Motion sickness in intelligent electric cars has become a critical issue affecting passenger comfort and the widespread adoption of automated driving. In my research, I systematically investigated the mechanisms, evaluation methods, and active mitigation strategies for motion sickness induced by vehicle longitudinal dynamics. The central goal was to develop drive control strategies that explicitly minimize motion sickness incidence (MSI) while preserving driving performance. My approach combined theoretical modeling with physiological signal measurements and advanced control algorithms. Specifically, I constructed a six-degree-of-freedom subjective vertical conflict (6DOF-SVC) model, calibrated it with electroencephalogram (EEG) based objective assessments, and then designed both human-in-the-loop fuzzy control and model predictive control (MPC) frameworks for intelligent electric cars. Simulation results demonstrated significant reductions in MSI under various acceleration frequencies and amplitudes. This work provides a theoretical foundation for improving dynamic comfort in intelligent electric cars.
1. Introduction
The rapid development of electric vehicles and autonomous driving technology has brought convenience as well as new challenges. Among these, motion sickness, commonly known as “carsickness,” is increasingly reported by passengers in intelligent electric cars. Unlike traditional internal combustion engine vehicles, electric cars often exhibit higher torque response speeds, smoother acceleration profiles, and more frequent automated driving corrections. These characteristics can conflict with human sensory expectations, leading to discomfort. The problem is even more serious in autonomous driving modes because passengers no longer have the ability to predict vehicle motion through active driving actions. As a result, the motion sickness occurrence rate in autonomous electric cars is higher than that in conventional vehicles. Therefore, developing effective anti-motion-sickness control strategies for intelligent electric cars has both academic and industrial significance.
In my study, I focused on drive control strategies rather than suspension or route planning. The reasons are twofold. First, longitudinal acceleration and jerk are the most direct excitation sources for motion sickness during normal driving. Second, the electric motor torque can be controlled instantaneously and precisely, allowing the implementation of advanced algorithms such as fuzzy logic and model predictive control. My research addresses the following key questions: How can motion sickness be quantified and predicted in real time? How can physiological signals provide objective feedback? And how can drive control algorithms exploit this information to reduce MSI?
2. Motion Sickness Mechanisms and Mathematical Modeling
2.1 Sensory Conflict and Subjective Vertical Conflict
The most widely accepted explanation for motion sickness is the sensory conflict theory. When the human brain receives inconsistent information from the vestibular system, visual system, and proprioceptive system, it creates neural conflicts. Among various conflict models, the subjective vertical conflict (SVC) theory is particularly suitable for vehicle motion. It assumes that the brain maintains an internal model of the vertical direction, i.e., the direction of gravity. Any mismatch between the actual sensed vertical orientation and the predicted vertical orientation generates a conflict signal that accumulates over time and leads to motion sickness symptoms. The conflict is not only related to translational acceleration but also to angular velocity and their combinations.
2.2 Six-Degree-of-Freedom Subjective Vertical Conflict Model
To describe motion sickness in vehicles completely, I extended the SVC model to include three translational accelerations and three rotational velocities. This is the 6DOF-SVC model. The structure of the model consists of sensory organs (otoliths and semicircular canals), their internal models, a low-pass filter representing vertical perception, and an integration path that converts the conflict into an MSI prediction. The inputs are the specific force vector \( \mathbf{f} \), angular velocity \( \boldsymbol{\omega} \), and gravity vector \( \mathbf{g} \).
The estimated specific force is given by:
$$ \mathbf{f} = \mathbf{a} + \mathbf{g} $$
where \( \mathbf{a} \) is the inertial acceleration of the vehicle. The otolith transfer function is unity, and the semicircular canal dynamics are:
$$ G_{SCC}(s) = \frac{(1 + \tau_a s)(1 + \tau_d s)}{(\tau_a s)(\tau_d s)} $$
for the peripheral model, and for the internal model:
$$ \tilde{G}_{SCC}(s) = \frac{1}{(1 + \tau_d s)} $$
where \( \tau_a \) and \( \tau_d \) are time constants. The vertical perception low-pass filter is:
$$ \frac{d\mathbf{V}}{dt} = \frac{1}{\tau} \left( \mathbf{f} – \mathbf{V} – \boldsymbol{\omega} \times \mathbf{V} \right) $$
where \( \tau \) is a time constant and \( \mathbf{V} \) is the sensed vertical vector. Similar equations hold for the estimated vertical vector \( \tilde{\mathbf{V}} \).
