Motion sickness has become one of the most critical barriers to the broad acceptance of intelligent electric vehicles. As the automotive industry moves rapidly toward electrification and automation, the driving experience of passengers has changed fundamentally. In an intelligent electric vehicle, the instantaneous torque response of the electric motor, the absence of gear-shifting cues, and the shifting role from driver to passenger all contribute to a new kind of motion sickness that is more frequent and often more severe than that experienced in conventional vehicles. In this work, I investigate the comprehensive evaluation of motion sickness and propose active drive control strategies that mitigate motion sickness in intelligent electric vehicles. The study covers mechanistic modelling, physiological-signal-based recognition, model calibration, vehicle control design, and simulation validation.
1. Background and Research Motivation
Electric vehicles offer clear advantages in energy efficiency and environmental protection, but their driving characteristics also bring new challenges. The electric motor produces maximum torque almost instantly when the accelerator pedal is applied, with a response time typically below 0.2 seconds in many modern electric vehicles. By contrast, internal combustion engines usually exhibit a delay of more than one second between pedal input and full torque delivery, and the torque build-up is complicated by turbocharger lag and transmission response. Consequently, the longitudinal acceleration of an electric vehicle is much more abrupt and less filtered than that of a conventional internal combustion engine vehicle. Passengers who are not prepared for such rapid changes in longitudinal force are likely to experience sensory conflict and motion sickness.
Moreover, the emergence of autonomous driving changes the role of occupants. In manually driven electric vehicles, the driver can predict upcoming acceleration and braking because they initiate the control actions. Passengers, however, cannot anticipate exactly when the vehicle will accelerate or decelerate. In level-3 and higher autonomous electric vehicles, all occupants become passengers. They may be engaged in non-driving tasks such as reading, using a mobile phone, working, or watching video. These activities reduce their ability to anticipate vehicle motion and increase visual-vestibular conflict. Studies have indicated that unpredictable motion causes more motion sickness than predictable motion, and that the motion sickness incidence in autonomous electric vehicles is significantly higher than in manually driven electric vehicles. Thus, there is an urgent need for an active, vehicle-level motion-sickness mitigation strategy.
Another motivation comes from the spectral characteristics of acceleration. Translational oscillation frequencies between 0.1 Hz and 0.5 Hz are particularly provocative for humans. Electric vehicles often operate in urban stop-and-go traffic where the acceleration events have main frequency components in exactly this range. Vehicle dynamics, especially the pitch motion during rapid acceleration, further affects the otolith organs and semicircular canals in the human vestibular system. Therefore, the drive control algorithm of an intelligent electric vehicle should not only track the desired speed or acceleration but also reduce the expected motion sickness incidence of the occupants.
In this work, I first construct a six-degree-of-freedom subjective vertical conflict (6DOF-SVC) model that quantifies the relationship between vehicle motion and human motion sickness. I then establish a physiological signal evaluation method using electroencephalogram signals for objectively detecting motion sickness. After calibrating the model with experimental data through a genetic algorithm, I design motion-sickness-minimizing drive control strategies for both human-driven and autonomous driving modes. For the human-driven mode, I develop a fuzzy logic controller that modifies the accelerator pedal map based on the predicted motion sickness incidence. For the autonomous mode, I apply nonlinear model predictive control to generate acceleration commands that minimize passenger motion sickness while maintaining high tracking performance. The effectiveness of both strategies is validated through simulation in a CarSim/Simulink environment.
2. Mechanism of Motion Sickness and the 6DOF-SVC Model
Sensory conflict theory is widely used to explain the occurrence of motion sickness. The brain continuously compares actual sensory information from the vestibular system, visual system, and proprioceptors with the expected information generated by an internal model based on previous motion experience. When a mismatch occurs, and the mismatch persists for a sufficiently long time, motion sickness symptoms emerge. The conflict can originate from differences between semicircular canal cues and otolith cues, from disagreement between visual and vestibular motion cues, or from a discrepancy between actual motion and the motion expected by the central nervous system.
For vehicle occupants, the most important source of sensory conflict is the mismatch between the actual gravito-inertial force vector and the internal estimate of the direction of gravity. The subjective vertical conflict theory simplifies the problem by claiming that motion sickness depends on the conflict between the sensed vertical orientation and the expected vertical orientation. The otolith organs provide a measure of specific force, which is the sum of translational acceleration and gravitational acceleration. The brain, however, cannot directly distinguish gravity from inertial acceleration. During vehicle acceleration, the specific force vector tilts backward, and the brain may misinterpret this tilt as a change in gravity direction. If the occupant cannot anticipate the tilt because he or she is not actively driving, the internal model is slow to update the expected vertical direction, and the resulting conflict is interpreted as motion sickness.
