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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Research on a motion sickness prediction model for vehicle occupants based on vehicle dynamics parameter thresholds</title>
      <link>https://trid.trb.org/View/2686822</link>
      <description><![CDATA[The ride comfort of autonomous vehicles is affected by motion sickness. This study quantifies the thresholds of vehicle dynamics parameters that induce motion sickness in both curved and straight-road scenarios, and constructs a predictive model. The results show that on S-shaped curves, the lateral acceleration thresholds (Δay) for moderate motion sickness (M2) and severe motion sickness (M3) are 0.398 m/s² and 0.419 m/s², respectively, while the Z-axis angular velocity thresholds (Gyroz_mean) are 6.876°/s and 8.022°/s. In straight-road scenarios, the Δay thresholds for M2 and M3 are 0.394 m/s² and 0.648 m/s2² respectively, and the maximum longitudinal velocity (vx_max) reaches 13.961 m/s and 18.492 m/s. The proposed model achieves an accuracy of 86% for M2 and 82% for M3. Real-vehicle validation demonstrated that dynamically controlling vehicle motion states to maintain lateral acceleration, angular velocity, and longitudinal velocity below the specified thresholds reduced overall motion sickness risk by 39.7%.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:29:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686822</guid>
    </item>
    <item>
      <title>Acceleration or velocity? Exploring minimally disruptive visual motion cues for reducing motion sickness in passenger VR</title>
      <link>https://trid.trb.org/View/2684264</link>
      <description><![CDATA[Motion sickness is a common issue for passengers, where a sensory conflict between visual and physical motion elicits symptoms. This can be particularly problematic if VR headsets are used by passengers in moving vehicles. Commonly used matched motion cues, which visually represent vehicle velocity via in-car displays or VR headsets, can alleviate this conflict but may cause distraction from the task the user is trying to perform, such as working or watching a movie. Acceleration-based visual mitigations could be a good alternative as they present fewer motion cues. However, studies on their potential to cause distraction and their most suitable velocity/speed are lacking. Through an on-the-road study, we demonstrate that these cues reduce motion sickness as effectively as matched motion cues and provide the additional benefit of low distraction, allowing users to concentrate on non-driving related tasks. These findings offer new directions for motion sickness mitigation and highlight the potential of acceleration-based designs in addressing sensory mismatch.]]></description>
      <pubDate>Fri, 26 Jun 2026 08:41:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2684264</guid>
    </item>
    <item>
      <title>Research on Active Car Seats for Reducing Car Sickness: Focusing on the Directional Specificity of Human Acceleration Perception Intensity</title>
      <link>https://trid.trb.org/View/2684167</link>
      <description><![CDATA[This study investigates an active car seat designed to reduce car sickness by considering the directional specificity of human acceleration perception. A modified minivan with an active reclining seat was used to test different head tilt angles during braking. Results showed that tilting the head in the direction of the gravito-inertial force (GIF) significantly reduced car sickness symptoms compared to a neutral head position. However, considering the directional specificity of acceleration perception alone did not show a significant effect in reducing car sickness. Adjusting head posture based on acceleration perception can effectively mitigate car sickness in vehicles.]]></description>
      <pubDate>Thu, 25 Jun 2026 14:51:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2684167</guid>
    </item>
    <item>
      <title>Can seat suspensions mitigate motion sickness and enhance vibration comfort while being driven? A subjective assessment of the K-Seat</title>
      <link>https://trid.trb.org/View/2681559</link>
      <description><![CDATA[Prolonged exposure to whole-body vibration (WBV) is a key contributor to motion discomfort in vehicles, including motion sickness and ride comfort. This issue becomes more compelling in automated vehicles, where occupants are expected to frequently engage in non-driving-related activities and will expect high comfort levels. Hence, enhancing seat design to mitigate WBV is essential for improving ride comfort across vehicle types. Therefore, this study, which primarily addresses vertical accelerations, optimized an existing seat suspension (K-Seat) and subjectively assessed discomfort using 24 participants (13 males and 11 females) exposed to a 29-minute driving session. The experiment was conducted with a conventional Toyota Yaris seat in a driving simulator, where a K-Seat model was used to emulate the effect of the seat suspension. Thus we evaluated the K-Seat, which has shown great promise for attenuating low-frequency vibrations; however, it had never been tested on human participants. The results show an overall reduction of 50% in reported motion sickness using the motion illness symptoms classification scale (MISC). Subjective discomfort was also alleviated for head and upper back. In addition, perceived discomfort was analyzed based on gender, illustrating a greater effectiveness of the K-Seat in enhancing lower neck comfort for females than for males.]]></description>
      <pubDate>Thu, 18 Jun 2026 08:54:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681559</guid>
    </item>
    <item>
      <title>Anticipatory vibrotactile cues about upcoming turns reduce motion sickness: A study with car passengers on public roads</title>