The conflict vector is defined as \( \Delta \mathbf{V} = \mathbf{V} – \tilde{\mathbf{V}} \). To model the saturating and cumulative nature of motion sickness, a Hill-type function is used after an integration/filtering stage. In my dissertation, a second-order low-pass filter was adopted instead of a single integrator to better match experimental data:
$$ \frac{d^2 x}{dt^2} + 2 \zeta \omega_n \frac{dx}{dt} + \omega_n^2 x = k \cdot \Delta V $$
where \( x \) is an intermediate conflict variable, \( \zeta \) is the damping ratio, \( \omega_n \) is the natural frequency, and \( k \) is a gain. The instantaneous MSI is obtained from the Hill function:
$$ \text{MSI} = \frac{x^n}{x^n + x_0^n} $$
where \( x_0 \) represents the half-saturation value and \( n \) is the Hill coefficient. The initial parameters used in my research are summarized in Table 1.
| Parameter | Symbol | Value | Unit |
|---|---|---|---|
| Time constant (vertical percept) | \(\tau\) | 5 | s |
| Otolith adaptation time | \(\tau_a\) | 190 | s |
| Canal time constant | \(\tau_d\) | 7 | s |
| Internal model integration time | \(\tau_I\) | 720 | s |
| Second-order filter time constant | \(\tau_L\) | 0 | s |
| Acceleration gain | \(K_a\) | 0.1 | – |
| Angular velocity gain | \(K_\omega\) | 0.8 | – |
| Acceleration feedback gain | \(K_{ac}\) | 1 | – |
| Rotational feedback gain | \(K_{\omega c}\) | 5 | – |
| Vertical conflict feedback gain | \(K_{vc}\) | 5 | – |
| Half-saturation constant | \(b\) | 0.5 | m/s² |
| Hill coefficient | \(P\) | 0.85 | – |
3. Objective Motion Sickness Evaluation using EEG
3.1 Experimental Design and Data Collection
To obtain real motion sickness data, I conducted on-road experiments with an instrumented electric vehicle. A total of 30 healthy participants (24 males, 6 females) aged 18–30 years were recruited after screening with the Motion Sickness Susceptibility Questionnaire (MSSQ). Each participant sat in the rear right seat and was asked to read a mobile phone to increase visual-vestibular conflict. The vehicle was driven by an experienced driver over a closed test route, including straight roads, curves, and slopes. Two driving styles were applied: a smooth style for the baseline condition and an aggressive style with repeated rapid accelerations and decelerations to trigger motion sickness. The experiment lasted up to 30 minutes; however, the trial was terminated if a participant experienced severe nausea or vomiting.
During the experiments, I recorded EEG signals using an 8-channel wearable device with Ag/AgCl electrodes positioned according to the international 10–20 system (Fpz, Fz, F3, F4, P3, P4, O1, O2). The reference electrode was placed on the mastoid, and the sampling rate was 256 Hz. Simultaneously, a 9-axis IMU sensor mounted on the dashboard measured vehicle acceleration and angular velocity. Participants provided subjective ratings every 3 minutes using the Misery Scale (MISC). After the experiment, they completed the Motion Sickness Questionnaire (MSQ).
From the collected EEG data, three participants could not complete the experiment, and their data were excluded. The remaining usable synchronized samples were segmented into 6-second epochs, resulting in a dataset of 2,478 labeled samples. Each sample carried a one-hot label: (1,0) for “motion sickness” and (0,1) for “normal state”.
