A novel motion cueing algorithm integrated multi-sensory system–Vestibular and proprioceptive system

2019 ◽  
Vol 234 (2) ◽  
pp. 256-271
Author(s):  
Pham Duc-An ◽  
Nguyen Duc-Toan
Keyword(s):  
Visual Motion ◽  
Driving Simulator ◽  
Sensory System ◽  
Lateral Acceleration ◽  
Optimal Motion ◽  
Tilted Angle ◽  
Motion Cueing ◽  
Simulated Motion ◽  
S Curve ◽  
The Mathematical Model

Motion cueing algorithms are used to produce a motion which feels as realistic as possible while remaining in the limited workspace of driving simulators. Several optimal motion cueing algorithms were developed to improve both the exploitation of the workspace of a driving simulator and the realistic of the simulated motion. In the dynamics model of the optimal motion cueing algorithms, several kinds of motion-sensory systems are integrated to optimize the simulated motion sensation. However, most previous works have just focused on the visual and vestibular system. The mathematical model of the proprioceptive system, that also senses the non-visual motion, has rarely been concerned. In this paper, a novel optimal motion cueing algorithm, which integrates model of the proprioceptive system, is developed to reduce the false cues from muscle spindle of head/neck system sensing lateral tilted angle. The optimal motion cueing algorithm has a significant effect on the pilot's perception when the tilted angle is rather large. An example of the simulation of a roller coaster running along a planar S-curve trajectory with only lateral acceleration is investigated with current motion cueing algorithms and optimal motion cueing algorithm. Several objective criteria were introduced to evaluate the simulated perception of all investigated motion cueing algorithms. The results demonstrate that optimal motion cueing algorithm is better than current motion cueing algorithms in most criteria and also sub-criteria.

A genetic algorithm–based nonlinear scaling method for optimal motion cueing algorithm in driving simulator

2018 ◽  
Vol 232 (8) ◽  
pp. 1025-1038 ◽  
Author(s):  
Houshyar Asadi ◽  
Chee Peng Lim ◽  
Arash Mohammadi ◽  
Shady Mohamed ◽  
Saeid Nahavandi ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Driving Simulator ◽  
Linear Acceleration ◽  
Linear Quadratic Regulator ◽  
Linear Quadratic ◽  
Optimal Motion ◽  
Scaling Method ◽  
Motion Cueing ◽  
Physical Limitations ◽  
Simulator Motion

A motion cueing algorithm plays an important role in generating motion cues in driving simulators. The motion cueing algorithm is used to transform the linear acceleration and angular velocity of a vehicle into the translational and rotational motions of a simulator within its physical limitation through washout filters. Indeed, scaling and limiting should be used along within the washout filter to decrease the amplitude of the translational and rotational motion signals uniformly across all frequencies through the motion cueing algorithm. This is to decrease the effects of the workspace limitations in the simulator motion reproduction and improve the realism of movement sensation. A nonlinear scaling method based on the genetic algorithm for the motion cueing algorithm is developed in this study. The aim is to accurately produce motions with a high degree of fidelity and use the platform more efficiently without violating its physical limitations. To successfully achieve this aim, a third-order polynomial scaling method based on the genetic algorithm is formulated, tuned, and implemented for the linear quadratic regulator–based optimal motion cueing algorithm. A number of factors, which include the sensation error between the real and simulator drivers, the simulator’s physical limitations, and the sensation signal shape-following criteria, are considered in optimizing the proposed nonlinear scaling method. The results show that the proposed method not only is able to overcome problems pertaining to selecting nonlinear scaling parameters based on trial-and-error and inefficient usage of the platform workspace, but also to reduce the sensation error between the simulator and real drivers, while satisfying the constraints imposed by the platform boundaries.

scholarly journals Influence of a new discrete-time LQR-based motion cueing on driving simulator

2013 ◽  
Vol 35 (4) ◽  
pp. 454-467 ◽  
Author(s):  
B. Aykent ◽  
F. Merienne ◽  
D. Paillot ◽  
A. Kemeny
Keyword(s):  
Discrete Time ◽  
Driving Simulator ◽  
Motion Cueing

scholarly journals Effects of Visual Display and Motion System Delays on Operator Performance and Uneasiness in a Driving Simulator

1987 ◽  
Vol 31 (5) ◽  
pp. 492-496 ◽  
Author(s):  
Lawrence H. Frank ◽  
John G. Casali ◽  
Walter W. Wierwille
Keyword(s):  
Visual Motion ◽  
Driving Simulator ◽  
Secondary Analysis ◽  
Visual Display ◽  
Well Being ◽  
Operator Performance ◽  
Motion System ◽  
System A ◽  
Performance Examination

The role of visual-motion coupling delays and cueing order on operator performance and uneasiness was assessed in a driving simulator by means of a response surface methodology central-composite design. The most salient finding of the study was that visual delay appears to be more disruptive to an individual's control performance and well-being than motion delay. Empirical multiple regression models were derived to predict 10 reliable measures of simulator operator driving performance and comfort. Principal components analysis on these 10 models decomposed the dependent measures into two significant models which were labeled vestibular disruption and degraded performance. Examination of the empirical models revealed that, for asynchronous delay conditions, better performance and well-being were achieved when the visual system led the motion system. A secondary analysis of the role of subject gender and perceptual style on susceptibility to simulator sickness revealed that neither of these independent variables was a significant source of variance.

