Do Electric Vehicles Induce More Motion Sickness Than Fuel Vehicles? A Survey Study in China
- URL: http://arxiv.org/abs/2506.22674v1
- Date: Fri, 27 Jun 2025 22:55:55 GMT
- Title: Do Electric Vehicles Induce More Motion Sickness Than Fuel Vehicles? A Survey Study in China
- Authors: Weiyin Xie, Chunxi Huang, Jiyao Wang, Dengbo He,
- Abstract summary: Electric vehicles (EVs) are a promising alternative to fuel vehicles (FVs)<n>The increasing prevalence of EVs is accompanied by widespread complaints regarding the high likelihood of motion sickness (MS) induction.<n>This study aims to investigate the existence of EV-induced MS and explore the potential factors leading to it.
- Score: 0.18749305679160366
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Electric vehicles (EVs) are a promising alternative to fuel vehicles (FVs), given some unique characteristics of EVs, for example, the low air pollution and maintenance cost. However, the increasing prevalence of EVs is accompanied by widespread complaints regarding the high likelihood of motion sickness (MS) induction, especially when compared to FVs, which has become one of the major obstacles to the acceptance and popularity of EVs. Despite the prevalence of such complaints online and among EV users, the association between vehicle type (i.e., EV versus FV) and MS prevalence and severity has not been quantified. Thus, this study aims to investigate the existence of EV-induced MS and explore the potential factors leading to it. A survey study was conducted to collect passengers' MS experience in EVs and FVs in the past one year. In total, 639 valid responses were collected from mainland China. The results show that FVs were associated with a higher frequency of MS, while EVs were found to induce more severe MS symptoms. Further, we found that passengers' MS severity was associated with individual differences (i.e., age, gender, sleep habits, susceptibility to motion-induced MS), in-vehicle activities (i.e., chatting with others and watching in-vehicle displays), and road conditions (i.e., congestion and slope), while the MS frequency was associated with the vehicle ownership and riding frequency. The results from this study can guide the directions of future empirical studies that aim to quantify the inducers of MS in EVs and FVs, as well as the optimization of EVs to reduce MS.
Related papers
- Overtake Detection in Trucks Using CAN Bus Signals: A Comparative Study of Machine Learning Methods [51.28632782308621]
We focus on overtake detection using Controller Area Network (CAN) bus data collected from five in-service trucks provided by the Volvo Group.<n>We evaluate three common classifiers for vehicle manoeuvre detection, Artificial Neural Networks (ANN), Random Forest (RF), and Support Vector Machines (SVM)<n>Our pertruck analysis also reveals that classification accuracy, especially for overtakes, depends on the amount of training data per vehicle.
arXiv Detail & Related papers (2025-07-01T09:20:41Z) - Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives [56.528835143531694]
We introduce DriveBench, a benchmark dataset designed to evaluate Vision-Language Models (VLMs)<n>Our findings reveal that VLMs often generate plausible responses derived from general knowledge or textual cues rather than true visual grounding.<n>We propose refined evaluation metrics that prioritize robust visual grounding and multi-modal understanding.
arXiv Detail & Related papers (2025-01-07T18:59:55Z) - Hybrid-Generative Diffusion Models for Attack-Oriented Twin Migration in Vehicular Metaverses [58.264499654343226]
Vehicle Twins (VTs) are digital twins that provide immersive virtual services for Vehicular Metaverse Users (VMUs)
High mobility of vehicles, uneven deployment of edge servers, and potential security threats pose challenges to achieving efficient and reliable VT migrations.
We propose a secure and reliable VT migration framework in vehicular metaverses.
arXiv Detail & Related papers (2024-07-05T11:11:33Z) - Contingency Analysis of a Grid of Connected EVs for Primary Frequency
Control of an Industrial Microgrid Using Efficient Control Scheme [0.0]
Electric vehicles (EVs) can operate as both a load and a source.
Industrial Microgrids are made up of different energy sources such as wind farms and PV farms, storage systems, and loads.
A proposed control scheme for frequency management is presented.
arXiv Detail & Related papers (2024-02-02T18:14:16Z) - EnduRL: Enhancing Safety, Stability, and Efficiency of Mixed Traffic Under Real-World Perturbations Via Reinforcement Learning [1.7273380623090846]
We analyze real-world driving trajectories and extract a wide range of acceleration profiles.
We then incorporates these profiles into simulations for training RVs to mitigate congestion.
Our RVs demonstrate significant improvements: safety by up to 66%, efficiency by up to 54%, and stability by up to 97%.
arXiv Detail & Related papers (2023-11-21T00:45:13Z) - A Counterfactual Safety Margin Perspective on the Scoring of Autonomous
Vehicles' Riskiness [52.27309191283943]
This paper presents a data-driven framework for assessing the risk of different AVs' behaviors.
We propose the notion of counterfactual safety margin, which represents the minimum deviation from nominal behavior that could cause a collision.
arXiv Detail & Related papers (2023-08-02T09:48:08Z) - Learning energy-efficient driving behaviors by imitating experts [75.12960180185105]
This paper examines the role of imitation learning in bridging the gap between control strategies and realistic limitations in communication and sensing.
We show that imitation learning can succeed in deriving policies that, if adopted by 5% of vehicles, may boost the energy-efficiency of networks with varying traffic conditions by 15% using only local observations.
arXiv Detail & Related papers (2022-06-28T17:08:31Z) - Driving and charging an EV in Australia: A real-world analysis [0.0]
This study aims to collect data on real-world driving and charging patterns of 239 EVs across Australia.
Data collection from current EV owners via an application programming interface platform began in November 2021 and is currently live.
arXiv Detail & Related papers (2022-06-03T11:01:23Z) - Investigating the Spatiotemporal Charging Demand and Travel Behavior of
Electric Vehicles Using GPS Data: A Machine Learning Approach [1.160208922584163]
Electric vehicles (EVs) may change the travel behavior of drivers and pose a significant electricity demand on the power system.
Since the electricity demand depends on the travel behavior of EVs, the forecasting of daily charging demand (CD) will be a challenging task.
In this paper, we use the recorded GPS data of EVs and conventional gasoline-powered vehicles from the same city to investigate the potential shift in the travel behavior of drivers.
arXiv Detail & Related papers (2022-02-28T23:11:30Z) - An Energy Consumption Model for Electrical Vehicle Networks via Extended
Federated-learning [50.85048976506701]
This paper proposes a novel solution to range anxiety based on a federated-learning model.
It is capable of estimating battery consumption and providing energy-efficient route planning for vehicle networks.
arXiv Detail & Related papers (2021-11-13T15:03:44Z) - Accelerating the Adoption of Disruptive Technologies: The Impact of
COVID-19 on Intentions to Use Autonomous Vehicles [0.0]
This study examines the impact of the COVID-19 pandemic on willingness to adopt the emerging technology of autonomous vehicles.
Results reveal that the COVID-19 pandemic has a positive and highly significant impact on the consideration of using autonomous vehicles.
arXiv Detail & Related papers (2021-08-03T16:35:38Z) - A Case Study to Identify the Hindrances to Widespread Adoption of
Electric Vehicles in Qatar [0.0]
The adoption of electric vehicles (EVs) have proven to be a crucial factor to decreasing the emission of greenhouse gases (GHG) into the atmosphere.
This article reports the public perception of EV-adoption using statistical analyses and proposes some recommendations for improving EV-adoption in Qatar.
arXiv Detail & Related papers (2020-06-27T18:56:46Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.