3.2 EEG Signal Preprocessing
Raw EEG signals contain various artifacts such as eye blinks, muscle activity, and line noise. Therefore, careful preprocessing was necessary. First, I band-pass filtered the signals between 1 Hz and 30 Hz using a zero-phase FIR filter. Then the signals were down-sampled to 128 Hz. Re-referencing was performed to the common average reference. The major artifact removal was achieved using Independent Component Analysis (ICA). ICA assumes that observed multichannel signals \( \mathbf{x}(t) \) are a linear mixture of independent sources \( \mathbf{s}(t) \):
$$ \mathbf{x}(t) = \mathbf{A} \mathbf{s}(t) $$
The goal is to find a demixing matrix \( \mathbf{W} \) so that:
$$ \mathbf{y}(t) = \mathbf{W} \mathbf{x}(t) $$
approximates the independent components. In practice, preprocessing steps include centering and whitening. I applied the FastICA algorithm to iteratively update the columns of \( \mathbf{W} \) according to:
$$ \mathbf{w}^+ = E\{ \mathbf{z} g(\mathbf{w}^T \mathbf{z}) \} – E\{ g'(\mathbf{w}^T \mathbf{z}) \} \mathbf{w} $$
where \( g \) is a nonlinear function and \( \mathbf{z} \) is the whitened data. Components representing eye movements or other artifacts were visually identified by their characteristic topography (strong frontal projection) and time course, and then removed. After ICA, a baseline correction was applied using the ERPLAB toolbox. Figure 1 (not redrawn here) illustrates the EEG signals before and after preprocessing.
3.3 Deep Learning Classification with TSception
To automatically distinguish motion sickness states from normal states, I employed a Temporal-Spatial Convolutional Neural Network (TSception). This network is well suited for EEG because it captures multi-scale temporal dynamics and asymmetric spatial patterns. The architecture consists of four main components: a dynamic temporal layer, an asymmetric spatial layer, a high-level fusion layer, and a classifier. The dynamic temporal layer applies multiple 1D convolution kernels of varying sizes to each channel, thus extracting frequency-specific features. The operation can be described by:
$$ \mathbf{Z}_T = \Phi_{ReLU}\left( AP \left( Conv1D(\mathbf{X}; s) \right) \right) $$
where \( \mathbf{X} \) is the input EEG epoch, \( AP \) represents average pooling, and \( s \) denotes the kernel size. Next, the asymmetric spatial layer applies convolution across channels to capture global and hemispheric asymmetries:
$$ \mathbf{Z}_S = \Phi_{ReLU}\left( AP \left( Conv1D(\mathbf{Z}_T; 1) \right) \right) $$
The high-level fusion layer combines temporal and spatial features via global average pooling and batch normalization:
$$ \mathbf{Z}_{fusion} = \Phi_{ReLU}\left( BN \left( GAP \left( Conv1D(\mathbf{Z}_S; (3,1)) \right) \right) \right) $$
Finally, the classifier uses a fully connected layer with softmax activation:
$$ \hat{\mathbf{y}} = \text{softmax}\left( \mathbf{W}_2 \Phi_{ReLU}\left( \mathbf{W}_1 \mathbf{Z}_{fusion} + \mathbf{b}_1 \right) + \mathbf{b}_2 \right) $$
I trained TSception with the AdamW optimizer, an initial learning rate of 0.001, a batch size of 32, and 100 epochs. The dropout rate was 0.5 to avoid overfitting. The model achieved a test accuracy of 91.7% on the motion sickness detection task. Table 2 compares its performance with several baseline models.
| Model | Training accuracy | Training loss | Test accuracy | Test loss |
|---|---|---|---|---|
| Standard CNN | 0.869 | 0.081 | 0.768 | 0.224 |
| EEGNet | 0.913 | 0.055 | 0.902 | 0.161 |
| DeepConvNet | 0.891 | 0.118 | 0.874 | 0.243 |
| TSception (chosen) | 0.957 | 0.078 | 0.917 | 0.182 |
After training, I used the model to predict the probability of motion sickness in consecutive one-minute EEG segments for a participant. The resulting curve was used as an objective evaluation index. The trend showed that motion sickness increases over time, with a rapid rise between 6 and 11 minutes, followed by a slight drop around 11–12 minutes due to self-adaptation.
3.4 Model Calibration with a Genetic Algorithm
The previously described 6DOF-SVC model was initially developed based on subjective ratings. To improve its fidelity for objective physiological responses, I recalibrated it using the EEG-derived objective evaluation results. Vehicle acceleration and angular velocity data recorded by the IMU served as inputs to the 6DOF-SVC model. I then compared the model output with the normalised objective evaluation curve. The initial discrepancy indicated the need for parameter tuning. Therefore, I used a Genetic Algorithm (GA) to optimize several key parameters: \( K_{vc} \), \( \tau_L \), \( K_{\omega c} \), and \( K_{ac} \).