The original subjective vertical conflict model has been extended to six degrees of freedom by considering both linear accelerations along three axes and angular velocities around three axes. I denote the specific force vector as \( \mathbf{f} \), the angular velocity vector as \( \boldsymbol{\omega} \), and the inertial acceleration vector as \( \mathbf{a} \). The specific force is:
\[
\mathbf{f} = \mathbf{a} + \mathbf{g}
\tag{1}
\]
where \( \mathbf{g} \) is the gravitational acceleration vector. In the peripheral vestibular model, the otolith organ is modelled as a unit transfer function. The semicircular canal is represented by a high-pass filter:
\[
G_{SCC}(s) = \frac{\tau_d s}{\left(1 + \tau_a s\right)\left(1 + \tau_d s\right)}
\tag{2}
\]
where \( \tau_d \) and \( \tau_a \) are the adaptation and decay time constants, respectively. The internal model of the semicircular canal has a much simpler transfer function:
\[
G_{\widehat{SCC}}(s) = \frac{\tau_d s}{1 + \tau_d s}
\tag{3}
\]
The perceived vertical orientation, denoted \( \mathbf{V} \), is updated through the following leaky integrator and cross-product operation:
\[
\frac{d\mathbf{V}}{dt} = \frac{1}{\tau}\left( \mathbf{f} – \mathbf{V} – \boldsymbol{\omega} \times \mathbf{V} \right)
\tag{4}
\]
where \( \tau \) is a time constant. The subjective vertical conflict is defined as the difference between the actual sensed vertical representation and the internal expected vertical representation:
\[
\Delta \mathbf{V} = \mathbf{V} – \widehat{\mathbf{V}}
\tag{5}
\]
The conflict signal is then mapped to motion sickness incidence through a Hill-type saturation function and a temporal integration process. In the original model, the learning process uses a first-order integrator. To better match the frequency response of human motion sickness, I replace the simple learning process with a second-order low-pass filter. The transfer function of the revised filtering process is:
\[
G_L(s) = \frac{1}{s(\tau_L s + 1)}
\tag{6}
\]
where \( \tau_L \) is an additional time constant that controls the bandwidth of the filter. A larger \( \tau_L \) broadens the passband and improves the model response to prolonged low-frequency stimulation. The six-degree-of-freedom model also includes multiple feedback gains denoted \( K_a \), \( K_\omega \), \( K_{a c} \), \( K_{\omega c} \), and \( K_{v c} \). The model consists of an external sensory pathway and an internal estimation pathway, both of which are coupled with the subjective vertical conflict computation. The initial parameters are summarized in the following table.
| Parameter | Symbol | Initial value | Unit |
|---|---|---|---|
| Vertical orientation time constant | \( \tau \) | 5 | s |
| Semicircular canal adaptation time constant | \( \tau_a \) | 190 | s |
| Semicircular canal decay time constant | \( \tau_d \) | 7 | s |
| Integrator time constant | \( \tau_I \) | 720 | s |
| Low-pass filter time constant | \( \tau_L \) | 0 | s |
| Linear acceleration gain | \( K_a \) | 0.1 | – |
| Angular velocity gain | \( K_\omega \) | 0.8 | – |
| Linear acceleration feedback gain | \( K_{ac} \) | 1 | – |
| Angular velocity feedback gain | \( K_{\omega c} \) | 5 | – |
| Vertical conflict feedback gain | \( K_{vc} \) | 5 | – |
| Half-saturation parameter | \( b \) | 0.5 | m/s2 |
| Hill coefficient | \( P \) | 0.85 | – |
The output of the 6DOF-SVC model is the motion sickness incidence percentage. The nonlinear input-output relationship is expressed as:
\[
MSI = \frac{x^n}{x^n + x_{50}^n}
\tag{7}
\]
where \( x \) is the accumulated conflict signal, \( x_{50} \) is the half-saturation value, and \( n \) is the Hill coefficient. The two-stage integration and the Hill saturation jointly make the model a nonlinear dynamic system that can predict the temporal evolution of motion sickness under arbitrary six-degree-of-freedom motion input.
3. Objective Evaluation of Motion Sickness Using EEG Signals
Subjective rating scales such as the Motion Sickness Susceptibility Questionnaire, the Misery Scale, and the Motion Sickness Questionnaire are useful for measuring a person’s experience of motion sickness, but they suffer from discrete sampling and individual bias. To obtain a continuous and objective measure of motion sickness during the operation of an intelligent electric vehicle, I selected electroencephalogram signals as the physiological indicator.
The Misery Scale is a common tool for collecting subjective ratings. It uses an integer score from 0 to 10: a score of 10 represents vomiting, which is generally avoided in experiments; scores of 8 to 9 indicate severe nausea or retching; scores of 5 to 7 correspond to mild to moderate nausea; scores of 2 to 4 represent slight discomfort with symptoms such as headache, stomach awareness, or sweating; and scores of 0 to 1 indicate no symptoms. The following table summarizes the structure of the Misery Scale.
