Cueing Algorithm Research Articles

Enhancing perception of vehicle motion by objective positioning of the longitudinal axis of rotation in driving simulators

The automotive industry is heading towards a more objective approach to vehicle testing, but subjective evaluation is still an important part of the development process. Subjective evaluation in physical testing has environmental implications and is dependent on ambient conditions. A more repeatable, faster, safer and more cost-effective tool for subjective evaluation is to use moving base driving simulators. The motion cueing algorithms (MCA) maps the movement of the vehicle into the limited space of the simulator. The choice of reference point, that is, where on the vehicle to sample the motion to feed to the MCA and the alignment of the axis of rotation of the simulator cabin is still an open topic. This paper investigates the choice of reference point and corresponding simulator longitudinal axis of rotation in roll using two methods. The first method uses a linearised model of the combined system of vehicle, simulator and vestibular models. The second method, to position the cabin longitudinal axis of rotation, is based on offline optimisation. The linear model can capture important characteristics of the specific forces and rotations that are fed to the driver through the motion cueing algorithms and offers a method to objectively analyse and potentially tune the motion cueing. The analysis is further complemented with a subjective evaluation of corresponding settings. The results from the linear model, the offline optimisation and the subjective evaluation shows that a reference point at the driver’s head has a clear advantage over the full frequency range compared to a reference point in the chassis roll axis and that the positioning of the cabin longitudinal axis of rotation has a significant effect on the perceived vehicle characteristics.

High-fidelity learning-based motion cueing algorithm by bypassing worst-case scenario-based tuning technique

The motion cueing algorithm (MCA) enhances the realism of simulator driving experiences by generating vehicle motions within platform limitations. Existing MCAs are typically tuned for worst-case scenarios, limiting their efficiency for medium or slow driving motions. This study proposes a comprehensive MCA unit using learning-based models to overcome this problem and efficiently utilise the simulator workspace for all driving scenarios. Data samples are regenerated to cover various motion signal levels, and three classical washout filters are tuned to extract optimal motion signals. A multilayer perceptron (MLP) is trained with these extracted datasets, forming an AI-based MCA that provides high-fidelity driving motions for any scenario while optimising the platform workspace. Simulink/MATLAB is used for modelling and evaluation. Results demonstrate the proposed model's superior performance, with lower motion sensation errors, a higher correlation between sensed motion signals, and more efficient platform workspace usage.

A Deep Reinforcement Learning Based Motion Cueing Algorithm for Vehicle Driving Simulation

A Deep Reinforcement Learning Based Motion Cueing Algorithm for Vehicle Driving Simulation

Objective Evaluation of Motion Cueing Algorithms for Vehicle Driving Simulator Based on Criteria Importance through Intercriteria Correlation (CRITIC) Weight Method Combined with Gray Correlation Analysis

Perception-based fidelity evaluation metrics are crucial in driving simulators, as they play a key role in the automatic tuning, assessment, and comparison of motion cueing algorithms. Nevertheless, there is presently no unified and effective evaluation framework for these algorithms. To tackle this challenge, our study initially establishes a model rooted in visual–vestibular interaction and head tilt angle perception systems. We then employ metrics like the Normalized Average Absolute Difference (NAAD), Normalized Pearson Correlation (NPC), and Estimated Delay (ED) to devise an evaluation index system. Furthermore, we use a combined approach incorporating CRITIC and gray relational analysis to ascertain the weights of these indicators. This allows us to consolidate them into a comprehensive evaluation metric that reflects the overall fidelity of motion cueing algorithms. Subjective evaluation experiments validate the reasonableness and efficacy of our proposed Perception Fidelity Evaluation (PFE) method.

On-Demand Gait-Synchronous Electrical Cueing in Parkinson's Disease Using Machine Learning and Edge Computing: A Pilot Study.

