Motion Sickness in Autonomous Cars: Causes and Solutions

Autonomous vehicles (AVs) promise to transform transportation: safer roads, more productive commutes, and greater mobility for people who cannot drive. Yet as control of the vehicle shifts from human drivers to automated systems, an unintended human problem has emerged — motion sickness. Passengers who once trusted their own steering and visual cues may now feel queasy when they are no longer in control or when vehicle motion becomes smoother or more unpredictable. This article explains why motion sickness occurs in autonomous cars, summarizes what research and industry testing have discovered about its prevalence and risk factors, and presents technological, design and behavioral solutions that can reduce or prevent symptoms. Illustrations and practical recommendations are provided for engineers, designers, fleet operators, and everyday passengers.

What is motion sickness?

Motion sickness is a syndrome characterized by symptoms such as nausea, dizziness, cold sweats, pallor, and in severe cases, vomiting. Although often associated with seasickness, it also occurs in cars, airplanes, virtual reality, and spaceflight.

Two major frameworks explain why motion sickness develops:

1. Sensory conflict / sensory mismatch theory. The brain expects coordinated signals from the visual, vestibular (inner ear), and proprioceptive systems. When these signals disagree — for example, when your inner ear senses acceleration but your eyes see a stationary book while reading — the mismatch can trigger nausea. This is the most widely accepted explanation for motion sickness.

2. Postural instability theory. This theory argues that prolonged inability to maintain stable posture in response to motion causes sickness. It emphasizes motor control and adaptation rather than only sensory signals.

Both theories overlap and help explain why certain activities (reading in a car, watching a screen in a driverless vehicle, using a VR headset) increase the risk of motion sickness.

Why autonomous cars make motion sickness different (and sometimes worse)

Autonomous vehicles change three important elements that influence motion sickness:

1. Control and predictability. When people drive, they anticipate and control motions — accelerating, braking, steering — which reduces sensory conflict. Being a passive passenger removes that control and reduces the brain’s ability to predict motion, increasing susceptibility.

2. Passenger activities. AVs encourage non-driving activities: reading, typing, watching videos, and working on laptops — all of which reduce visual input about the outside world and increase conflict between what the eyes see and what the vestibular system senses.

3. Vehicle dynamics and smoothness. Paradoxically, smoother driving and new electric vehicle dynamics (e.g., regenerative braking) can increase low-frequency oscillations that are particularly nauseogenic. Similarly, precise lane-centering and micro-corrections may produce lateral accelerations or fluctuating cues that are more provocative than human driving patterns.

These factors can combine: a passenger reading a tablet in a smoothly cornering autonomous taxi may experience stronger sensory mismatch than when that same person is driving.

How common is motion sickness in autonomous/automated driving contexts?

Reported prevalence varies by study, but there is clear evidence that a substantial minority of passengers experience motion sickness in automated contexts and that certain maneuvers and passenger activities raise the risk.

Field and simulator studies have shown that tasks like reading increase sickness risk; controlled tests and large surveys indicate that anywhere from roughly 20–40% of people report motion-sickness symptoms in scenarios with reduced control or increased visual–vestibular mismatch. Experimental testbeds for automated vehicles (such as the Mcity protocol) were developed specifically because early evidence suggested motion sickness could become a limiting factor in user acceptance of self-driving technology.

Individual susceptibility: who is most at risk?

Not everyone is equally vulnerable. Common risk factors include:

• History of motion sickness. People who get seasick, airsick, or carsick are more likely to experience sickness in AVs.

• Age and gender. Children and adolescents can be particularly susceptible; some studies report higher susceptibility among women, though findings vary and are influenced by hormonal and situational factors.

• Task and posture. Engaging in visually-intensive tasks (reading, screen use), reclining seats, or facing away from the direction of travel increase risk.

• Visual and vestibular conditions. Uncorrected vision problems or vestibular disorders increase susceptibility.

Understanding these risk factors helps designers craft mitigation strategies and allows fleet operators to offer user settings for sensitive passengers.