| Score | Symptom level |
|---|---|
| 0 | No discomfort |
| 1 | Some discomfort but no specific symptom |
| 2–4 | Dizziness, warmth, headache, stomach awareness, sweating, but no nausea |
| 5 | Mild nausea |
| 6–7 | Moderate to strong nausea |
| 8–9 | Severe nausea or retching |
| 10 | Vomiting |
Electroencephalography can directly reflect the neural responses of the brain to vestibular and visual conflict. Among all physiological signals, EEG has higher temporal resolution and closer correlation with the central mechanisms of motion sickness. I designed a real-vehicle experiment in which thirty healthy volunteers participated. The group consisted of twenty-four males and six females, aged between eighteen and thirty years. Before the experiment, each participant completed the Motion Sickness Susceptibility Questionnaire to screen for the normal range of motion sickness susceptibility. The EEG signals were recorded at eight channels following the international 10–20 system: Fpz, Fz, F3, F4, P3, P4, O1, and O2. The reference electrode was placed on one mastoid process, and the sampling frequency was 256 Hz. A nine-axis inertial sensor was mounted on the vehicle dashboard to record the vehicle’s longitudinal acceleration, lateral acceleration, and angular velocity synchronously with the EEG signals.
The experiment used two driving styles: a smooth style and an aggressive acceleration/deceleration style. The aggressive style was designed to elicit mild motion sickness. To accelerate the onset of symptoms, each volunteer was required to read a mobile phone during the driving task, creating a strong visual-vestibular conflict. The participants rated their motion sickness level with the Misery Scale every three minutes. The total exposure time was up to thirty minutes, but the experiment was terminated if the participant developed severe nausea or vomiting. After the experiment, each participant completed the Motion Sickness Questionnaire to quantify the experienced symptoms. Three participants withdrew early due to strong discomfort, leaving valid data for twenty-seven participants.
The raw EEG data contained numerous artifacts, including eye blinks, eye movements, muscle activity, and environmental noise. I preprocessed the EEG signals using MATLAB and the EEGLAB toolbox. A finite impulse response band-pass filter with cutoff frequencies of 1 Hz and 30 Hz was applied to remove baseline drift and high-frequency noise. The signal was then resampled from 256 Hz to 128 Hz and re-referenced. Independent component analysis was used to separate eye movement artifacts from genuine neural signal components. The recorded eight-channel EEG signal \( \mathbf{x}(t) \) is modelled as the product of a mixing matrix \(\mathbf{A}\) and independent sources \(\mathbf{s}(t)\):
\[
\mathbf{x}(t) = \mathbf{A}\mathbf{s}(t)
\tag{8}
\]
Independent component analysis estimates an unmixing matrix \(\mathbf{W}\) so that \(\mathbf{y}(t) = \mathbf{W}\mathbf{x}(t)\) approximates the independent sources. By visually inspecting the spatial topographies and time series, components corresponding to eye blinks and horizontal eye movements were identified and removed. Baseline correction was then performed on each artifact-free segment.
For the classification task, I segmented the preprocessed EEG data into epochs of six seconds. Each epoch was labelled as “motion sickness” or “normal” according to the subjective Misery Scale score. If the score was greater than or equal to five, the segment was labelled as motion sickness; otherwise it was labelled as normal. After eliminating segments containing residual artifacts, the final dataset contained 2,478 valid epochs. I adopted the TSception architecture, which is a temporal-spatial convolutional neural network designed for EEG signal classification. TSception captures multi-scale temporal dynamics and asymmetric spatial patterns from EEG signals, making it more suitable for detecting motion sickness than conventional convolutional neural networks.
The dynamic temporal convolution layer extracts frequency-specific features using multiple one-dimensional convolution kernels of different sizes. The asymmetric spatial convolution layer captures the spatial relationships between distant electrodes. A high-level fusion layer combines the learned feature maps, and a softmax classifier outputs the probability of each class. Let \(\mathbf{X}\) denote the input EEG segment. The temporal features are:
\[
\mathbf{Z}_T = \Phi_{ReLU}\left( AP\left( Conv1D_{D,T_s}(\mathbf{X}) \right) \right)
\tag{9}
\]
where \(AP\) is average pooling and \(Conv1D_{D,T_s}\) denotes depthwise one-dimensional convolution with kernel size \(T_s\). The spatial features are obtained with:
\[
\mathbf{Z}_S = \Phi_{ReLU}\left( AP\left( Conv1D_{D,T_s}(\mathbf{X}) \right) \right)
\tag{10}
\]
The final fused feature vector is flattened and passed through fully connected layers. I trained the network using cross-entropy loss and the AdamW optimizer with an initial learning rate of 0.001. The batch size was 32, and the number of epochs was 100. A one-cycle learning rate scheduler was applied to accelerate convergence and improve generalization.
The performance of TSception was compared with that of EEGNet, DeepConvNet, and a conventional CNN. The following table shows the test results.
| Model | Training accuracy | Training loss | Test accuracy | Test loss |
|---|---|---|---|---|
| Conventional CNN | 0.869 | 0.081 | 0.768 | 0.224 |
| DeepConvNet | 0.891 | 0.118 | 0.874 | 0.243 |
| EEGNet | 0.913 | 0.055 | 0.902 | 0.161 |
| TSception | 0.957 | 0.078 | 0.917 | 0.182 |
The TSception classifier reached a test accuracy of 91.7%, which was the highest among the compared methods. The conventional CNN was unable to sufficiently model the time-frequency characteristics of EEG signals. EEGNet performed reasonably but its single-scale temporal kernel could not fully capture the broad range of frequency components that vary during motion sickness. DeepConvNet used multi-layer convolution but lacked the multi-scale receptive field structure. TSception, with its multi-branch dynamic temporal layer and non-symmetric spatial layer, produced the most robust representation for distinguishing motion sickness from the normal state.
