Ensuring the safety and efficiency of Autonomous Vehicles (AVs) necessitates highly accurate perception, especially for lane detection and lane-change manoeuvres. Among object detection frameworks, “You Only Look Once” (YOLO) algorithms have emerged as prominent contenders due to their rapid inference and commendable accuracy. However, the broad spectrum of YOLO variants and their applications in complex, real-world environments remain insufficiently mapped, necessitating a more integrative and critical perspective than what is typically offered by surveys. This comprehensive review synthesizes theoretical foundations, architectural innovations, and empirical evaluations of YOLO-based algorithms in AV-related tasks. It not only highlights key findings—such as the notable gains in real-time detection and adaptability to a range of driving conditions—but also explicitly identifies persistent gaps and limitations. These include difficulties in detecting subtle or degraded lane markings, handling unpredictable environmental factors like adverse weather and varied lighting, mitigating adversarial perturbations, and scaling effectively across diverse datasets and geographic regions. By critically examining these vulnerabilities, we illuminate the opportunities for refining YOLO's training paradigms, optimizing model architectures, incorporating sensor fusion, and fostering universally applicable datasets. The implications of addressing these gaps extend beyond mere technical refinements. Proactively tackling YOLO's current challenges can expedite the realization of safer, more robust, and globally adaptable AV navigation systems. In doing so, this review provides clear, actionable insights for researchers, engineers, and policymakers, guiding them toward strategic innovations that will strengthen AV perception and contribute to more reliable, future-ready transportation solutions.

ABSTRACT Emotion recognition has become a key component of intelligent systems, enabling improved human–computer interaction across domains such as healthcare, education, and robotics. Progress has been achieved using facial expressions and physiological signals, particularly Electroencephalography (EEG) and Electrocardiogram (ECG), supported by advances in deep learning. This paper presents a comprehensive review of unimodal emotion recognition based on facial expressions or physiological signals, with each modality analysed independently to understand signal-specific characteristics and modelling strategies. In addition to commonly studied modalities, this review covers physiological signals including Galvanic Skin Response (GSR), Photoplethysmography (PPG), Electrooculography (EOG), Electromyography (EMG), Respiration Rate (RR), Skin Temperature (SKT), and functional near-infrared spectroscopy (fNIRS). The review emphasises the design and evaluation of deep learning architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and emerging approaches such as transformer-based models, Vision Transformers (ViTs), and transfer learning techniques. Studies are analysed using a structured evaluation framework considering model design, preprocessing strategies, datasets, evaluation protocols, and performance. Unlike existing surveys, this work provides a critical analysis of prior studies, highlighting strengths, limitations, and trade-offs, with emphasis on generalisation capability and evaluation strategies. The review identifies risks associated with improper data partitioning, where data leakage can lead to overestimated performance. Key challenges include limited dataset sizes, lack of standardised evaluation protocols, and inconsistencies in performance reporting. This study provides a structured understanding of current research trends and outlines future directions for developing more robust, reliable, and generalisable emotion recognition systems.

Motion cueing algorithm aims to replicate realistic motion sensations for drivers while adhering to the physical limitations of the simulation platform. Model Predictive Control has been extensively employed within the domain of motion cueing algorithms for vehicle and flight simulators due to its ability to handle system constraints and optimise motion fidelity. However, traditional Model Predictive Control-based motion cueing algorithm rely on manually tuned cost function weights, which can be suboptimal and difficult to determine for different operating conditions. This suboptimal tuning can cause discrepancies between the visual input perceived by the simulator driver and the motion cues processed by their vestibular system, potentially resulting in motion sensation errors and increased risk of motion sickness. In this paper, we propose a reinforcement learning weight optimisation approach for the model predictive control-based motion cueing algorithm, leveraging Deep Q-Networks to determine an optimal set of cost function weights through training in a simulated environment. The optimised weights aim to minimise the cost function, thereby maximising the reward function. Simulation results indicate that the proposed method outperforms the traditional approach, leading to a reduction in motion sensation errors between the simulator and real vehicle driver and improving platform utilisation. The reinforcement learning-based control achieves better correlation between the reference and simulated signals for both sensed specific force and angular velocity, enhancing overall motion fidelity. It also reduces the root mean square error for sensed specific force, ensuring more accurate replication of target motion cues. Additionally, the method enables broader use of the simulator's linear displacement range, confirming the effectiveness of reinforcement learning in tuning control parameters for superior simulation performance.