      <link>https://trid.trb.org/View/2681473</link>
      <description><![CDATA[The introduction of automated driving brings improvements towards comfort and enables all passengers to use their time more efficiently. However, the more the occupant engages in tasks unrelated to driving, the risk of massive discomfort caused by motion sickness or more specifically carsickness, increases. The development of carsickness includes symptoms such as dizziness or nausea and depends, among other factors, on the occupants’ ability to anticipate upcoming vehicle movements. The present study investigated whether anticipatory vibrotactile cues, focused exclusively on lateral maneuvers (turns) and tested in real-world driving conditions, can reduce passengers' carsickness. In a counterbalanced within-subjects design, 40 participants experienced two 30-min rides on public roads. During the ride, all participants watched a movie and were asked every minute about their current subjective carsickness level, using the Fast Motion Sickness Scale (FMS). In the intervention condition, upcoming right and left turns were announced 1 s in advance by cues via a vibrotactile belt. In the control condition, passengers merely wore the belt but did not receive such cues. We found that anticipatory vibrotactile cues about upcoming right and left turns have a mitigating effect on the level of carsickness.]]></description>
      <pubDate>Thu, 18 Jun 2026 08:54:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681473</guid>
    </item>
    <item>
      <title>Effects of anticipatory auditory cues and non-driving related tasks on motion sickness in automated vehicles</title>
      <link>https://trid.trb.org/View/2670146</link>
      <description><![CDATA[In the context of highly automated vehicles, motion comfort deserves crucial attention for their widespread adoption. Passengers of automated vehicles are prone to motion sickness from diminished cognizance of the driving environment, and from engaging in non-driving related tasks (NDRTs). Awareness about upcoming motion and mental distraction from NDRTs have the potential to mitigate motion sickness. The current study used a motion-based driving simulator where 31 participants performed easy and hard versions of an NDRT in the presence and absence of spatialized anticipatory auditory cues displayed 3 s prior to lateral accelerations over a 20-min drive in a fully automated vehicle. Results suggested that anticipatory cues may only be effective when displayed selectively on an as-needed basis for both lateral and longitudinal accelerations. Sufficient cue-motion association internalization through training or continued exposure may also be required for anticipatory cues to be effective. Further, regardless of the cognitive demand of the NDRT, some form of mental distraction has the potential to suppress motion sickness.]]></description>
      <pubDate>Fri, 29 May 2026 08:59:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2670146</guid>
    </item>
    <item>
      <title>Efficient Motion Sickness Assessment: Recreation of On-Road Driving on a Compact Test Track</title>
      <link>https://trid.trb.org/View/2659043</link>
      <description><![CDATA[The ability to engage in other activities during the ride is considered by consumers as one of the key reasons for the adoption of automated vehicles. However, engagement in non-driving activities will provoke occupants’ motion sickness, deteriorating their overall comfort and thereby risking acceptance of automated driving. Therefore, it is critical to extend our understanding of motion sickness and unravel the modulating factors that affect it through experiments with participants. Currently, most experiments are conducted on public roads (realistic but not reproducible) or test tracks (feasible with prototype automated vehicles). This research study develops a method to design an optimal path and speed reference to accurately replicate on-road motion sickness exposure on a small test track. The method uses model predictive control to replicate the longitudinal and lateral accelerations collected from on-road drives on a test track of 70 m by 175 m. A within-subject experiment (47 participants) was conducted comparing the occupants’ motion sickness occurrence in test-track and on-road conditions, with the conditions being cross-randomized. The results illustrate that the subjective (reported) motion sickness is well reproduced with an insignificant reduction on the track. Meanwhile, there is an overall correspondence of individual sickness levels between on-road and test-track. This paves the path for the employment of our method for a simpler, safer and more replicable assessment of motion sickness.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659043</guid>
    </item>
    <item>
      <title>Quantifying Motion Sickness in Virtual Reality Using a Multimodal 1CNN–GRU–Attention Approach With GSR Data</title>
      <link>https://trid.trb.org/View/2658989</link>