4. Model Calibration Based on Objective Evaluation
The initial 6DOF-SVC model parameters were chosen from literature and preliminary tests, but the output MSI curve did not match the EEG-based objective evaluation precisely. I therefore used the objective evaluation results as the target for model calibration. The model parameters were adjusted through a genetic algorithm to minimize the difference between the simulated MSI curve and the physiological evaluation curve.
The genetic algorithm encodes each parameter vector as a chromosome. The population size was set to 60. The parameters to be optimized were \(K_{vc}\), \(\tau_L\), \(K_{\omega c}\), and \(K_{ac}\). I defined the objective error as a weighted sum of four temporal errors:
\[
E(\mathbf{X}) = w_u e_u(\mathbf{X}) + w_p e_p(\mathbf{X}) + w_d e_d(\mathbf{X}) + w_r e_r(\mathbf{X})
\tag{11}
\]
where \(e_u\) is the rise-time error, \(e_p\) is the peak amplitude error, \(e_d\) is the decay error, and \(e_r\) is the cumulative response error. The fitness function was:
\[
f(\mathbf{X}) = \frac{1}{1 + E(\mathbf{X})}
\tag{12}
\]
Lower error corresponds to higher fitness. The genetic algorithm used roulette-wheel selection, single-point crossover, and Gaussian mutation. The iteration continued for 200 generations. The search process converged quickly to a region of high fitness, as indicated by the clustering of high-fitness individuals in parameter space.
After calibration, the model output MSI curve closely matched the objective evaluation curve in both the transient response and the final cumulative percentage. The optimized parameters are shown in the following table.
| Parameter | Symbol | Optimized value |
|---|---|---|
| Semicircular canal adaptation time constant | \( \tau_a \) | 190 s |
| Semicircular canal decay time constant | \( \tau_d \) | 7 s |
| Low-pass filter time constant | \( \tau_L \) | 0.04 s |
| Linear acceleration gain | \( K_a \) | 0.1 |
| Angular velocity gain | \( K_\omega \) | 0.8 |
| Linear acceleration feedback gain | \( K_{ac} \) | 1.1 |
| Angular velocity feedback gain | \( K_{\omega c} \) | 4.8 |
| Vertical conflict feedback gain | \( K_{vc} \) | 5.5 |
The calibrated model showed a stronger response in the low-frequency range and a smoother growth of MSI over time. This calibration is essential for using the 6DOF-SVC model in real-time drive control because the controller needs a reliable prediction of motion sickness tendency as a feedback signal.
5. Vehicle Dynamics and Longitudinal Control Platform
To verify the proposed drive control strategies, I built a simulation platform in Simulink and CarSim. CarSim provides a high-fidelity vehicle dynamics model, including suspension kinematics, tire nonlinearities, driveline dynamics, and body pitch/roll. In this work I focused primarily on the longitudinal dynamics. The longitudinal force balance on a slope is:
\[
F_x = F_f + F_i + F_a + F_{air}
\tag{13}
\]
where the driving force \(F_x\) is:
\[
F_x = \frac{T_{tq} i_g i_0 \eta_t}{r}
\tag{14}
\]
The rolling resistance is:
\[
F_f = m g f \cos\theta
\tag{15}
\]
The gradient resistance is:
\[
F_i = m g \sin\theta
\tag{16}
\]
The acceleration resistance is:
\[
F_a = \delta m a
\tag{17}
\]
and the aerodynamic drag is:
\[
F_{air} = \frac{C_D A u^2}{21.15}
\tag{18}
\]
The vehicle parameters used in CarSim correspond to a C-segment electric sedan with a maximum motor power of 180 kW and maximum torque of 400 N·m. The following table lists the main parameters of the simulated electric vehicle.
| Parameter | Value | Unit |
|---|---|---|
| Front track width | 1675 | mm |
| Rear track width | 1675 | mm |
| Frontal area | 2.2 | m2 |
| Aerodynamic drag coefficient | 0.3 | – |
| Tire size | 215/55 R17 | – |
| Rolling resistance coefficient | 0.014 | – |
| Maximum motor power | 180 | kW |
| Maximum motor torque | 400 | N·m |
| Rated motor speed | 4298 | r/min |
The longitudinal controller for human-driven mode used a driver model that generated an expected acceleration from a speed error. The speed error is defined as:
\[
e(t) = v_{ref}(t) – v(t)
\tag{19}
\]
and the PID output is:
\[
a_{des}(t) = K_P e(t) + K_I \int e(t) dt + K_D \frac{de(t)}{dt}
\tag{20}
\]
For autonomous driving mode, I implemented a dual PID controller using position and speed errors. The position error is:
\[
e_s(t) = s_{ref}(t) – s(t)
\tag{21}
\]
which generates a speed compensation term \(\Delta v\). The speed error is:
\[
e_v(t) = v_{ref}(t) – v(t) + \Delta v(t)
\tag{22}
\]
The final desired acceleration is then computed by a speed PID controller. These conventional controllers serve as the baseline against which I compare the anti-motion-sickness controllers.
