad of contextual factors, and many of these factors are extremely difficult to distill into discrete data streams and assign different weights. This level of nuanced decision-making is challenging for autonomous systems to fully replicate, and they are also susceptible to errors and biases, including false positives and omissions [55]. Consequently, if the system autonomously makes decisions, the ramifications of incorrect choices could lead to extremely serious consequences [56].  
The concept of \"Human-in-the-Loop\" (HITL) refers to the understanding that artificial intelligence, due to its limitations, cannot handle all situations and thus cannot replace humans in real-world applications. Given the robustness and adaptability of humans in complex scenarios, there is a consideration to incorporate human input into the decision-making loop of artificial intelligence systems [57]. To achieve meaningful human control in a HITL system, it is crucial to understand how humans can be involved in autonomous driving decisions from a multidisciplinary and interdisciplinary perspective. This approach considers the intersection of psychology, cognitive science, artificial intelligence, and engineering [57; 58].  
User's confidence in system cognition is paramount. Potential discrepancies between sensor readings and the system's interpretation necessitate users, akin to semi-automobile drivers, to consistently monitor both the road and the system's reactions. The continuous involvement ensures the system's accurate comprehension and the anticipated vehicular response. From a cognitive science perspective, decision-making is an interactive and continuous dynamic high-level cognitive process involving communication between humans and their environment. It is founded on basic cognitive processes such as perception, memory, and attention, as well as core selection processes [58]. These can be enhanced with the aid of sensors and automatic systems, but the human role remains indispensable throughout the operational process, influencing every decision cycle of the system. This is particularly true in dynamic, highly complex, or uncertain environments where machine performance may be impacted [59]. The human-in-the-loop (HITL) approach delegates tasks that are challenging for machines yet simplistic for humans, leveraging human aptitude in nuanced environmental perception, adaptive decision-making, and complex pattern recognition [60]. Merging the high-efficiency performance of sensors and autonomous mechanism with human capabilities provides a highly adaptive solution for navigating scenarios in autonomous wheelchairs, thereby enhancing the overall quality of the system's perception and responsiveness.