Goal: Parkinson's disease (PD) can lead to gait impairment and Freezing of Gait (FoG). Recent advances in cueing technologies have enhanced mobility in PD patients. While sensor technology and machine learning offer real-time detection for on-demand cueing, existing systems are limited by the usage of smartphones between the sensor(s) and cueing device(s) for data processing. By avoiding this we aim at improving usability, robustness, and detection delay. Methods: We present a new technical solution, that runs detection and cueing algorithms directly on the sensing and cueing devices, bypassing the smartphone. This solution relies on edge computing on the devices' hardware. The wearable system consists of a single inertial sensor to control a stimulator and enables machine-learning-based FoG detection by classifying foot motion phases as either normal or FoG-affected. We demonstrate the system's functionality and safety during on-demand gait-synchronous electrical cueing in two patients, performing freezing of gait assessments. As references, motion phases and FoG episodes have been video-annotated. Results: The analysis confirms adequate gait phase and FoG detection performance. The mobility assistant detected foot motions with a rate above 94 % and classified them with an accuracy of 84 % into normal or FoG-affected. The FoG detection delay is mainly defined by the foot-motion duration, which is below the delay in existing sliding-window approaches. Conclusions: Direct computing on the sensor and cueing devices ensures robust detection of FoG-affected motions for on demand cueing synchronized with the gait. The proposed solution can be easily adopted to other sensor and cueing modalities.

Model Predictive Control based Motion Cueing Algorithm for Driving Simulator

Model Predictive Control based Motion Cueing Algorithm for Driving Simulator

Novel model predictive control-based motion cueing algorithm for compensating centrifugal acceleration in KUKA robocoaster-based driving simulators.

The washout motion cueing algorithm (MCA) is a critical element in driving simulators, designed to faithfully reproduce precise motion cues while minimizing false cues during simulation processes, particularly deceptive translational and rotational cues. To enhance motion sensation accuracy and optimize the use of available workspace, model predictive control (MPC) has been employed to develop innovative motion cueing algorithms. While most MCAs have been tailored for the Steward motion platform, there has been a recent adoption of the motion platform based on KUKA Robocoaster as an economical option for driving simulators. However, leveraging the full potential of the KUKA Robocoaster requires trajectory conversion of the motion base. Thus, this research proposes a novel MCA specifically designed for the KUKA Robocoaster-based motion platform, utilizing large planar circular motion to simulate lateral movement for drivers. Nonetheless, circular motion introduces disruptive centrifugal forces, which can be mitigated through proper pitch-tilted angles. The novel MPC generates simulated motion that accurately follows the lateral specific force target and effectively maintains the roll angular velocity below its threshold value. Additionally, it compensates for disturbing centrifugal acceleration by implementing pitch rotational motion, ensuring the pitch angular velocity remains below its threshold. Simulation tasks conducted on the motion platform, focusing solely on lateral acceleration, demonstrate the successful elimination of false motion cues in both the roll/sway and pitch/surge channels. The proposed innovative MPC solution offers an original approach to motion cueing algorithms in KUKA Robocoaster-based driving simulators. It enables the exploitation of the KUKA Robocoaster platform's capabilities while delivering accurate and immersive motion cues to drivers during simulation experiences.

Real-time forecasting of driver-vehicle dynamics on 3D roads: A deep-learning framework leveraging Bayesian optimisation

Most state-of-the-art works in trajectory forecasting for automotive target predicting the pose and orientation of the agents in the scene. This represents a particularly useful problem, for instance in autonomous driving, but it does not cover a spectrum of applications in control and simulation that require information on vehicle dynamics features other than pose and orientation. Also, multi-step dynamic simulation of complex multibody models does not seem to be a viable solution for real-time long-term prediction, due to the high computational time required. To bridge this gap, we present a deep-learning framework to model and predict the evolution of the coupled driver-vehicle system dynamics jointly on a complex road geometry. It consists of two components. The first, a neural network predictor, is based on Long Short-Term Memory autoencoders and fuses the information on the road geometry and the past driver-vehicle system dynamics to produce context-aware predictions. The second, a Bayesian optimiser, is proposed to tune some significant hyperparameters of the network. These govern the network complexity, as well as the features importance. The result is a self-tunable framework with real-time applicability, which allows the user to specify the features of interest. The approach has been validated with a case study centred on motion cueing algorithms, using a dataset collected during test sessions of a non-professional driver on a dynamic driving simulator. A 3D track with complex geometry has been employed as driving environment to render the prediction task challenging. Finally, the robustness of the neural network to changes in the driver and track was investigated to set guidelines for future works.