Each candidate solution was encoded as a vector of the four parameters. The fitness function was designed based on the time-domain error between the model output and the experimental objective curve. The error included rise time error, peak amplitude error, decay time error, and area error:
$$ E(\mathbf{X}) = w_u e_u(\mathbf{X}) + w_p e_p(\mathbf{X}) + w_d e_d(\mathbf{X}) + w_r e_r(\mathbf{X}) $$
and the fitness was defined as:
$$ f(\mathbf{X}) = \frac{1}{1+E(\mathbf{X})} $$
The GA used tournament selection, arithmetic crossover, and Gaussian mutation. The algorithm iterated until the maximum generation number was reached. The scatter plot of parameter combinations during iterations showed convergence to high-fitness regions. The final optimized parameters are listed in Table 3.
| Parameter | Symbol | Value |
|---|---|---|
| Vertical time constant | \(\tau\) | 5 s |
| Otolith adaptation time | \(\tau_a\) | 190 s |
| Canal time constant | \(\tau_d\) | 7 s |
| Integration time | \(\tau_I\) | 720 s |
| Second-order filter constant | \(\tau_L\) | 0.04 s |
| Acceleration gain | \(K_a\) | 0.1 |
| Angular velocity gain | \(K_\omega\) | 0.8 |
| Acceleration feedback | \(K_{ac}\) | 1.1 |
| Rotational feedback | \(K_{\omega c}\) | 4.8 |
| Vertical feedback | \(K_{vc}\) | 5.5 |
| Half-saturation | \(b\) | 0.5 m/s² |
| Hill coefficient | \(P\) | 0.85 |
After calibration, the model output matched the EEG-derived objective curve closely, especially in the main response interval and cumulative rise. This calibrated model was then utilized in the control strategy design as the prediction model for passenger motion sickness.
4. Vehicle Dynamics Modeling and Control Architecture
4.1 Longitudinal Dynamics
To simulate realistic vehicle behavior, I used the CarSim software as a high-fidelity plant. The vehicle was a C-class sedan with parameters summarized in Table 4. The longitudinal motion model is based on Newton’s second law:
$$ F_x = F_f + F_i + F_a + F_{air} $$
where the tractive force is:
$$ F_x = \frac{T_{tq} i_g i_0 \eta_t}{r} $$
and the resistances are given as follows:
$$ F_f = m g f \cos \theta $$
$$ F_i = m g \sin \theta $$
$$ F_a = \delta m \frac{dv}{dt} $$
$$ F_{air} = \frac{C_D A v^2}{21.15} $$
| Parameter | Symbol | Value |
|---|---|---|
| Track width front/rear | \(T_{wf}/T_{wr}\) | 1675 mm |
| Frontal area | \(A\) | 2.2 m² |
| Drag coefficient | \(C_D\) | 0.3 |
| Rolling resistance coefficient | \(f\) | 0.014 |
| Tire specification | – | 215/55 R17 |
| Max power | \(P_{max}\) | 180 kW |
| Max torque | \(T_{max}\) | 400 N·m |
| Rated speed | \(n_e\) | 4298 r/min |
4.2 Pitch Motion Model
Since pitch angle and pitch rate are strongly correlated with motion sickness, I included a simplified pitch model:
$$ I_{yy} \ddot{\varphi} = – \sum F_{zi} l_i + \sum F_{xi} h_i $$
where \( I_{yy} \) is the pitch moment of inertia, \( \varphi \) is the pitch angle, \( l_i \) are horizontal distances, and \( h_i \) are vertical distances from the center of gravity. The pitch dynamics were modeled in CarSim and linked to Simulink through interfaces.
4.3 Conventional Longitudinal Control
Before implementing anti-motion-sickness strategies, I established two baseline controllers. For the human-driven mode, a PID controller tracked the target velocity:
$$ e(t) = v_{ref}(t) – v(t) $$
$$ a_{des}(t) = K_P e(t) + K_I \int_0^t e(\tau) d\tau + K_D \frac{de(t)}{dt} $$
For the autonomous mode, a double PID structure (position loop + velocity loop) was implemented. The position error was:
$$ e_s(t) = s_{ref}(t) – s(t) $$
The velocity compensation was output by the position PID, and then the velocity error was formed. The final desired acceleration was generated by the velocity PID with feedforward of the planned acceleration.