Because the CarSim model cannot accept an acceleration value directly, I built a calibrated model of the accelerator pedal map and brake pressure map. The pedal position was modelled as a function of vehicle speed \(v\) and acceleration \(a\):
\[
P_{acc} = f_{acc}(v,a)
\tag{23}
\]
The brake pressure was modelled as:
\[
P_{br} = f_{br}(v,a)
\tag{24}
\]
I calibrated these functions by simulating constant pedal positions over a grid of speeds and accelerations. The resulting three-dimensional surface was smoothed using Gaussian convolution and median filtering. The combined drive/brake map converted the desired acceleration into an actuator command that consisted of either motor torque or brake cylinder pressure.
The baseline controllers were validated under multiple conditions. For a sinusoidal acceleration command with frequency 0.08 Hz and amplitude 3 m/s², the maximum speed tracking error was less than 0.1 m/s, and the maximum position error was less than 0.7 m. The standard FTP75 urban driving cycle was also tested. The controller tracked the reference speed accurately during acceleration, cruising, and braking phases, demonstrating the reliability of the simulation platform.
6. Why Electric Drive Vehicles Are More Provocative
I compared the torque response characteristics of an electric motor and an internal combustion engine. The electric motor reaches the target torque in approximately 0.2 seconds and the torque build-up is almost perfectly linear. The engine, on the other hand, requires more than 1.2 seconds, and its torque trajectory is affected by throttle delay, manifold dynamics, and turbocharger lag. From the perspective of motion sickness, the prompt and linear torque response of an electric vehicle is not necessarily beneficial. A high jerk value during the initial pedal application translates into a sudden backward pitch and an upward motion of the head. If the passenger is not expecting the movement, the otolith organs and semicircular canals receive a concentrated low-frequency stimulus that strongly conflicts with the internal estimate of gravity.
In addition, many electric vehicles use regenerative braking in a way that combines a moderate braking torque from the motor and a delayed hydraulic braking torque from the conventional brake. The transition between these two sources can produce a jerky deceleration profile, further increasing passenger discomfort. Through careful calibration of the accelerator pedal map and the torque response, an electric vehicle can emulate the smooth torque delivery of a well-tuned conventional vehicle. Therefore, I developed a Pedal Map optimization method that defines both an efficiency-oriented hard response curve and a comfort-oriented soft response curve. The anti-motion-sickness drive control scales between these two curves according to the real-time motion sickness prediction.
7. Fuzzy Anti-Motion-Sickness Control for Human-Driven Mode
In human-driven mode, the ordinary driver is not aware that their acceleration behavior may be causing discomfort to passengers. I integrated the calibrated 6DOF-SVC model into the drive control loop to provide a real-time estimate of motion sickness incidence. The fuzzy controller modifies the motor torque request according to the accelerator pedal opening \(ACC\), its derivative \(DACC\), and the predicted MSI. The output of the fuzzy controller is a dimensionless coefficient \(K\) that interpolates between the hard and soft pedal maps. The final torque load fraction \(L\) is calculated as:
\[
L = L_{soft} + K \left( L_{hard} – L_{soft} \right)
\tag{25}
\]
where \(L_{hard}\) is determined by the efficiency-oriented hard pedal map and \(L_{soft}\) by the comfort-oriented soft pedal map. When the predicted motion sickness MSI is low and the accelerator is smooth, the controller retains the hard map to preserve vehicle responsiveness. When the risk of motion sickness increases, the controller gradually shifts the torque request toward the soft map, reducing the abruptness of acceleration.
The hard map was calibrated using typical pedal-position working points. The following table lists the relationship between accelerator pedal opening and torque load coefficient for the efficiency-oriented curve.
| Accelerator opening (%) | 0 | 20 | 40 | 60 | 80 | 100 |
|---|---|---|---|---|---|---|
| Torque load coefficient | 0 | 0.31 | 0.48 | 0.67 | 0.82 | 1.00 |
The soft map emphasizes a gradual torque build-up that prevents aggressive acceleration in urban conditions. Its working points are shown below.
| Accelerator opening (%) | 0 | 20 | 40 | 60 | 80 | 100 |
|---|---|---|---|---|---|---|
| Torque load coefficient | 0 | 0.040 | 0.076 | 0.160 | 0.420 | 1.000 |
The two curves can be interpolated into a two-dimensional Pedal Map surface. For a given accelerator opening and a given torque load coefficient, the desired motor torque is computed by multiplying the load coefficient by the maximum torque at the current motor speed:
\[
T_{des} = T_{max}(n_m) \cdot L
\tag{26}
\]
where \(n_m\) is the motor speed.