An Optimal Nonlinear Model Predictive Control- Based Motion Cueing Algorithm Using Cascade Optimization and Human Interaction

Nonlinear model predictive control has been used in motion cueing algorithms recently to consider the nonlinear dynamics model of the system. The entire motion cueing algorithm indexes, including the physical and dynamical constraints of the actuators and physical constraints of passive joints, can be controlled with precision using nonlinear model predictive control. However, several weighting parameters in the nonlinear model predictive control-based motion cueing algorithm (including driving sensation, motion description of the actuators, and passive joints) require proper and laborious tuning to attain an optimal design structure. In this work, the optimal weighting parameters of a nonlinear predictive control-based motion cueing algorithm model are calculated using cascade optimisation and human interaction. A cascade optimisation method consisting of a particle swarm optimisation and genetic algorithm is designed to identify the best weighting parameters compared to those from one optimiser. In addition, the human decision-making units are added to the two-level cascade optimiser to determine the best solution from a Pareto front. The proposed cascade optimiser decreases the run-time with better extraction of the optimal weighting parameters to increase the motion fidelity compared to a single optimiser. It should be noted that the proposed methodology is applied along longitudinal channel. While the same methodology can be applied along lateral, heave and yaw channels for further evaluation of the proposed method. The proposed model is simulated utilising the MATLAB software and the results prove the efficiency of the newly proposed model compared to those from the previous single optimiser in reproducing more accurate motion signals with better usage of the driving motion platform workspace.

A Novel Decoupled Model Predictive Control-Based Motion Cueing Algorithm for Driving Simulators

The motion cueing algorithm (MCA) is used to reproduce the driving/flying motion sensation of the real land or air vehicles for the users of motion simulator. Highly accurate MCAs are required for the motion simulators to create realistic sensation for the simulator users, otherwise, they might cause motion sickness and user discomfort. Model predictive control (MPC) is a popular technique in development of the high-fidelity MCAs as it is able to consider the motion sensation as well as the workspace constraints of the simulator platform. Tilt coordination in MCA is in charge of creating sustained linear acceleration feeling through tilting the platform cabin and exploiting gravitational acceleration under human motion threshold, to not allow the user to perceive this as a rotational motion. However, the existing MPC-based MCAs have not considered the rate limit to constrain the tilt motion under human threshold which causes violations of the rotational sensation threshold during generating sustained acceleration feeling. Instead, some researchers increase the penalty weights of rotational motions in order to slow down rotational motions. Using this strategy slows down all the simulator rotational motions not only the ones coming from tilt coordination, to generate sustained acceleration feeling, but also from rotational channel (due to real vehicle rotation) which causes motion sensation error and consequently motion sickness. Therefore, as the existing MPC-based MCAs cannot differentiate the rotations of tilt coordination channel (due to sustained acceleration feeling generation), from rotational channel (due to vehicle rotational motions), they are not able to produce accurate rotational motion sensations because of insufficient rotational motions. In this research, a novel decoupled MPC-based MCA is developed to systematically address the issues related to the existing MPC-based MCAs, in terms of inaccurate motion generation, by redesigning the MPC-based MCA using a series of vestibular system-based MPC models and considering tilt rate limit in tilt coordination. The simulation study using MATLAB software is used to verify and validate the proposed MCA compared to the existing MCAs. The proposed decoupled MPC-based MCA is able to generate accurate motion cues compared to those of the existing MCAs.

Adaptive Motion Cueing Algorithm Using Optimized Fuzzy Control System for Motion Simulators

The Motion Cueing Algorithm (MCA) is the main unit of motion simulators responsible for transforming the vehicle motions to generate driving motion sensation for the simulator users within the motion simulator's physical workspace through washout filters. In this study, we design and provide a new framework by developing a set of novel washout filters using optimized fuzzy control systems to solve the drawbacks associated with the existing optimal MCAs of the motion simulators. These drawbacks include constant washout filter parameters, inefficient simulator movement in the workspace, lack of considering the simulator's physical constraints and driver sensation-related factors, and sub-optimal settings which causes false motion cues and subsequently simulator sickness. As a pioneering framework, the fuzzy logic controllers generate restitution signals for the optimal washout filters based on the motion sensation error between the real vehicle and simulator divers as well as the platform's position in the workspace aiming to correct the filtered signals, reduce error, generate realistic motions, and increase motion fidelity online. The GA is applied to optimize the fuzzy logic membership functions and control rules. The optimization procedure takes into account a number of important factors that are normally ignored in the current MCAs including the sensed motion error between the drivers of simulator and real car; the platform physical constraints; the motion threshold limiter effects; and signal shape following. The new adaptive MCA is designed, implemented and tested using the MATLAB/Simulink. The simulation results confirm the effectiveness of the developed MCA in maximizing motion fidelity and the reference sensation shape following, and enhancing the workspace usage of the simulator.

