Optimal Motion Cueing for Race Cars

Salisbury, Ingrid Gael; Limebeer, D.J.N.

doi:10.1109/tcst.2015.2424161

Cited by 17 publications

(14 citation statements)

References 13 publications

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“…This limitation is expressed as an inequality constraint, where the actuator length is bounded between minimum ( L min ) and maximum values ( L max ). The lengths of the actuators can be computed solving the hexapod inverse kinematics, 23 as follows:…”

Section: Methodsmentioning

confidence: 99%

Objective evaluation of prediction strategies for optimization-based motion cueing

et al. 2018

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Optimization-based motion cueing algorithms based on model predictive control have been recently implemented to reproduce the motion of a car within the limited workspace of a driving simulator. These algorithms require a reference of the future vehicle motion to compute a prediction of the system response. Assumptions regarding the future reference signals must be made in order to develop effective prediction strategies. However, it remains unclear how the prediction of future vehicle dynamics influences the quality of the motion cueing. In this study two prediction strategies are considered. Oracle: the ideal prediction strategy that knows exactly what the future reference is going to be. Constant: a prediction strategy that ignores every future change and keeps the current vehicle’s linear accelerations and angular velocities constant. The two prediction strategies are used to reproduce a sequence of maneuvers between 0 and 50 km/h. A comparative analysis is carried out to objectively evaluate the influence of the prediction strategies on motion cueing quality. Dedicated indicators of correlation, delay and absolute error are used to compare the effects of the adopted prediction on simulator motion. Also the motion cueing mechanisms adopted by the different conditions are analyzed, together with the usage of simulator workspace. While the constant strategy provided reasonable cueing quality, the results show that knowledge of the future vehicle trajectory reduces the delay and improves correlation with the reference trajectory, it allows the combined usage of different motion cueing mechanisms and increases the usage of workspace.

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Section: Methodsmentioning

confidence: 99%

Objective evaluation of prediction strategies for optimization-based motion cueing

et al. 2018

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“…Recently, Dagdelen et al have proposed an MCA based on model-based predictive control [ 23 ]. This approach has received a lot of attention lately [ 24 , 25 , 26 , 27 ], since the classical, the adaptive and the optimal algorithms have been used for more than thirty years, without any of them having proven to be considerably superior to the others. In fact, the classical algorithm is still the most utilized one because it is simple, effective and relatively easy to implement, even though still limited.…”

Section: Related Workmentioning

confidence: 99%

Development of a Hybrid Method to Generate Gravito-Inertial Cues for Motion Platforms in Highly Immersive Environments

Riera

Casas

Fernández

et al. 2021

Sensors

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Motion platforms have been widely used in Virtual Reality (VR) systems for decades to simulate motion in virtual environments, and they have several applications in emerging fields such as driving assistance systems, vehicle automation and road risk management. Currently, the development of new VR immersive systems faces unique challenges to respond to the user’s requirements, such as introducing high-resolution 360° panoramic images and videos. With this type of visual information, it is much more complicated to apply the traditional methods of generating motion cues, since it is generally not possible to calculate the necessary corresponding motion properties that are needed to feed the motion cueing algorithms. For this reason, this paper aims to present a new method for generating non-real-time gravito-inertial cues with motion platforms using a system fed both with computer-generated—simulation-based—images and video imagery. It is a hybrid method where part of the gravito-inertial cues—those with acceleration information—are generated using a classical approach through the application of physical modeling in a VR scene utilizing washout filters, and part of the gravito-inertial cues—the ones coming from recorded images and video, without acceleration information—were generated ad hoc in a semi-manual way. The resulting motion cues generated were further modified according to the contributions of different experts based on a successive approximation—Wideband Delphi-inspired—method. The subjective evaluation of the proposed method showed that the motion signals refined with this method were significantly better than the original non-refined ones in terms of user perception. The final system, developed as part of an international road safety education campaign, could be useful for developing further VR-based applications for key fields such as driving assistance, vehicle automation and road crash prevention.

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“…In this case the vehicle will tend to run outside the intended path, with the front slip angle increasing more rapidly Figure 11. Lateral acceleration and yaw cueing filter design problems cast in the generalised regulator framework [3,17].…”

Section: Centre Of Turningmentioning

confidence: 99%

“…The optimal-control-based cueing filter design strategy is frequently used in passenger car applications [4], and is applied to the longitudinal race-car motion in [3]. In this work, this method is used to design filters for the lateral and yaw directions, which are tuned using the analysis of the vehicle limit behaviour presented in the previous sections.…”

Section: Cueing Filter Designmentioning

confidence: 99%

“…In order to satisfy workspace and bandwidth constraints, vehicular acceleration signals must be filtered and/or attenuated and consequently only part of the vehicle's movement is rendered by the motion platform. Motion cueing algorithms have evolved to address the design of these filters [3]. Most of the approaches described in the literature aim to minimise the difference between the accelerations perceived in the simulator and those perceived in the real vehicle, where the perception model is a linear transfer function representing the vestibular dynamics [4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Motion cueing in high-performance vehicle simulators

Salisbury

Limebeer

2017

Vehicle System Dynamics

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The majority of motion cueing algorithms have been developed for passenger car applications, with correspondingly less research dedicated to race-car and high-performance vehicle simulators. In the high-performance context, the focus is on cueing the vehicle's behavioural and handling characteristics, particularly when driving near the limits of performance. In race car simulators the cueing requirements are therefore quite different, with the problem made all the more challenging by the presence of large accelerations. To understand the drivers' cueing needs, the vehicle's stability and handling response characteristics must be examined near the performance boundary. Frozen-time eigenvalue analyses are used to determine stability and response characteristics across all vehicle operating conditions, including accelerating and braking under cornering, with the results used to determine motion cueing algorithm requirements. Lateral acceleration and yaw cueing filters are designed in order to retain information critical to understanding the vehicle's behaviour on its performance boundary. Cueing filters are then tested, with the help of a professional race car driver, and are found to provide the cues necessary for the driver to control the vehicle on the limit of performance.

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Optimal Motion Cueing for Race Cars

Cited by 17 publications

References 13 publications

Objective evaluation of prediction strategies for optimization-based motion cueing

Objective evaluation of prediction strategies for optimization-based motion cueing

Development of a Hybrid Method to Generate Gravito-Inertial Cues for Motion Platforms in Highly Immersive Environments

Motion cueing in high-performance vehicle simulators

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