The desired acceleration needed to be converted to throttle or braking commands. I obtained a calibration map by simulating the vehicle under different constant pedal openings. The map surfaces \( P_{acc} = f(v,a) \) and \( P_{brk} = g(v,a) \) were built. Gaussian smoothing was applied to remove noise. The final drive/brake calibration surface is shown in Figure 2 in my dissertation. This surface enabled accurate tracking of acceleration commands.
4.4 Why Electric Cars Cause More Motion Sickness
Electric motors have a much faster torque response (about 0.1–0.2 s) and a more linear torque rise than internal combustion engines (response time above 1.2 s, with complex stages). Such fast and linear torque response creates high jerk values at the beginning of acceleration, causing sudden pitching of the vehicle. This unexpected motion conflicts with the passenger’s internal prediction model. Therefore, I concluded that a dedicated anti-motion-sickness calibration of the pedal map is necessary for electric cars. Additionally, in autonomous driving, the lack of preview information amplifies the problem.
5. Anti-Motion-Sickness Drive Control Strategy for Human-Driven Electric Cars
5.1 Structure of the Proposed Control
In human-driven electric cars, the driver expects good acceleration when pressing the accelerator pedal. However, when passengers are susceptible to motion sickness, the drive torque should be suppressed. I designed a fuzzy logic controller that receives the accelerator pedal opening (ACC), its rate of change (DACC), and the real-time MSI from the calibrated 6DOF-SVC model. The output is a “motion sickness coefficient” \( K \). The torque load factor \( L \) is computed as:
$$ L = L_{eff} + K (L_{comfort} – L_{eff}) $$
where \( L_{eff} \) is the load factor from a hard (performance-oriented) pedal map, and \( L_{comfort} \) is from a soft (comfort-oriented) pedal map. The desired torque is then:
$$ T = K_p \cdot T_{max}(n) \cdot L $$
where \( K_p \) is a driver demand gain and \( T_{max}(n) \) is the motor maximum torque at speed \( n \). Table 5 lists the working points for the hard and soft pedal curves.
| Pedal opening (%) | Hard torque load factor | Soft torque load factor |
|---|---|---|
| 0 | 0 | 0 |
| 20 | 0.31 | 0.040 |
| 40 | 0.48 | 0.076 |
| 60 | 0.67 | 0.160 |
| 80 | 0.82 | 0.420 |
| 100 | 1.00 | 1.000 |
I constructed a three-dimensional surface for the anti-motion-sickness pedal control by interpolating between these two curves with the fuzzy output. The fuzzy controller uses the following input universes: ACC in [0,100], DACC in [0,100], MSI in [0,0.2] (or [0,1] normalized), and output \( K \) in [0,1]. Membership functions were triangular/trapezoidal. The fuzzy rules were formulated as:
- If ACC is low and DACC is high, then K is high (to suppress aggressive starts).
- If MSI is large, then K increases regardless of pedal behavior.
- If driving normally and MSI is low, then K is low to preserve driveability.
Table 6 presents a partial rule matrix for the MSI level “medium”.
| ACC \ DACC | VS | S | M | L | VL |
|---|---|---|---|---|---|
| VS | M | S | VS | VS | VS |
| S | M | M | S | S | VS |
| M | L | M | M | S | VS |
| L | VL | L | M | S | S |
| VL | VL | VL | L | M | S |
The control strategy was implemented in Simulink and co-simulated with CarSim. The real-time MSI was calculated by feeding the vehicle longitudinal acceleration and pitch rate into the 6DOF-SVC model. A sinusoidal acceleration scenario with a frequency of 0.2 Hz and amplitude of 2 m/s² was used as the nominal validation condition. The simulation duration was 1500 s.
5.2 Results of Fuzzy Anti-Motion-Sickness Control
Figure 4 shows the comparisons between the conventional PID control (labeled “Non-fuzzy”) and the fuzzy anti-motion-sickness control (labeled “Fuzzy”). The acceleration amplitude under fuzzy control was reduced, and the pitch angle was significantly suppressed. The velocity tracking error increased slightly, but the average speed remained near the reference. The jerk was also reduced at most peaks. The most important result is the MSI evolution: after 1500 s, the conventional control produced an MSI of 7.06%, while the fuzzy control reduced it to 5.63%, a relative reduction of 20.26%.
