====== Modes of Interactions ======
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While the previous section described the foundations and goals of HMI, this section focuses on **how autonomous vehicles communicate with various stakeholders** and through which modes.  
These interactions can be categorized by user type, purpose, and proximity.

===== 1. Passenger Communication =====

The **vehicle–passenger interface** supports comfort, awareness, and accessibility. It replaces the human driver’s social role by providing:

  * Visual or auditory cues explaining system decisions (e.g., “Yielding to pedestrian”).  
  * Clear indications of route, stops, and operational mode.  
  * Options for emergency stop, help request, or trip feedback.  

Passenger communication must balance automation with reassurance. In an Estonian field study (Kalda, Sell & Soe, 2021), over 90% of first-time AV users reported feeling safe and willing to ride again when the interface clearly explained the vehicle’s actions.

===== 2. Pedestrian Communication =====

The **vehicle–pedestrian interface (V2P)** substitutes human cues such as eye contact or gestures.  
The *Language of Driving* (Kalda et al., 2022) proposes using standardized visual symbols, light bars, or projections to express intent:

  * Green arrows — invitation to cross.  
  * White pulses — awareness of pedestrian presence.  
  * Red cross — do not cross / vehicle in motion.  

Pedestrian communication must remain **universal and intuitive**, avoiding dependence on text or language comprehension.

===== 3. Safety Operator and Teleoperation =====

At current autonomy levels (L3–L4), a **safety operator interface** remains essential.  
Two variants exist:

  * **Onboard HMI:** allows manual control, displays alerts, and ensures quick handover.  
  * **Teleoperation station:** enables remote monitoring and control via secure networks.  

Teleoperation acts as a *bridge* between human oversight and full autonomy — essential for handling ambiguous traffic or emergency scenarios.

===== 4. Maintenance and Diagnostics Interface =====

A dedicated **maintenance interface** enables technicians to safely inspect and update the vehicle:

  * Sensor and actuator diagnostics.  
  * Log analysis and system replay.  
  * Secure firmware updates and access control.  

Such interfaces ensure traceability, reliability, and compliance with safety regulations.

===== 5. Fleet Manager Interface =====

Fleet-level interfaces provide centralized control and analytics for multiple vehicles.  
They support:

  * Mission planning and route monitoring.  
  * Predictive maintenance using vehicle telemetry.  
  * Integration with smart city and MaaS platforms.  

These tools operate mainly over remote communication channels, relying on secure data infrastructure.

===== 6. Direct vs. Remote Communication =====

Autonomous vehicle interaction can be divided into **direct** (local) and **remote** (supervisory) communication:

^ Type ^ Example ^ Key Features ^
| **Direct (Local)** | Passenger, pedestrian, or on-site operator | Low latency, physical proximity, immediate feedback. |
| **Remote (Supervisory)** | Teleoperation or fleet control | Network-based, high security, possible latency. |
| **Service-Level (Asynchronous)** | Maintenance, updates, diagnostics | Back-end communication; focuses on reliability and traceability. |


===== 7. Design Principles for Effective Communication =====

To ensure that human–machine communication is intuitive and safe, several universal design principles apply:

  * **Transparency:** reveal intent and system state clearly.  
  * **Consistency:** uniform behavior across environments.  
  * **Accessibility:** accommodate diverse users and abilities.  
  * **Multimodality:** combine light, sound, and motion cues.  
  * **Security and privacy:** protect both human and machine data.  

When applied systematically, these principles make autonomous systems understandable, predictable, and trustworthy.

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**References**

Kalda, K.; Pizzagalli, S.-L.; Soe, R.-M.; Sell, R.; Bellone, M. (2022). *Language of Driving for Autonomous Vehicles.* Applied Sciences, 12(11), 5406. [https://doi.org/10.3390/app12115406](https://doi.org/10.3390/app12115406)

Kalda, K.; Sell, R.; Soe, R.-M. (2021). *Use Case of Autonomous Vehicle Shuttle and Passenger Acceptance.* Proceedings of the Estonian Academy of Sciences, 70(4), 429–435. [https://doi.org/10.3176/proc.2021.4.09](https://doi.org/10.3176/proc.2021.4.09)