The fuzzy controller has three inputs and one output. The universes of discourse were selected according to the possible ranges of vehicle operating variables. The accelerator opening \(ACC\) was normalized to [0,100], where 100% represents the fully depressed pedal. The accelerator opening rate \(DACC\) was also normalized to [0,100]. The motion sickness incidence output from the calibrated model was represented in the range [0,0.2], and the output coefficient \(K\) was normalized to [0,1]. The linguistic variable sets for the inputs ACC and DACC were chosen as {VS, S, M, B, VL}, where VS means very small, S means small, M means medium, B means big, and VL means very large. The MSI input was represented by {S, M, L} and the output K by {VS, S, M, B, VL}.
I used triangular and trapezoidal membership functions because they are simple and provide sufficient flexibility for the nonlinear mapping. The fuzzy inference rules were designed based on the dynamic characteristics of electric vehicle drive systems. At low accelerator openings, the electric motor often produces a high torque per pedal change, and the vehicle is likely to accelerate sharply, especially if the driver presses the pedal quickly. Therefore, a small ACC combined with a large DACC should produce a large anti-motion-sickness coefficient K. When the MSI is high, K should be high regardless of the pedal state. Conversely, when the MSI is low and the pedal commands are smooth, K remains close to zero so that the vehicle maintains its normal performance. A representative subset of the fuzzy rules is shown in the following table.
| MSI | ACC | DACC | ||||
|---|---|---|---|---|---|---|
| VS | S | M | B | VL | ||
| S | VS | VS | VS | VS | VS | VS |
| S | M | S | VS | VS | VS | |
| M | M | M | S | VS | VS | |
| B | L | M | M | S | VS | |
| VL | L | L | M | M | S | |
| M | VS | M | S | VS | VS | VS |
| S | M | M | S | S | VS | |
| M | L | M | M | S | S | |
| B | VL | L | M | S | S | |
| VL | VL | VL | L | M | S | |
| L | VS | M | M | S | S | VS |
| S | L | M | M | S | VS | |
| M | VL | L | M | S | S | |
| B | VL | VL | L | M | S | |
| VL | VL | VL | VL | L | M | |
This rule table ensures that the controller behaves consistently with human driving intuition. The fuzzy output K is computed using the center-of-gravity defuzzification method.
8. Model Predictive Control for Autonomous Driving Mode
In autonomous driving, the intelligent electric vehicle should perform longitudinal control without requiring a pedal command from a human driver. Model predictive control provides a natural framework for multi-objective optimization with constraints. I designed a hierarchical controller. The high-level MPC uses the predicted motion sickness incidence from the 6DOF-SVC model as an additional state, together with the vehicle speed, acceleration, and other variables, to compute the desired longitudinal acceleration. The low-level controller converts the desired acceleration into throttle or brake commands using the calibrated inverse vehicle model.
The vehicle states in the MPC prediction model include position \(p\), velocity \(v\), pitch angle \(\theta\), and pitch rate \(\omega\). The control input is the desired traction acceleration \(u_v\). I also included the internal state variables of the calibrated 6DOF-SVC model. To reduce the computational burden while preserving the essential dynamics, I formed a reduced-order state vector:
\[
\mathbf{X}_S = [p, v, a, x, \dot{x}]^T
\tag{27}
\]
where \(x\) is the accumulated conflict signal from the subjective vertical conflict model and \(\dot{x}\) is its time derivative. The continuous non-linear dynamics are denoted:
\[
\dot{\mathbf{X}}_S = f\left( \mathbf{X}_S, u_v \right)
\tag{28}
\]
The model is linearized around a nominal operating point and discretized with a sampling time \(T_s\). The linear discrete model is:
\[
\delta \mathbf{X}_S(k+1) = A_k \delta \mathbf{X}_S(k) + B_k \delta u_v(k)
\tag{29}
\]
where \(A_k\) and \(B_k\) are the discrete state-space matrices obtained from the Jacobians of \(f\).
The objective function of the MPC controller was constructed to balance speed tracking, acceleration smoothness, and motion sickness minimization. Let \(N_p\) be the prediction horizon and \(N_c\) the control horizon. The objective function is:
\[
J = \sum_{k=0}^{N_p-1} \left[
w_1 \left( v(k) – v_{ref}(k) \right)^2 + w_2 \left( a(k) – a_{des}(k) \right)^2 + w_3 x(k)^2 + w_4 \left( \Delta u_v(k) \right)^2
\right]
\tag{30}
\]
where \(w_1, w_2, w_3, w_4\) are positive weights. The first term penalizes speed tracking error. The second term ensures that the actual acceleration follows the desired acceleration pattern generated by the higher level. The third term penalizes the internal conflict signal that is directly related to motion sickness. The fourth term penalizes rapid changes in the control input, thus reducing jerk.