I further tested the fuzzy controller under different frequencies and amplitudes. With fixed acceleration amplitude 2 m/s² and frequencies from 0.1 Hz to 0.5 Hz, the performance is listed in Table 7.
| Frequency | Conventional MSI | Fuzzy MSI | Improvement |
|---|---|---|---|
| 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% |
When the frequency was fixed at 0.2 Hz and the amplitude varied from 1 to 3 m/s², the results are summarized in Table 8.
| Amplitude | Conventional MSI | Fuzzy MSI | Improvement |
|---|---|---|---|
| 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% |
From these tables, the fuzzy control is more effective at higher frequencies, while its effectiveness is roughly stable across the amplitude range tested. The averaged improvement over the tested frequencies was 31.97% and over the amplitudes was 20.07%.
6. MPC-Based Anti-Motion-Sickness Control for Autonomous Electric Cars
6.1 System Model for MPC
For fully autonomous electric cars, the driver is no longer in the control loop, so the controller must simultaneously track the planned speed and minimize passenger motion sickness. I designed a model predictive controller (MPC) that uses a combined vehicle-and-passenger state model. The continuous-time state vector includes vehicle position \( p \), velocity \( v \), pitch angle \( \theta \), pitch rate \( \omega \), and the motion sickness conflict states \( x_1, x_2, x_3, x_4, x_5 \). To reduce computational complexity, I simplified the state vector to:
$$ \mathbf{X} = [p, v, a, x, \dot{x}]^T $$
with \( \dot{x} \) being the derivative of the conflict variable. The nonlinear state equation is:
$$ \dot{\mathbf{X}} = f(\mathbf{X}, u) $$
where \( u \) is the desired acceleration command. Linearization around an operating point \( (\mathbf{X}_0, u_0) \) yields:
$$ \delta \dot{\mathbf{X}} = \mathbf{A} \delta \mathbf{X} + \mathbf{B} \delta u $$
with Jacobians \( \mathbf{A} = \frac{\partial f}{\partial \mathbf{X}} \), \( \mathbf{B} = \frac{\partial f}{\partial u} \). Discretizing with sampling time \( T_s \) gives:
$$ \mathbf{X}[k+1] = \mathbf{A}_d \mathbf{X}[k] + \mathbf{B}_d u[k] $$
where \( \mathbf{A}_d = e^{\mathbf{A} T_s} \), \( \mathbf{B}_d = \int_0^{T_s} e^{\mathbf{A}\tau} d\tau \mathbf{B} \).
6.2 Objective Function and Constraints
The cost function \( J \) is defined over a prediction horizon \( N_p \) and a control horizon \( N_c \):
$$ J = \sum_{k=0}^{N_p-1} \Big[ w_1 \big( v[k]-v_{ref}[k] \big)^2 + w_2 \big( a[k]-a_{ref}[k] \big)^2 + w_3 x[k]^2 + w_4 \big( u[k+1]-u[k] \big)^2 \Big] $$
Here, \( v_{ref} \) is the planned speed, \( a_{ref} \) is the planned acceleration, \( x[k] \) is the intermediate conflict variable, and the weighted squared control change penalizes jerk-like oscillations. Constraints were imposed on vehicle speed, acceleration, jerk, pitch angle, and control input:
$$ 0 \le v \le 30 \ \text{m/s}, \quad -5 \le a \le 3.5 \ \text{m/s}^2 $$
$$ -5 \le j \le 5 \ \text{m/s}^3, \quad -0.8^\circ \le \theta \le 0.8^\circ $$
$$ -3.5 \le u \le 3.5 \ \text{m/s}^2, \quad |\Delta u| \le 1 \ \text{m/s}^2 $$
To solve the optimization problem efficiently, I discretized the linear model and used quadratic programming at each sampling instant. The first element of the optimized control sequence was applied, and the horizon was shifted at every time step.
6.3 Simulation Results for Nominal Sinusoidal Excitation
The same 0.2 Hz, 2 m/s² sinusoidal acceleration scenario was used for comparison with the conventional controller (labeled “Non-MPC”). Figure 6 in my dissertation showed the responses. The MPC controller tracked the speed/acceleration reference with small delays, but it effectively reduced the peaks of pitch angle and pitch rate. The jerk profiles showed some oscillations near the peaks, but the overall amplitude was reduced. The MSI after 1500 s was 6.14% under MPC control versus 7.03% under conventional control, corresponding to a 12.66% reduction. This moderate improvement is expected because MPC balances tracking and comfort, whereas fuzzy control directly suppresses torque more aggressively.