A New Prepositioning Technique of a Motion Simulator Platform Using Nonlinear Model Predictive Control and Recurrent Neural Network

The motion cueing algorithm (MCA) is the main algorithm in motion simulators in charge of generating vehicle motions within the platform’s constraints. The classical washout filter is one of the popular types of MCA, which is used in air and land vehicle motion simulators. The fixed home position of the simulator platform is always cogitated in the MCA to washout the motion simulator after generating each motion. Unfortunately, considering the fixed home position reduces the efficient consumption of the workspace in the linear directions. The linear motion of the motion simulator is due to the production of the high-pass frequency part of the motion scenarios. Prepositioning is used to tackle this assumption by varying the home position rather than the fixed position. The linear motion limitations of the motion simulator can virtually be enlarged using the prepositioning method. The efficient regeneration of the high-pass motion cues using a new propositioning technique is the main goal of this study to increase the motion realism of the simulator and remove any false motion cues due to the platform limitations. The proposed model utilised the recurrent neural network (RNN) to estimate the motion scenario along the prediction horizon. The nonlinear model predictive control (MPC) uses the estimated motion signals to extract the best optimal off-centre position of the motion simulator platform. The newly developed prepositioning technique is developed in the simulation environment of MATLAB to validate the proposed technique in terms of efficiency and applicability. The outcomes prove the capability of the proposed technique against the recently developed prepositioning technique using fuzzy logic and RNN.

Validation of a moving base driving simulator for motion sickness research

Increasing levels of vehicle automation are envisioned to allow drivers to engage in other activities but are also likely to increase the incidence of Carsickness or Motion Sickness (MS). Ideally, MS is studied in a safe and controlled environment, such as a driving simulator. However, only few studies address the suitability of driving simulators to assess MS. In this study, we validate a moving base driving simulator for MS research by comparing the symptoms and time course of MS between a real-road driving scenario and a rendition of this scenario in a driving simulator, using a within-subjects design. 25 participants took part as passengers in an experiment with alternating sections (slaloming, stop-and-go) with normal and provocative driving styles. Participants performed Sudoku puzzles (eyes-off-road) during both scenarios and reported MIsery SCale (MISC) scores at 30 s intervals. Motion Sickness Assessment Questionnaire (MSAQ) scores were collected upon completion of either scenario. Overall, the results indicate that MS was more severe in the car than in the simulator. Nevertheless, significant correlations were found between individual MS in the car and simulator for 3 out of 4 MSAQ symptom categories (0.48 < r < 0.73, p < 0.02), with a strong overall correlation (r = 0.57, p = 0.004). MS onset times were similar between the car and the simulator, and sickness fluctuations as a result of driving style showed a similar pattern between scenarios, albeit more pronounced in the car. Based on observed similarities in MS, we conclude these simulator results to have relative validity. We attribute the observed reduction of MS severity in the simulator to the downscaling of the motion by the Motion Cueing Algorithm (MCA). These results suggest that, at least in eyes-off-road conditions, findings on MS from simulator studies may generalize to real vehicles after application of a conversion factor. This conversion factor is likely to depend on simulator and MCA characteristics.

A Case Study on Vestibular Sensations in Driving Simulators.

