Passenger in autonomous transport: the evolution of the human role from operator to system element

Autonomous transport is not just a technological upgrade; it is a fundamental shift in the paradigm of movement, where the passenger ceases to be just a "cargo" and becomes an active user of a complex cyber-physical system. This transition gives rise to a complex of scientific, engineering, and psychological issues.

Cognitive offloading and a new form of trust

In traditional transport, the passenger delegates responsibility to the driver, assessing his competence by indirect signs (driving style, appearance). In an autonomous system, trust is transferred to the algorithm and its developers. This is a technological trust based on safety statistics, but devoid of human empathy. The passenger experiences cognitive dissonance: the brain, evolutionarily tuned to assess the intentions of another person, is forced to trust a "black box".

Interesting fact: Studies in autonomous vehicle simulators show that passengers are worse at handling sharp maneuvers performed by an algorithm, even if they are statistically safer and rarer than those of a person. The brain interprets a sharp braking impulse from the system as a failure, and from a driver — as a manifestation of caution.

Change in sensory experience and motion sickness

A person in a vehicle is not a passive object. His vestibular apparatus, vision, and proprioception (body position sensation) form a unified sensory picture. The driver, actively participating in the control, anticipates maneuvers, which reduces the risk of motion sickness. The passenger of an autonomous vehicle, deprived of predictability and control, becomes more vulnerable.

Scientific approach:

• For this, engineers and neurophysiologists are working on:
• Predictable trajectory. Movement algorithms learn not only to be safe but also "smooth," avoiding sharp accelerations that are unusual for human perception.
• Sensory coordination. Systems of augmented reality may be used in the cabin to visualize vehicle plans (for example, trajectory illumination on the windshield), or tactile seats that give a warning signal before a turn.
• Interior ergonomics. Seats with optimal head support and the ability to choose orientation (especially in autonomous taxis) help reduce sensory conflict.

Safety as a perceived, not statistical, parameter

According to modeling data, autonomous transport has the potential to reduce the number of accidents by 90%, eliminating the main cause — human factor (errors, fatigue, alcohol). However, the perceived safety of the passenger depends on something else.

• The "trolley problem" in the code: How should the algorithm act in an unresolved ethical situation? Swerve sharply to avoid hitting a pedestrian, but put passengers at risk? Public acceptance of technology depends on the transparency and consensus around these moral frameworks embedded in the code.
• The psychology of the "black box." The passenger needs not just safety but also an understanding of what is happening. Interfaces explaining the actions of the system in real-time will be in demand: "Stopping to avoid a pedestrian on the left," "Changing lanes because of an accident ahead."

Transformation of space and time

The elimination of the steering wheel and pedals radically changes the interior architecture. Passenger space becomes a mobile office, a cinema, or a rest room. This gives rise to new requirements:

• Stable internet and power supply for personal devices.
• Active safety systems inside the cabin (passenger condition sensors, fire extinguishing, door locking in an emergency situation).
• Universal design for people with disabilities, for whom the autonomous vehicle is the key to new mobility.
• The time resource of the passenger is freed up, which may change the structure of cities (less need to live near work) and increase overall productivity.

Examples and prototypes: from concepts to reality

Waymo One (USA) — the world's first commercial service of fully autonomous taxis (without an instructor driver) in the Phoenix, Arizona area. Passengers call a car through an application, which follows the route autonomously. The service collects unique data on passenger interaction with the system.

Autonomous shuttles (Russia, Europe). Low-speed electric buses (MatrЁshka, Yandex) circulate on closed territories (VDNKh, Skolkovo, university campuses). Their value lies in the training of interaction with pedestrians and infrastructure in a predictable environment.

Airbus Pop.Up (concept) — a modular system where the passenger capsule can connect both to an automotive chassis and to an air drone, creating a hybrid of ground and air autonomous transport.

Autonomous cargo ships (Yara Birkeland, Norway) — although they do not carry passengers, they are an important testbed for autonomous navigation systems in complex conditions.

Future challenges: from hacking attacks to the legal field

Cybersecurity. An autonomous vehicle is a node in the network. Its hacking can lead not to the theft of one car, but to the collapse of the city's transportation system. Cryptographic protection of communication channels and reserve "emergency" algorithms are required.

Legal responsibility. In the event of an accident, who is to blame: the vehicle owner, the algorithm developer, the sensor developer, or the engineer writing the code? Creating a legal field is a task no less difficult than creating the autopilot itself.

Social acceptance. Dramatic individual accidents with autonomous vehicles (such as the Uber pedestrian hit in 2018) cause disproportionately strong public resonance compared to daily accidents caused by people, slowing down the adoption of technology.

Conclusion

The passenger in autonomous transport is a new anthropological type. His experience is a symbiosis of trust in statistics, adaptation to new sensory conditions, and interaction with an explanatory interface. The success of this quiet revolution depends not only on the quality of algorithms but also on the ability of engineers to take into account human psychology and legislators to create adequate "traffic rules" for artificial intelligence. The ultimate goal is not just to replace the driver but to create a new mobility ecosystem where the passenger, freed from the routine of driving, becomes the center of a comfortable, safe, and efficient transportation network.

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