      <description><![CDATA[Cybersickness remains a significant barrier to the widespread adoption of virtual reality (VR) and enhancing passenger comfort and safety in autonomous vehicles (AVs). Predicting and mitigating cybersickness is crucial for creating safer and more comfortable VR experiences. This study introduces a one-dimensional convolutional neural network-gated recurrent unit-attention (1CNN–GRU–Attention) model trained on galvanic skin response (GSR) data collected from 24 participants in VR under various weather conditions. Participants reported cybersickness severity on a 10-point scale every minute, providing real-time labels for evaluation. The model’s performance was compared with that of k-nearest neighbours (KNN) and support vector machine (SVM) classifiers in binary (two-class) and multi-class (four-class: none, low, acute, high) classification settings, both with and without class balancing using the synthetic minority oversampling technique (SMOTE). Evaluation metrics included accuracy (ACC), Matthews correlation coefficient (MCC), and unified performance metric (UPM), with performance assessed via 5-fold cross-validation and statistical t-tests. Results show that the 1CNN–GRU–Attention model significantly outperforms both SVM and KNN across all metrics. Applying SMOTE improved binary classification accuracy from 75.15% to 86.94% in binary classification and from 48.77% to 64.63% in multiclass classification, with notable improvements in MCC. These findings highlight the importance of balanced data and the effectiveness of the 1CNN–GRU–Attention model in assessing cybersickness severity, advancing physiological monitoring methods for VR, and facilitating broader VR adoption.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658989</guid>
    </item>
    <item>
      <title>A Study on Autonomous Driving Motion Sickness from the Perspective of Multimodal Human Signals</title>
      <link>https://trid.trb.org/View/2691590</link>
      <description><![CDATA[In autonomous driving, motion sickness (MS) arises from physical or visual stimuli, or a combination of both. However, objective quantification of MS level (MSL) remains limited beyond questionnaire-based assessments. Using multimodal human signals (physiological and behavioral) collected in an autonomous driving simulator, this study addresses the association between these signals and MSL, across these MS types, by (i) screening and curating a decade of human-signal MS studies (HS-Set) to establish a data-driven foundation for selecting target sensor domains and features, (ii) constructing a dataset with subjective measures of MSL (fast motion sickness scale and simulator sickness questionnaire (SSQ)), alongside human signals (electroencephalogram (EEG), photoplethysmogram (PPG), electrodermal activity (EDA), skin temperature, and head/eye movement), (iii) conducting a correlation analysis between MSL and the identified features from HS-Set, and (iv) quantifying multivariable contributions at the feature and sensor domains through an explainable boosting machine (EBM). Key correlations include head amplitude/energy (pitch/surge) with SSQ total/oculomotor, eye entropy with nausea/oculomotor (positive), and EDA with nausea (negative). The EBM-based contribution analysis highlights EEG connectivity and head kinematics as dominant contributors; excluding EEG, the interpretability of single-domain models remains limited. Additionally, a combination of Head, PPG, and EDA domains retains over 80% of the full model's interpretability.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691590</guid>
    </item>
    <item>
      <title>Ideal comfort ellipses and comfort dynamics model for mitigating motion sickness in battery electric vehicle</title>
      <link>https://trid.trb.org/View/2659290</link>
      <description><![CDATA[Due to the rapid response and high torque output of the electric motor, drivers and passengers in battery electric vehicles are more prone to dizziness. This is particularly challenging for mitigating motion sickness, as acceleration performance conflicts with ride comfort. In this article, we develop ideal comfort ellipses based on various driving cycles, which show a comfort zone for driving. A comfort dynamics model is formulated using acceleration, which is then applied to design a model predictive controller. In addition, the motion sickness dose value is used as an indicator to evaluate the effectiveness of the control strategy. The proposed approach is validated on a battery electric vehicle under the Economic Commission for Europe driving cycle, Worldwide harmonised Light-duty vehicles Test Cycle and linear driving condition. The results indicate that, compared to scenarios without the control strategy, this approach reduces the motion sickness dose value by 20.34%, 34.67%, and 24.11%, respectively, enhancing ride comfort and mitigating motion sickness.]]></description>
      <pubDate>Thu, 16 Apr 2026 13:54:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659290</guid>
    </item>
    <item>
      <title>Motion sickness in autonomous driving: Environmental, individual, and time effects</title>
      <link>https://trid.trb.org/View/2679260</link>
      <description><![CDATA[With the rise of electrification and autonomous driving, prolonged exposure to low-frequency vehicle motion has become an environmental health concern. Motion sickness is often triggered in such environments, hindering the wider adoption of autonomous vehicles. However, limited understanding of its mechanisms challenges effective intervention. This study explores the time-varying progression of motion sickness and influencing factors under real-world conditions. Three on-road experiments with varied driving intervals were conducted in no-task and video task. Subjective ratings (0–9) were continuously recorded, and a new vehicle motion parameter – stimulus intensity – was developed to quantify its impact on comfort. Individual-level analysis using a Cumulative Link Mixed-effects Model (CLMM) revealed distinct response patterns capturing key features of symptom progression, and a saturation effect induced by rapid escalation reduces the marginal impact of environmental factors. These findings enhance understanding of how motion-related environmental stimuli affect human health and support personalized prediction and mitigation strategies in autonomous driving contexts.]]></description>
      <pubDate>Wed, 08 Apr 2026 13:40:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2679260</guid>
    </item>
    <item>
      <title>On-road evaluation of sickness-less motion planning in automated vehicles: ‘Head motion first’ helps!</title>