6.4 Validation under a Mixed-Frequency Realistic Profile
A single sinusoidal signal does not fully represent real vehicle excitation. Therefore, I constructed a multi-sine profile by superimposing 50 sinusoidal components with frequencies in the range 0.01–0.5 Hz and random phases. The amplitudes were selected to ensure the total acceleration remained below 3 m/s². The resulting acceleration profile and its spectrum are shown in Figure 7 of my dissertation. The spectrum had energy concentrated below 0.5 Hz. Figure 8 compared the conventional and MPC controllers. The MPC controller slightly reduced the high-frequency (0.2–0.5 Hz) content in the output acceleration, while maintaining good speed tracking. The pitch rate was attenuated near extremal values. At the end of the 1500 s simulation, the conventional controller produced an MSI of 4.81%, whereas the MPC controller achieved an MSI of 3.40%, representing a relative reduction of 29.31%.
To quantify frequency dependence, I ran simulations at different sinusoidal frequencies with a fixed amplitude of 2 m/s², from 0.1 Hz to 0.5 Hz in steps of 0.01 Hz. The results are aggregated in Table 9.
| Quantity | Value |
|---|---|
| Conventional MSI range | 0.99% – 12.49% |
| MPC MSI range | 0.92% – 5.98% |
| Average relative reduction | 22.02% |
| Highest reduction (0.5 Hz) | ≈ 52.1% |
| Lowest reduction (0.1 Hz) | ≈ 7.2% |
The MPC strategy demonstrates increasing benefit at higher frequencies, which matches the frequency weighting of human motion sickness sensitivity. It also maintained robust tracking performance and respected all constraints during the simulations.
7. Comparative Discussion and Summary
I compared the fuzzy and MPC approaches with respect to MSI reduction, velocity tracking, and computational complexity. Table 10 provides a qualitative comparison.
| Aspect | Fuzzy control (human-driven) | MPC control (autonomous) |
|---|---|---|
| Target mode | Human-driven electric cars | Autonomous electric cars |
| Inputs | ACC, DACC, MSI | State vector including vehicle and MSI |
| Output | Torque load factor correction | Optimal acceleration command |
| Tracking performance | Moderately degraded | Nearly maintained |
| MSI reduction at 0.2 Hz | 20.26% | 12.66% |
| Average reduction in tests | ~28% | ~22% |
| Implementation complexity | Low | Moderate |
| Real-time feasibility | Excellent | Good with LTI approximation |
Both strategies reduced motion sickness incidence in intelligent electric cars. The fuzzy control is simpler and more aggressive in suppressing torque, while the MPC is more suitable for autonomous driving where accurate trajectory following is required. My comprehensive evaluation framework, using EEG signals and 6DOF-SVC model calibration, enabled a reliable prediction of MSI and allowed the control algorithms to actively respond to passenger discomfort.
8. Conclusions
My research established an integrated framework combining motion sickness modeling, physiological evaluation, and drive control optimization for intelligent electric cars. The main contributions are summarized as follows:
- I built a six-degree-of-freedom subjective vertical conflict model and calibrated it with a genetic algorithm using EEG-based objective motion sickness data. The calibrated model accurately described the temporal evolution of motion sickness under realistic vehicle excitation.
- I proposed a deep learning approach based on TSception for automatic motion sickness recognition from multichannel EEG signals. The model achieved a 91.7% test accuracy and proved more robust than standard CNNs or EEGNet.
- For human-driven electric cars, I designed a fuzzy anti-motion-sickness drive control strategy with a custom pedal-map control surface. Simulation results showed an MSI reduction of about 20–56% depending on acceleration frequency.
- For autonomous electric cars, I developed a model predictive controller that explicitly incorporates the calibrated motion sickness model into the prediction dynamics. The MPC reduced MSI by 12.7% under the nominal 0.2 Hz sinusoidal condition and by 29.3% under a mixed-frequency realistic profile, with only slight degradation in speed tracking.
These findings demonstrate that active drive control strategies based on physiological signals and theoretical motion sickness models are effective in improving passenger comfort in intelligent electric cars. Future research may extend this framework to incorporate lateral motion, individual sensitivity differences, and real-time in-vehicle physiological monitoring.