The optimization is subject to state and control constraints that reflect the physical limits of an electric vehicle and the requirement of comfort. The speed is constrained between 0 and 30 m/s, the longitudinal acceleration between \(-5\) and \(3.5\) m/s², the pitch angle between \(-0.8\) and \(0.8\) degrees, and the jerk between \(-5\) and \(5\) m/s³. The control input rate is limited to ±1 m/s² per step. The MPC controller solves the constrained quadratic programming problem at each sampling instant and applies the first element of the optimal control sequence to the vehicle.
An important feature of the proposed MPC strategy is that the 6DOF-SVC model provides real-time feedback of the passenger state. The MPC controller can therefore limit the rate of acceleration increase at the beginning of the motion, even if high-speed tracking would be desirable in the conventional sense. When the motion sickness state is still low, the controller can safely allow faster acceleration to satisfy driving demands. This dynamic trade-off cannot be achieved by a fixed low-pass filter or by simply scaling the pedal map.
9. Simulation Results and Discussion
I first selected sinusoidal acceleration profiles as the verification inputs based on the frequency sensitivity of human motion sickness. According to previous experiments, horizontal and vertical oscillation frequencies around 0.2 Hz produce the highest motion sickness incidence. Therefore, I initially set the frequency to 0.2 Hz and the amplitude to 2 m/s². The initial vehicle speed was 15 m/s. The entire simulation ran for 1,500 seconds. I compared the conventional human-driven controller without anti-motion-sickness logic with the proposed fuzzy-controlled drive strategy.
The simulation results showed that the fuzzy controller reduced the peak acceleration amplitude and slightly delayed the acceleration build-up. The pitch angle and pitch rate were suppressed near the peaks. The longitudinal speed tracking remained close to the reference speed, but the maximum speed error increased slightly because the controller intentionally limited the acceleration to prevent motion sickness. The jerk signal became smoother in most regions, although some small oscillations appeared near the peaks. The MSI output from the calibrated 6DOF-SVC model was 7.06% after 1,500 seconds under the conventional controller. With the fuzzy anti-motion-sickness strategy, the final MSI was 5.63%, corresponding to a relative reduction of 20.26%.
I further carried out simulations at constant acceleration amplitude but different frequencies. The acceleration frequency was varied from 0.1 Hz to 0.5 Hz with the amplitude fixed at 2 m/s². The results are summarized in the following table.
| Frequency | Conventional MSI | Fuzzy-controlled MSI | Relative reduction |
|---|---|---|---|
| 0.1 Hz | 0.99% | 0.98% | 1.01% |
| 0.2 Hz | 7.06% | 5.63% | 20.26% |
| 0.3 Hz | 10.41% | 6.75% | 35.16% |
| 0.4 Hz | 11.79% | 6.25% | 46.99% |
| 0.5 Hz | 12.49% | 5.44% | 56.45% |
The fuzzy anti-motion-sickness strategy was clearly more effective at high-frequency acceleration profiles. This outcome was expected because the fuzzy rules increase the softening effect as the predicted MSI becomes larger. At very low frequencies, the MSI value is already low, and the controller cannot produce significant additional improvement without sacrificing speed tracking too much.
I also performed simulations at a fixed frequency of 0.2 Hz while changing the acceleration amplitude from 1 m/s² to 3 m/s². The results are shown below.
| Amplitude | Conventional MSI | Fuzzy-controlled MSI | Relative reduction |
|---|---|---|---|
| 1 m/s² | 1.66% | 1.30% | 21.69% |
| 2 m/s² | 7.06% | 5.63% | 20.26% |
| 3 m/s² | 15.00% | 12.26% | 18.27% |
The relative reduction decreased slightly when the amplitude increased to 3 m/s², which suggests that the fuzzy controller requires a high K output to suppress very strong acceleration. Even in this case, the absolute reduction remained substantial, with the MSI decreasing from 15% to 12.26%.
For the autonomous driving mode, I tested the MPC strategy under the same 0.2 Hz and 2 m/s² sinusoidal acceleration condition. The conventional autonomous controller without anti-motion-sickness logic produced an MSI of 7.03% after 1,500 seconds. The MPC anti-motion-sickness controller produced an MSI of 6.14%, i.e., a relative reduction of 12.66%. The MPC strategy reduced the peak pitch rate and effectively suppressed jerk during the acceleration phases. The speed tracking error was slightly larger than that of the conventional dual PID controller but still acceptable for autonomous driving applications.
The sinusoidal profile is somewhat artificial. Real roads and traffic typically produce a broad-band acceleration signal with energy concentrated below 1 Hz. To validate the robustness of the MPC controller, I generated a multi-sine acceleration signal by combining twelve sinusoidal components whose frequencies, amplitudes, and phases were randomly chosen within the range 0.01–0.5 Hz. The resulting peak acceleration was limited to 3 m/s². The time-domain signal exhibited a repeating beat-like pattern with a period of roughly 100 seconds. The conventional controller tracked this reference acceleration accurately with an MSI of 4.81% after 1,500 seconds. The MPC anti-motion-sickness controller delivered an MSI of 3.40%, which corresponds to a relative reduction of 29.31%. The frequency-domain analysis of the output acceleration indicated that the MPC controller mainly attenuated the frequency components between 0.2 and 0.5 Hz, which are the most provocative frequencies for motion sickness. Thus, the MPC strategy did not simply reduce all acceleration; it selectively changed the acceleration profile to be less motion-provoking while preserving the low-frequency trend needed for route tracking.
