Model to predict motion sickness within autonomous vehicles

2019 ◽  
Vol 234 (5) ◽  
pp. 1330-1345
Author(s):  
Spencer Salter ◽  
Cyriel Diels ◽  
Paul Herriotts ◽  
Stratis Kanarachos ◽  
Doug Thake
Keyword(s):  
Motion Sickness ◽  
Autonomous Vehicles ◽  
Systems Approach ◽  
Autonomous Vehicle ◽  
Human Motion ◽  
Independent Study ◽  
Vehicle Design ◽  
Specific Method ◽  
Motion Vision ◽  
Vehicle Motion

Background: Motion sickness is common within most forms of transport; it affects most of the population who experience varied symptoms at some stage in their lives. Thus far, there has been no specific method to quantify the predicted levels of motion sickness for a given vehicle design, task and route. Objective: To develop a motion sickness virtual prediction tool that includes the following inputs: human motion, vision, vehicle motion, occupant task and vehicle design. Method: A time domain analysis using a multi-body systems approach has been developed to provide the raw data for post-processing of vehicle motion, occupant motion and vision, based on a virtual route designed to provoke motion sickness, while the digital occupant undertakes a specific non-driving related task. Results: Predicted motion sickness levels are shared for a simple positional sweep of a vehicle cabin due to a prescribed motion and task. Two additional examples are shared within this study; first, it was found that the model can predict the difference found between sitting forwards and backwards in an autonomous vehicle. Second, analysis of a respected and independent study into auxiliary display height shows that the model can predict both relative and absolute levels between the two display heights congruent to the original physical experiment. Conclusion: It has been shown that the tool has been successful in predicting motion sickness in autonomous vehicles and is therefore of great use in guiding new future mobility solutions in the ability to tune vehicle dynamics and control alongside vision and design attributes.

scholarly journals Research on the Comfort of Vehicle Passengers Considering the Vehicle Motion State and Passenger Physiological Characteristics: Improving the Passenger Comfort of Autonomous Vehicles

2020 ◽  
Vol 17 (18) ◽  
pp. 6821
Author(s):  
Chang Wang ◽  
Xia Zhao ◽  
Rui Fu ◽  
Zhen Li
Keyword(s):  
Prediction Model ◽  
Autonomous Vehicles ◽  
Control Algorithm ◽  
Short Term Memory ◽  
Autonomous Vehicle ◽  
Physiological Characteristics ◽  
Binary Logistic Regression Analysis ◽  
Analysis Model ◽  
Passenger Comfort ◽  
Vehicle Motion

Comfort is a significant factor that affects passengers’ choice of autonomous vehicles. The comfort of an autonomous vehicle is largely determined by its control algorithm. Therefore, if the comfort of passengers can be predicted based on factors that affect comfort and the control algorithm can be adjusted, it can be beneficial to improve the comfort of autonomous vehicles. In view of this, in the present study, a human-driven experiment was carried out to simulate the typical driving state of a future autonomous vehicle. In the experiment, vehicle motion parameters and the comfort evaluation results of passengers with different physiological characteristics were collected. A single-factor analysis method and binary logistic regression analysis model were used to determine the factors that affect the evaluation results of passenger comfort. A passenger comfort prediction model was established based on the bidirectional long short-term memory network model. The results demonstrate that the accuracy of the passenger comfort prediction model reached 84%, which can provide a theoretical basis for the adjustment of the control algorithm and path trajectory of autonomous vehicles.

SwiVR-Car-Seat

2021 ◽  
Vol 5 (4) ◽  
pp. 1-26
Author(s):  
Mark Colley ◽  
Pascal Jansen ◽  
Enrico Rukzio ◽  
Jan Gugenheimer
Keyword(s):  
Interface Design ◽  
Autonomous Vehicles ◽  
User Study ◽  
Low Cost ◽  
Autonomous Vehicle ◽  
User Input ◽  
The Road ◽  
Car Seat ◽  
Perceived Realism ◽  
Vehicle Motion

Autonomous vehicles provide new input modalities to improve interaction with in-vehicle information systems. However, due to the road and driving conditions, the user input can be perturbed, resulting in reduced interaction quality. One challenge is assessing the vehicle motion effects on the interaction without an expensive high-fidelity simulator or a real vehicle. This work presents SwiVR-Car-Seat, a low-cost swivel seat to simulate vehicle motion using rotation. In an exploratory user study (N=18), participants sat in a virtual autonomous vehicle and performed interaction tasks using the input modalities touch, gesture, gaze, or speech. Results show that the simulation increased the perceived realism of vehicle motion in virtual reality and the feeling of presence. Task performance was not influenced uniformly across modalities; gesture and gaze were negatively affected while there was little impact on touch and speech. The findings can advise automotive user interface design to mitigate the adverse effects of vehicle motion on the interaction.