      <link>https://trid.trb.org/View/2652433</link>
      <description><![CDATA[With the rapid advancement of automated driving, motion sickness (MS) has gathered significant attention from both academia and industry. Advanced sensing, decision-making, and control systems in autonomous vehicles offer promising opportunities to mitigate MS in next-generation transportation. Traditionally, objective metrics such as Motion Sickness Dose Value (MSDV) and Motion Sickness Incidence (MSI) are derived directly from vehicle motion states to quantify MS severity. However, these metrics often overlook the filtering or amplification effects of human body dynamics on the motion stimuli experienced in occupant heads. To address this, we designed two motion planning algorithms to generate vehicle trajectories: one optimized solely based on vehicle motion (control group) and another incorporating occupant head motion dynamics (experimental group). Real-road experiments with 23 participants, using a within-subject design, were conducted on an automated vehicle. Results demonstrated that trajectories with ‘head-motion-first’ concept can significantly reduce MSDV, with subjective assessments via the Misery Scale (MISC) showing notable reductions in MS severity. This study represents one of the few occupant-in-the-loop on-road validations of MS mitigation through automated driving, confirming the effectiveness of incorporating head motion dynamics into MS-oriented trajectory planning.]]></description>
      <pubDate>Tue, 31 Mar 2026 16:35:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652433</guid>
    </item>
    <item>
      <title>Flowing Landscapes: A Motion-Synced In-Car VR Mindfulness Experience with Biofeedback-Driven Adjustments for Motion Sickness</title>
      <link>https://trid.trb.org/View/2580270</link>
      <description><![CDATA[Autonomous driving increases the risk of motion sickness. Flowing Landscapes takes passengers on a mindfulness virtual journey through the beauty of traditional Chinese landscapes, integrating peaceful landscape aesthetics with mindfulness techniques to reduce motion sickness during travel. The system incorporates an adaptive mechanism that responds to physiological signals, dynamically adjusting visual stimuli to further minimize discomfort. We tested 4 conditions by varying input adjustments and collected psychological and physiological data from the participants (N = 20). The experimental results indicate that biofeedback reduces motion sickness, while the landscape meditation environment enhances user immersion and pleasure. This study highlights the potential of combining cultural aesthetics, mindfulness, and adaptive features to create a more comfortable in-car VR experience, with future work focusing on further personalization based on individual biofeedback.]]></description>
      <pubDate>Tue, 24 Mar 2026 13:08:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2580270</guid>
    </item>
    <item>
      <title>Activities that Correlate with Motion Sickness in Driving Cars – An International Online Survey</title>
      <link>https://trid.trb.org/View/2581698</link>
      <description><![CDATA[Up to 2 out of 3 passengers suffer from motion sickness, caused by non-driving related activities. Occupant monitoring systems detect such activities via cameras in the vehicle interior and hence can be used to warn passengers or to assist them. An international online survey in Germany, USA, China, India, Turkey and Mexico was conducted in order to identify activities that correlate with motion sickness. The results identify reading, using a device, watching a movie and turning in the seat to be the most relevant activities for occupant monitoring systems to detect and hence for motion sickness assistance systems to address.]]></description>
      <pubDate>Wed, 18 Mar 2026 09:00:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2581698</guid>
    </item>
    <item>
      <title>Reduced Motion Sickness Using Vestibular EEG-Guided tACS Under Mismatched Physical Rotation and VR Visual Motion</title>
      <link>https://trid.trb.org/View/2591257</link>
      <description><![CDATA[The increasing use of virtual reality (VR) in public transportation enables travelers to engage in immersive entertainment or productive tasks, thereby enhancing the overall travel experience. However, mismatched VR visual motion and physical car motion can cause motion sickness (MS), leading to nausea, postural instability and reduced time for enjoyment or productive tasks. Thus, the benefits of using VR in transportation systems are currently limited to individuals who do not experience MS. Thus, effective MS mitigation is essential for improving travel safety, maximizing travel time, and expanding VR accessibility. Neuromodulation targeting the vestibular apparatus, such as bone-conducted vibration (BCV), has shown promise in reducing MS. However, it remains unclear whether neuromodulation directly targeting vestibular cortical regions, such as transcranial alternating current stimulation (tACS), is superior. This paper focuses on the design and validation of a novel vestibular cortical neuromodulation approach using tACS in mitigating MS in a novel simulated in-car VR environment. Eighty participants were recruited to evaluate the tACS approach. The results demonstrate that the proposed tACS approach effectively reduces nausea, enhances postural stability, and extends survival time in MS. Compared to BCV, tACS demonstrates a faster onset of action and longer mitigation effects. However, from an applied perspective, the effects of our tACS approach were relatively short-lived and accompanied by side effects such as tingling and itching.]]></description>
      <pubDate>Wed, 04 Mar 2026 09:17:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591257</guid>
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