Measuring motion sickness: how researchers quantify the problem

Motion sickness is measured using subjective scales and physiological metrics. Common tools include:

• Simulator Sickness Questionnaire (SSQ): A validated self-report instrument widely used in simulator and vehicle studies.

• Real-time symptom ratings: Simple scales where passengers report symptoms during exposure.

• Physiological measures: Heart rate variability, skin conductance, and gastric activity have been used as objective correlates.

Standardized test protocols (e.g., Mcity’s on-road protocols) allow researchers and manufacturers to compare results across vehicles and driving behaviors.

Causes specific to autonomous vehicles: deeper dive

1. Visual–vestibular mismatch from in-vehicle activities. When a passenger looks down at a device, the vestibular system senses motion while the eyes register a stable object; the brain’s mismatch response triggers nausea.

2. Reduced anticipatory cues. Human drivers often provide anticipatory cues — subtle steering and head movements, eye glances, and speed modulation — that passengers use to predict motion. AVs remove those social and motor cues.

3. Micro-jerks and high-frequency lateral motions. Some autonomous control systems can generate small, frequent steering corrections or suspension responses that are not present in human driving and may be more provocative.

4. Low-frequency oscillations (LFO). Regenerative braking, certain torque profiles in EVs, and flexible body dynamics can produce LFOs (0.1–0.5 Hz) that are particularly likely to elicit motion sickness.

5. Seating orientation and interior layout. Rear-facing seats, seats that recline too far, and interiors where passengers face each other (e.g., some robo-taxi designs) can increase discomfort and conflict with expected motion cues.

Evidence-based solutions: technology, design, and behavior

Reducing motion sickness in autonomous vehicles requires a multi-layered approach. Here we outline proven and promising strategies.

1. Motion-aware driving algorithms and trajectory planning

• Jerk and acceleration limits. Minimizing jerk (the rate of change of acceleration) and keeping lateral and longitudinal accelerations within comfort envelopes reduces provocative cues.

• Frequency shaping and motion control. Advanced control strategies can target and suppress motion frequencies known to induce sickness. Coordinated control of suspension, steering, and powertrain helps reduce vertical and lateral oscillations.

• Predictive and smoother maneuvers. Planning trajectories that avoid sudden adjustments or abrupt lane corrections improves predictability for passengers.

Industry and research groups increasingly include comfort metrics in their path-planning objectives rather than optimizing solely for travel time.

2. Active vehicle systems: suspension and stabilization

• Active suspension systems that counteract body roll and vertical oscillations can reduce vestibular stimulation. High-bandwidth suspension systems and coordinated active control show promise in reducing symptoms during provoking maneuvers.

• Steering and traction control tuning to minimize micro-corrections can also help.

3. Anticipatory cues and haptics

• Pre-maneuver warnings. Vibrotactile or auditory cues that signal lane changes, turns, or stops give passengers extra time to prepare and reduce mismatch. Lab and on-road tests report meaningful reductions in symptoms when anticipatory cues are provided.

• Seat-based haptics. Subtle seat vibrations timed to motion can provide proprioceptive signals that align body expectations with actual motion.

4. Visual strategies and augmented reality

• Maintain visual access to the outside world. Encouraging passengers to look out the windows, larger glazing, and seating layouts that give a clear view of the road reduce sensory conflict.

• Head-up displays (HUD) and augmented reality. Overlaying travel trajectory, upcoming maneuvers, and visual flow cues on windows or displays can help synchronize visual information with motion and reduce nausea.

• Smart in-vehicle displays. Adjusting field of view, limiting screen motion content, or introducing a subtle external motion cue on a display can reduce conflict for screen users.

Augmented visualizations have been shown to reduce sickness in simulator and moving-base experiments by making vehicle motion predictable and visible.

5. Interior design and seating

• Forward-facing seats reduce conflict compared to rear-facing designs. When rear-facing is necessary, visual displays that present forward motion or artificial horizons can mitigate effects.

