## 9 Discussion
### Open Topics  
In order to acquire a more complete understanding of the immense potential and inherent complexities involved in the development of emotionally intelligent autonomous vehicles (EIAVs), the following clarifications may be considered:  
* **In-vehicle health monitoring [167]:** An enhanced focus on health monitoring within vehicles could lead to the incorporation of sophisticated biometric and physiological sensors. These could track passenger vital signs, stress levels, and emotional states. EIAVs, equipped with advanced health monitoring systems could adapt their behavior in real time to ensure a safer and more comfortable journey. For example, detecting elevated stress levels could trigger a more conservative driving style or initiate a calming ambient environment.  
* **Simulation-based testing and validation [168]:** Enriching simulation-based testing for emotionally intelligent autonomous vehicles (EIAVs) necessitates the introduction of multifaceted emotional scenarios. This incorporation brings a novel layer of complexity to the validation process. For instance, the manner in which an EIAV responds to a passenger experiencing emotional distress becomes a crucial metric of its performance. Similarly, the vehicle's ability to identify and respond appropriately to a passenger's discomfort in heavy traffic or concern about speed forms an essential part of its evaluation. The development of such emotional scenario databases for rigorous testing, followed by a thorough assessment of EIAVs' responses, constitutes a pivotal aspect of their evolution.
* **Underwater Internet of Things (UIoT) [169]:** Gaining higher-level abstractions or insights from the UIoT, EIAVs could incorporate underwater vehicular communication protocols for specific applications, such as underwater rescue or exploration vehicles. The unique challenges and solutions in UIoT communication could provide valuable lessons to enhance V2X (vehicle to everything) communication in EIAVs, even under challenging conditions.
* **Intelligent data processing methods [170]:** To enhance EIAV capabilities, we might integrate artificial intelligence and machine learning techniques to analyze the vast amount of data these vehicles would generate and receive. Emotional data, in particular, can be complex and multimodal, necessitating sophisticated, AI-driven approaches for reliable interpretation and reaction.
* **Autonomous traffic management (ATM) [171]:** Extending the scope of ATM to include affective factors is an intriguing area of research. For instance, traffic congestion, an external factor, invariably impacts the overall mood of a passenger within a vehicle. An advanced EIAV could be designed to intelligently respond to such circumstances. It may proactively select routes less predisposed to causing passenger stress or leverage onboard systems to sustain a tranquil environment, irrespective of the traffic conditions outside. This indicates that future EIAVs must not only be able to navigate efficiently through tangible road networks but also to understand the complex nature of human emotions.  
These topics further underscore the groundbreaking potential of EIAVs and the multitude of open-ended research avenues they present. By employing a cross-disciplinary approach and incorporating lessons learned from related disciplines, the emergence of EIAVs as an essential component of our transportation ecosystem becomes more plausible.