Vehicle motion and motion sickness in pigs

1998 ◽  
Vol 66 (1) ◽  
pp. 239-245 ◽  
Author(s):  
J. M. Randall ◽  
R. H. Bradshaw
Keyword(s):  
Motion Sickness ◽  
Predictive Models ◽  
Low Frequency ◽  
Vertical Axis ◽  
Human Motion ◽  
Human Model ◽  
Road Vehicles ◽  
Low Frequencies ◽  
Vehicle Motion ◽  
Motion Characteristics

AbstractLow frequency oscillatory motion (0·05 to 0·5 Hz) experienced in ships and road vehicles is known to cause motion sickness in humans and some predictive models are available. There have been very few studies of the incidence of motion sickness in pigs and none which has attempted to identify the frequencies of motion of transporters which are likely to be implicated. In this study, the vibration and motion characteristics of a commercial pig transporter were measured while seven individually penned 40-kg pigs were transported for short (100 min) journeys and 80-kg pigs penned in groups of 12 or 13 were transported for longer (4·5 h) journeys. Direct behavioural observations were made of individual pigs for symptoms of travel sickness (sniffing, foaming at the mouth, chomping, and retching or vomiting). A comparison was then made between the incidence of travel sickness in pigs and that expected in humans given the measured vehicle vibration characteristics. The low frequencies of motion measured on the transporter (0·01 to 0·2 Hz) were well within the range implicated in human motion sickness with considerable power in the longitudinal and lateral axes but little in the vertical axis. On both short and long journeys pigs exhibited symptoms of travel sickness. The likely incidence of travel sickness on the short journeys predicted by the human model was 24 to 31% which corresponds to approximately two of the seven 40-kg pigs becoming travel sick. The numbers observed were generally lower than this although the same pigs were transported twice each day for 2 days and this may have therefore reflected the effects of habituation. The incidence of travel sickness on the long journeys predicted by the human model was 34%. During these journeys which involved four groups of 80-kg pigs which were not repeatedly transported, 26% of pigs vomited or retched (13 out of 50) while 50% showed advanced symptoms of foaming and chomping. These results are not inconsistent with the human model which should form the basis offurther research.

Autonomous Vehicles and Gender

Transfers ◽  
2018 ◽  
Vol 8 (1) ◽  
pp. 104-111 ◽  
Author(s):  
Ehren Helmut Pflugfelder
Keyword(s):  
Autonomous Vehicles ◽  
Special Section ◽  
Autonomous Vehicle ◽  
Vehicle Design ◽  
Material Rhetoric ◽  
Men And Women ◽  
Gender Divisions ◽  
And Gender ◽  
Autonomous Technology ◽  
Limited Usefulness

Several recent surveys report a gap between how men and women feel about autonomous vehicles. While such binaries may have limited usefulness, female respondents rank autonomous technology as less trustworthy and are less likely than men to report feeling safe in an autonomous car. This comment frames such results within the articles for this special section on autonomous vehicles, showing how reported gender divisions are resultant from discursive formations that frame user experience and individual performed experiences. These discursive-material dynamics generate persuasive configurations of power that thoughtful research and action in autonomous vehicle development could help mitigate. After summarizing survey diff erences, this comment off ers a brief commentary on how they might be addressed, focusing on material rhetoric and vehicle design.

scholarly journals Design of a Low-complexity Graph-Based Motion-Planning Algorithm for Autonomous Vehicles

2020 ◽  
Vol 10 (21) ◽  
pp. 7716
Author(s):  
Tamás Hegedűs ◽  
Balázs Németh ◽  
Péter Gáspár
Keyword(s):  
Motion Planning ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Low Complexity ◽  
The Real ◽  
Time Motion ◽  
Planning Algorithm ◽  
Vehicle Motion ◽  
Vehicle Trajectory

In the development of autonomous vehicles, the design of real-time motion-planning is a crucial problem. The computation of the vehicle trajectory requires the consideration of safety, dynamic and comfort aspects. Moreover, the prediction of the vehicle motion in the surroundings and the real-time planning of the autonomous vehicle trajectory can be complex tasks. The goal of this paper is to present low-complexity motion-planning for overtaking scenarios in parallel traffic. The developed method is based on the generation of a graph, which contains feasible vehicle trajectories. The reduction of the complexity in the real-time computation is achieved through the reduction of the graph with clustering. In the motion-planning algorithm, the predicted motion of the surrounding vehicles is taken into consideration. The prediction algorithm is based on density functions of the surrounding vehicle motion, which are developed through real measurements. The resulted motion-planning algorithm is able to guarantee a safe and comfortable trajectory for the autonomous vehicle. The effectiveness of the method is illustrated through simulation examples using a high-fidelity vehicle dynamic simulator.