• Seat geometry and headrests that stabilize the head and reduce unintended head movements help reduce vestibular mismatch.

• Ventilation and odor management. Good airflow and neutral scents can reduce nausea intensity and improve comfort.

6. Personalization and user modes

• Comfort modes. Allow passengers to select comfort-focused driving styles: gentler acceleration, longer braking distances, and reduced cornering speeds for sensitive riders.

• Adaptive control based on physiology. Using wearable sensors or cameras to detect signs of discomfort could let the vehicle adapt driving or provide cues automatically.

7. Behavioral interventions and pharmacology

• Simple behavioral strategies remain effective: look at the horizon, avoid reading or intense screen work during motion, sit in forward-facing positions, and fix the gaze on distant points.

• Medications (e.g., scopolamine, antihistamines) can be effective prophylactically but carry side effects (drowsiness, dry mouth) and may not be suitable for all passengers.

Implementation challenges and trade-offs

Designing for comfort must be balanced with safety, efficiency, and legal requirements. For example, extreme smoothing of acceleration might reduce throughput in dense traffic or conflict with regulatory requirements for collision avoidance. Similarly, adding active suspension or haptic systems increases cost and complexity.

Fleet operators and OEMs must test solutions across diverse populations and environments. Standardized comfort testing protocols, shared datasets, and regulatory guidance will accelerate adoption of effective countermeasures.

Case studies and promising trials

• Mcity (University of Michigan) developed repeatable on-road protocols to test how activities like reading affect sickness and to compare maneuvers. These protocols have helped translate simulator work into real-world vehicle tuning.

• Research consortia in Europe and industry-academic partnerships have produced trajectory-planning algorithms that include comfort as an objective, reducing reported symptoms in test subjects.

• Visual augmentation experiments have shown up to 30–40% symptom reduction in simulator trials when augmented displays or predictive visual flow were used.

Practical recommendations for passengers and operators

For passengers

• Sit facing forward and look at the horizon when possible.

• Avoid intense reading or close-up screen work during motion.

• Choose seats near the vehicle’s center of rotation (often the middle of the vehicle) for less lateral motion.

• Try anticipatory actions (brace slightly when the vehicle signals a turn).

• Consider over-the-counter remedies if you are highly susceptible, but consult a clinician first.

For designers and fleet operators

• Include comfort metrics in path-planning and control cost functions.

• Offer a comfort mode and provide anticipatory haptic/auditory cues.

• Prioritize window visibility and consider AR/HUD support for passengers engaged in visual tasks.

• Test with broad populations using standardized protocols and publish anonymized results to build industry knowledge.

The road ahead: research gaps and priorities

Key areas for further research include:

• Better population-level prevalence data from on-road deployments and diverse cohorts.

• Personalization algorithms that adapt vehicle behavior in real time based on physiological signifiers of discomfort.

• Cost-benefit analyses to determine how comfort-focused driving impacts traffic flow, energy use, and safety.

• Standardized testing and regulation that integrate human comfort with safety certification frameworks.

Addressing these gaps will help ensure broad acceptance of autonomous mobility systems.

Motion sickness in autonomous cars is a solvable problem — but it requires attention from vehicle engineers, human factors specialists, designers, and policymakers. By combining motion-aware control, vehicle hardware, anticipatory cues, smart visual design, and user-facing options, the industry can substantially reduce symptoms and make autonomous travel comfortable and accessible for most passengers. As AVs scale from pilots to full deployment, building and testing these countermeasures now will pay dividends in user acceptance and wellbeing.

References (books, reviews, and international data sources)

• Golding, J. F. (2006). Motion sickness susceptibility. Autonomic Neuroscience: Basic and Clinical.

• Lackner, J. R. (2014). Motion sickness: more than nausea and vomiting. Experimental Brain Research / Review Articles.

• Mcity (University of Michigan). Measuring motion sickness in driverless cars. White paper and protocols (2019).

• Pereira, E., et al. (2024). Motion sickness countermeasures for autonomous