Motion platforms have been used in simulators of all types for several decades. Since it is impossible to reproduce the accelerations of a vehicle without limitations through a physically limited system (platform), it is common to use washout filters and motion cueing algorithms (MCA) to select which accelerations are reproduced and which are not. Despite the time that has passed since their development, most of these algorithms still use the classical washout algorithm. In the use of these MCAs, there is always information that is lost and, if that information is important for the purpose of the simulator (the training simulators), the result obtained by the users of that simulator will not be satisfactory. This paper shows a case study where a BMW 325Xi AUT fitted with a sensor, recorded the accelerations produced in all degrees of freedom (DOF) during several runs, and data have been introduced in mathematical simulation software (washout + kinematics + actuator simulation) of a 6DOF motion platform. The input to the system has been qualitatively compared with the output, observing that most of the simulation adequately reflects the input to the system. Still, there are three events where the accelerations are lost. These events are considered by experts to be of vital importance for the outcome of a learning process in the simulator to be adequate.

Online Model Predictive Motion Cueing With Real-Time Driver Prediction

In this article a motion cueing algorithm (MCA) based on model predictive control (MPC) for a hexapod-based dynamic driving simulator is derived. The design objective of the MCA is to reproduce the real world accelerations and angular velocities for a test person in the simulator while respecting its actuator limitations. This results in an underlying nonlinear, state-constrained optimal control problem. In order to exploit the full predictive potential of the described algorithm, a method to predict the future driver behavior and thus the future desired values for the MPC is derived by modeling the driver as an optimal controller. The OCP weights that mainly influence the predicted driving actions are learned from demonstration using an inverse optimal control (IOC) approach. Furthermore, the direct incorporation of human perception models into the motion planning process is considered. An online driver-in-the-loop experiment with the Daimler driving simulator shows the high potential of the derived MPC scheme compared to the commonly used filter-based approach as well as the efficiency of the underlying optimization method.

A Time-Varying Weight MPC-Based Motion Cueing Algorithm for Motion Simulation Platform

The motion cueing algorithm (MCA) is playing the most critical role in motion simulation platform (MSP) to reproduce the realistic motion sensation of the real car for the MSP’s users while taking into cogitation of the physical boundaries of the platform. Recently, the model predictive control (MPC) is employed for designing MCAs which led to the formation of MPC-based MCAs. The purpose of the MPC-based MCA is to recalculate the optimal values of the input signals with consideration of the MSP’s physical restrictions. All the current MPC-based MCAs have fix weights that can cause the conservative and inefficient utilization of the MSP’s workspace limitations as they have been tuned based on the worst-case scenarios to keep the platforms within their physical limitations. Then, the error of motion sensation between the real car and MSP users increases due to the conservative utilization of the MSP’s workspace boundaries. The main objective of this study is to provide more efficient workspace utilization to minimise the error of motion feeling between the real car and MSP users while respecting the workspace boundaries. A procedure according to the optimised fuzzy logic-based units is employed to calculate the appropriate MPC weights online while considering the sensed specific force error, sensed angular velocity error and the current motion status of the MSP including linear position, linear velocity and angular position of the cockpit. The proposed MPC-based MCA is designed and developed using MATLAB. The outcomes show a better motion feeling compared with the current MPC-based MCAs.

Handiski simulator performance under PSO-based washout and control parameters optimization

The transfer of advanced technology to the person with a disability who wishes to practice a sporting activity is gaining momentum in the science/engineering world. This paper seeks to approve more comfort and sensations for people with paraplegia on a motion simulation platform during a ski operation. The Motion Cueing Algorithm (MCA) has proven itself for sensation reproduction, which we propose to improve by integrating the physical limits of our 8-DoF mechatronics platform. An extended classical MCA is proposed to respond to the significant lack of restored sensation in the intermediate frequency range. A Particle Swarm Optimization (PSO) is constructed to perform optimal washout filter parameters and control law parameters. Consequently, the reproduced skier trajectory stability is obtained. The results show that the proposed algorithm will overcome the physical limitation problem in the Handiski simulator, improve the realism of movement sensation, and reduce the false cues to enhance dynamic fidelity.

Auto-Tuning parameters of motion cueing algorithms for high performance driving simulator based on Kuka Robocoaster.