I further evaluated the MPC controller in the frequency domain by sweeping the frequency of the single-sine reference from 0.1 to 0.5 Hz with a step of 0.01 Hz. All the simulations used an acceleration amplitude of 2 m/s². The average relative reduction of MSI across this frequency range was 22.02%. The reduction was strongest near 0.5 Hz and weakest near 0.1 Hz. These results indicate that both fuzzy and MPC strategies can serve as effective anti-motion-sickness drive controllers for an intelligent electric vehicle. The fuzzy controller is more suitable for human-driven electric vehicles because it can be implemented directly in the vehicle control unit as a map-based overlay. The MPC controller is more suitable for autonomous electric vehicles because it can incorporate motion sickness prediction into a full multi-objective optimization that also considers dynamic constraints.
The table below summarizes the overall performance comparison between the fuzzy control and MPC strategies under different conditions.
| Scenario | Strategy | Reference MSI | Controlled MSI | MSI reduction |
|---|---|---|---|---|
| 0.2 Hz, 2 m/s², sinusoidal | Fuzzy | 7.06% | 5.63% | 20.26% |
| 0.2 Hz, 2 m/s², sinusoidal | MPC | 7.03% | 6.14% | 12.66% |
| Multi-sine 0.01–0.5 Hz | MPC | 4.81% | 3.40% | 29.31% |
| Frequency sweep amplitude 2 m/s² | MPC | — | — | Average 22.02% |
| Frequency sweep amplitude 2 m/s² | Fuzzy | — | — | Average 31.97% |
The difference between the fuzzy and MPC results under the 0.2 Hz sinusoidal test should not be interpreted as a general ranking. The fuzzy controller achieved a larger reduction because its rules were tuned to strongly soften the torque request when the motion sickness state was high. The MPC controller, on the other hand, had to maintain a more precise tracking of the reference acceleration and used a cost function with four competing objectives. Depending on the driving priority, the weights \(w_1\) to \(w_4\) can be adjusted. If the operator wants to prioritize comfort over speed tracking, the MPC weight for the conflict state can be increased, yielding an MSI reduction closer to that of the fuzzy strategy, but with a larger tracking error.
10. Conclusions
I have investigated the mechanisms, evaluation, and mitigation of motion sickness in intelligent electric vehicles. The main contribution of this work is a complete framework that combines objective physiological evaluation, a calibrated six-degree-of-freedom motion sickness model, and two active drive control strategies for different driving modes.
First, I constructed a six-degree-of-freedom subjective vertical conflict model based on sensory conflict theory and extended it by replacing the first-order learning process with a second-order low-pass filter. The model parameters were calibrated using experimental EEG data and a genetic algorithm. The resulting model accurately reproduces the temporal evolution of motion sickness incidence under realistic vehicle acceleration and pitch inputs.
Second, I established an objective motion sickness evaluation method using multi-channel EEG signals. Through a real-vehicle experiment, I collected and processed EEG data from thirty volunteers. A TSception-based temporal-spatial convolutional neural network was trained to distinguish motion sickness from normal states with an accuracy of 91.7%. This classifier outperformed conventional convolutional neural networks, EEGNet, and DeepConvNet, demonstrating that deep learning with multi-scale temporal and asymmetric spatial convolution is an effective tool for motion sickness detection.
Third, I developed a fuzzy anti-motion-sickness drive control strategy for human-driven intelligent electric vehicles. The controller uses the calibrated 6DOF-SVC model to predict the motion sickness level and adjusts the torque load coefficient between an efficiency-oriented hard pedal map and a comfort-oriented soft pedal map. The simulation results showed a reduction in MSI of up to 56.45% for high-frequency acceleration profiles and an average reduction of approximately 20% for typical sinusoidal and amplitude variations.
Fourth, I designed a model predictive control strategy for the autonomous driving mode of an intelligent electric vehicle. The MPC controller embeds the calibrated motion sickness model in its prediction model and optimizes speed tracking, acceleration smoothness, motion sickness incidence, and input variation under physical constraints. The controller was tested against sinusoidal and multi-sine acceleration profiles. It reduced the MSI by 12.66% in the baseline 0.2 Hz case and by 29.31% in the broadband case, with an average 22.02% reduction across the frequency range of 0.1–0.5 Hz. The frequency-domain analysis shows that the MPC controller selectively suppresses acceleration components in the most provocative motion sickness frequency range, thereby improving passenger comfort without significantly compromising tracking performance.
This research provides a theoretical basis for the design of active motion-sickness mitigation systems in intelligent electric vehicles. Future work will include real-vehicle tests with human passengers to verify the interaction between the proposed controllers and actual passengers. I will also expand the model to include lateral acceleration and steering-induced roll motion so that the control strategies can be extended to curved road scenarios in future intelligent electric vehicles.