Modes of Interactions

[raivo.sell]

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.

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.

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.

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.

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.

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.

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

Each communication mode imposes distinct requirements on reliability, bandwidth, and human comprehension. Together, they form a *hierarchical communication ecosystem* that ensures both operational safety and human trust.

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.

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)