Driving simulators have been utilized to test and evaluate products and services for a long time. Their complexity and price range from extremely simple low-cost simulators with a fixed base to very complex high-end and pricey six-degree-of-freedom simulators with the XY table. The recent novel technique that uses an industrial robot - KUKA Robocoaster - as an interactive motion simulator platform, allowing for a highly flexible workspace as well as significantly lower prices due to mass production of the fundamental mechanics. In the constrained workspace of driving simulators, motion cueing algorithms (MCAs) are commonly employed to merge the tilt gravity and translational acceleration components for simulating the linear acceleration in the real vehicle. However, there is a few MCAs developed for the motion platform, almost MCAs were implemented for the standard six-degree-of-freedom simulators in the Cartesian coordinate. The classical MCA in the cylindrical coordinate (ClCy) MCA was first developed for the novel motion platform to take advantage of enormous rotational motion to simulate lateral acceleration while compensating for the bothersome longitudinal acceleration (due to centrifugal acceleration appearing in the rotational motion) with a proper pitch tilted angle. The process of tuning MCAs for the novel motion platform is time-consuming due to both trial and error method and the disturbing motion cues generated by rotational motion, thus it needs the involvement of experts. Although there are several auto-tuning approaches for classical, optimal, and model-predictive control MCA based on fuzzy control theory or genetic optimization method, the methods were purely applied for Cartersian coordinate without taking the bothersome longitudinal acceleration into account. Therefore, this paper firstly presents the process of integrating MCAs in the novel motion platform utilizing rotational motion for simulating lateral acceleration. For the case, besides the ClCy algorithm, the classical algorithm developed for the standart six-degree-of-freedom simulators was a sample implementation due to its popular and familiar characteristics. Secondly, the proposal of the use of the mean-variance mapping optimization (MVMO) for auto-tuning parameters of the two algorithms for reducing both rotational false cues in roll and pitch channel, and longitudinal acceleration as well as washout effect. The simulation results prove that 1) The classical and other MCAs can be applied in the novel motion platform with the proposed motion conversion; 2) both algorithms with auto-tuned parameters have high performance in exploiting effectively the workspace of the motion platform, producing no false cues of angular velocity, conpensating the disturbed longitudinal acceleration, and pulling the motion platform to the initial position after the simulation task; 3) The auto-tuning method is so transparent that can manipulates the specific simulated quantities according to the tuning goals.

DESIGNING AN EVOLUTIONARY OPTIMAL WASHOUT FILTER BASED ON GENETIC ALGORITHM

This paper aims to design a reliable filter that can transform the actual motion of a flight simulator maneuver into a logical and understandable movement for its workspace. Motion cueing algorithms are used in scaling maneuvers to improve the user’s perception of real-world motion. As a unique algorithm, the washout-filter algorithm reduces the real motions where the user cannot understand the difference between the actual and simulated maneuvers. To design a proper washout filter, first, apply the inner ear model where humans can feel the motion to design a proper filter. The Otolith and semicircular systems were represented by two parts in this model. Second, an evolutionary theory based on a genetic algorithm is used to design a structure that minimizes human perception error and workspace boundaries. The issue is determining the coefficients in the model in order to create a high-performance flight simulator. The filtering algorithm, based upon the human vestibular model, compares human perception with flight simulator motion knowledge. The findings demonstrate an objective function that minimizes user perception error, and the flight simulator motion range can prepare a reliable washout filter for motion cueing.

An optimal washout filter for motion platform using neural network and fuzzy logic

To experience the motion sensation of a real vehicle through a motion simulator, a motion cueing algorithm (MCA) is required to transform the vehicle motions to the driving motion platform (DMP) while respecting the physical limitations of DMP. In this aspect, the optimal washout filter (WF) extracts the optimal motion signals including linear accelerations and angular velocities for the DMP with consideration of the human vestibular model and DMP motion states using the linear quadratic regulator (LQR) technique. The LQR technique is employed to obtain the optimal and pre-defined higher order transfer functions by solving the Riccati equation. However, the Riccati equation is solved using fixed weights, leading to an inconvenient usage of the DMP workspace. In this research, a new optimal WF model is designed and developed using a neural network (NN) and a fuzzy logic controller (FLC). The NN is introduced to solve the Riccati equation online while the FLC model is designed to extract the weighting matrices of the LQR technique. The proposed technique considers the physical DMP limitations online and reproduces accurate motion signals with a high degree of fidelity. The results demonstrate the efficiency of the developed optimal WF model as compared with those of existing optimal WF models.