Vision-Based Navigation of Autonomous Vehicles in Roadway Environments with Unexpected Hazards

2019 ◽  
Vol 2673 (12) ◽  
pp. 494-507 ◽  
Author(s):  
Mhafuzul Islam ◽  
Mashrur Chowdhury ◽  
Hongda Li ◽  
Hongxin Hu
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Semantic Segmentation ◽  
Steering Wheel ◽  
Potential Hazard ◽  
Driving System ◽  
Hazardous Object ◽  
Vision Based Navigation ◽  
Navigational System

Vision-based navigation of autonomous vehicles primarily depends on the deep neural network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras, and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment. Typically, these DNN-based systems in the autonomous vehicle are trained through supervised learning; however, recent studies show that a trained DNN-based system can be compromised by perturbation or adverse inputs. Similarly, this perturbation can be introduced into the DNN-based systems of autonomous vehicles by unexpected roadway hazards, such as debris or roadblocks. In this study, we first introduce a hazardous roadway environment that can compromise the DNN-based navigational system of an autonomous vehicle, and produce an incorrect steering wheel angle, which could cause crashes resulting in fatality or injury. Then, we develop a DNN-based autonomous vehicle driving system using object detection and semantic segmentation to mitigate the adverse effect of this type of hazard, which helps the autonomous vehicle to navigate safely around such hazards. We find that our developed DNN-based autonomous vehicle driving system, including hazardous object detection and semantic segmentation, improves the navigational ability of an autonomous vehicle to avoid a potential hazard by 21% compared with the traditional DNN-based autonomous vehicle driving system.

The human-like trajectory planning for autonomous vehicles based on optimal control in a test track environment

2021 ◽  
pp. 095440702110090
Author(s):  
Xing Xu ◽  
Minglei Li ◽  
Feng Wang ◽  
Ju Xie ◽  
Xiaohan Wu ◽  
...  
Keyword(s):  
Optimal Control ◽  
Autonomous Vehicles ◽  
Optimal Trajectory ◽  
Control Method ◽  
Autonomous Vehicle ◽  
Trajectory Data ◽  
Test Track ◽  
Optimal Control Method ◽  
The Future ◽  
On The Road

A human-like trajectory could give a safe and comfortable feeling for the occupants in an autonomous vehicle especially in corners. The research of this paper focuses on planning a human-like trajectory along a section road on a test track using optimal control method that could reflect natural driving behaviour considering the sense of natural and comfortable for the passengers, which could improve the acceptability of driverless vehicles in the future. A mass point vehicle dynamic model is modelled in the curvilinear coordinate system, then an optimal trajectory is generated by using an optimal control method. The optimal control problem is formulated and then solved by using the Matlab tool GPOPS-II. Trials are carried out on a test track, and the tested data are collected and processed, then the trajectory data in different corners are obtained. Different TLCs calculations are derived and applied to different track sections. After that, the human driver’s trajectories and the optimal line are compared to see the correlation using TLC methods. The results show that the optimal trajectory shows a similar trend with human’s trajectories to some extent when driving through a corner although it is not so perfectly aligned with the tested trajectories, which could conform with people’s driving intuition and improve the occupants’ comfort when driving in a corner. This could improve the acceptability of AVs in the automotive market in the future. The driver tends to move to the outside of the lane gradually after passing the apex when driving in corners on the road with hard-lines on both sides.

scholarly journals Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2244
Author(s):  
S. M. Yang ◽  
Y. A. Lin
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Random Trees ◽  
Average Deviation ◽  
Lane Changes ◽  
Path Smoothing ◽  
Rapidly Exploring Random Trees

Safe path planning for obstacle avoidance in autonomous vehicles has been developed. Based on the Rapidly Exploring Random Trees (RRT) algorithm, an improved algorithm integrating path pruning, smoothing, and optimization with geometric collision detection is shown to improve planning efficiency. Path pruning, a prerequisite to path smoothing, is performed to remove the redundant points generated by the random trees for a new path, without colliding with the obstacles. Path smoothing is performed to modify the path so that it becomes continuously differentiable with curvature implementable by the vehicle. Optimization is performed to select a “near”-optimal path of the shortest distance among the feasible paths for motion efficiency. In the experimental verification, both a pure pursuit steering controller and a proportional–integral speed controller are applied to keep an autonomous vehicle tracking the planned path predicted by the improved RRT algorithm. It is shown that the vehicle can successfully track the path efficiently and reach the destination safely, with an average tracking control deviation of 5.2% of the vehicle width. The path planning is also applied to lane changes, and the average deviation from the lane during and after lane changes remains within 8.3% of the vehicle width.

Minimisation of Motion Sickness in Autonomous Vehicles

2020 ◽  
Author(s):  
Zaw Htike ◽  
Georgios Papaioannou ◽  
Efstathios Siampis ◽  
Efstathios Velenis ◽  
Stefano Longo
Keyword(s):  
Motion Sickness ◽  
Autonomous Vehicles
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