Integrated Strategy for Automatic Lane-Changing Decision and Trajectory Planning in Dynamic Scenario

2020 ◽  
Author(s):  
Meng Ren ◽  
Guangqiang Wu
Keyword(s):  
Neural Network ◽  
Trajectory Planning ◽  
Driving Simulator ◽  
Back Propagation ◽  
Lateral Acceleration ◽  
Vehicle Speed ◽  
Lane Changing ◽  
Planning Strategy ◽  
Integrated Strategy ◽  
Dynamic Scenario

Abstract Automatic lane change is a necessary part for autonomous driving. This paper proposes an integrated strategy for automatic lane-changing decision and trajectory planning in dynamic scenario. The Back Propagation Neural Network (BPNN) is used in decision-making layer, whose prediction accuracy of the discretionary lane-changing is 94.22%. The planning layer determines the adjustable range of the average vehicle speed based on the size of the “lane-changing demand”, which is obtained based on the data of hidden layer in neural network, and then dynamically optimizes the lane-changing curve according to the vehicle speed and the current scenario. In order to verify the rationality of the proposed lane-changing architecture, simulation experiments based on a driving simulator is performed. The experiments show that the vehicle’s maximum lateral acceleration under the proposed lane-changing trajectory at a speed of 70km/h is about 0.1g, which means the vehicle has better comfort during lane-changing. At the same time, the proposed lane-changing trajectory is more in line with the human driver’s lane-changing trajectory compared with that of other planning strategy. Meanwhile, the planning strategy can also support the lane-changing trajectory planning on a curved road.

Nonvestibular Motion Cueing in a Fixed-Base Driving Simulator: Effects on Driver Braking and Cornering Performance

2011 ◽  
Vol 20 (2) ◽  
pp. 117-142 ◽  
Author(s):  
S. de Groot ◽  
M. Mulder ◽  
P. A. Wieringa
Keyword(s):  
Driving Simulator ◽  
Low Cost ◽  
Seat Belt ◽  
Steering Wheel ◽  
Braking Performance ◽  
Driver Performance ◽  
Driving Speed ◽  
Motion Cueing ◽  
Fixed Base ◽  
Lane Keeping

Motion platforms can be used to provide vestibular cues in a driving simulator, and have been shown to reduce driving speed and acceleration. However, motion platforms are expensive devices, and alternatives for providing motion cues need to be investigated. In independent experiments, the following eight low-cost nonvestibular motion cueing systems were tested by comparing driver performance to control groups driving with the cueing system disengaged: (1) seat belt tensioning system, (2) vibrating steering wheel, (3) motion seat, (4) screeching tire sound, (5) beeping sound, (6) road noise, (7) vibrating seat, and (8) pressure seat. The results showed that these systems are beneficial in reducing speed and acceleration and that they improve lane-keeping and/or stopping accuracy. The seat belt tensioning system had a particularly large influence on driver braking performance. This system reduced driving speed, increased stopping distance, reduced maximum deceleration, and increased stopping accuracy. It is concluded that low-cost nonvestibular motion cueing may be a welcome alternative for improving in-simulator performance so that it better matches real-world driving performance.

scholarly journals The Role of a Novel Discrete-Time MRAC Based Motion Cueing on Loss of Control at a Hexapod Driving Simulator

2015 ◽  
Vol 06 (01) ◽  
pp. 84-102 ◽  
Author(s):  
B. Aykent ◽  
D. Paillot ◽  
F. Merienne ◽  
C. Guillet ◽  
A. Kemeny
Keyword(s):  
Discrete Time ◽  
Driving Simulator ◽  
Loss Of Control ◽  
Motion Cueing

Sensitivity Analysis of Vehicle Parameters on Dynamic Stability and Vehicle Handling Performance

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
M.M.M. Salem ◽  
Mina. M Ibrahim ◽  
M.A. Mourad ◽  
K.A. Abd El-Gwwad
Keyword(s):  
Dynamic Stability ◽  
Degrees Of Freedom ◽  
Front Wheel ◽  
Lateral Acceleration ◽  
Vehicle Speed ◽  
Vehicle Dynamic ◽  
Vehicle Handling ◽  
Bicycle Model ◽  
State Region ◽  
The Mathematical Model

In this paper, a linear two degrees of freedom linear bicycle model is proposed to investigate the vehicle handling criterion. The study is based on simulation developed using MATLAB / Simulink to predict the vehicle dynamic stability. Steering angle is given as an input to the mathematical model for various vehicular manoeuvres. This model is validated using a step input which is adjusted to give 0.3g lateral acceleration. The system model is simulated under a typical front wheel steering to examine the highway vehicle prediction output within its manoeuvre. This input is also adjusted to keep lateral acceleration value in steady state region. It is found that changing the vehicle center of gravity (CG) position, vehicle mass, tire cornering stiffness and vehicle speed all have a significant influence on the vehicle dynamic stability.

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