Vehicle trajectory prediction plays a pivotal role in enhancing advanced driver assistance systems (ADAS) and autonomous vehicles (AVs), crucial for collision avoidance, path planning, and traffic management. Traditional models often fail to account for variations in driver behaviour, such as eye movement patterns, which can substantially influence trajectory predictions. Our research presents an advanced trajectory prediction model that integrates Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks with both eye movement data and traditional vehicle dynamic information. The accuracy of these models was evaluated in a simulated environment designed to mimic real-world driving conditions, capturing extensive data on vehicle dynamics, including position, rotation, acceleration, speed, and eye movement patterns. Data collection was rigorously conducted with 17 drivers, each using a driving simulator that ran the Euro Truck Simulator software. The models were implemented and validated using Python 3.9 and Google Colab, chosen for their effectiveness in handling deep learning tasks. Our findings demonstrate that the inclusion of eye movement data alongside vehicle dynamics enhances the accuracy of trajectory predictions, significantly reducing both Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) as well as Mean Absolute Error Percentage (MAPE), compared to models based solely on vehicle dynamics. This improvement not only bolsters the precision of trajectory predictions for ADAS and AV systems but also significantly elevates their safety and operational efficiency.

Simulator sickness remains a major challenge in immersive simulation systems, particularly in high-fidelity driving environments. While previous research has utilized machine learning with multimodal physiological data, it often depends on restricted feature sets and overlooks comprehensive eye movement and head motion data. In this study, we propose a deep learning framework using a hybrid 1CNN-BiLSTMAttention model for real-time quantification of simulator sickness severity. Data were collected using Deakin University’s Genesis Simulator—an immersive 360° environment with a six degrees-of-freedom motion platform. Eye-tracking and head movement features were extracted, and the Fast Motion Sickness (FMS) scale was used for severity labeling. The proposed model achieved 87.6% accuracy and an F1-score of 0.91 for high-severity detection. A 5-fold cross-validation demonstrated a significant benefit of attention over baseline models (p = 0.0019). This study offers a scalable solution for adaptive simulation and intelligent vehicle systems through integrated eye and head movement data.

Motion simulation can substantially improve the immersion of any form of vehicle simulation. However, inadequate motion simulation can fully break the immersion or even induce adverse effects, such as discomfort, motion sickness, or other harm to the simulator occupants. The selection of a stable and safe motion cueing algorithm (MCA) is therefore essential. In particular, complex and simultaneously real-time capable MCAs can carry the risk of instability. This phenomenon can be observed in non-linear model predictive control-based MCAs when the prediction horizon is defined too short, and in learning-based MCAs when there is insufficient utilization of training data. Specifically, the employment of artificial neural networks in the modeling of MCAs can lead to problems such as lack of generalization or overfitting, which, combined with the difficult interpretability due to the black-box character, makes analytical guarantees difficult. The problem is further intensified when the MCA is in a control loop with a motion platform that has a highly non-linear behavior.This work proposes a sample-based framework that utilizes simulative modeling of the deployed simulator platform to investigate the behavior of MCAs. The proposed framework is structured to initialize the system in random states, which allows for a comprehensive investigation of the behavior of any non-specific MCA. The framework is applied to two variations of one state-of-the-art MCA, and the results are compared. It is shown that the framework can identify deficiencies in the trajectory planning of MCAs. Thus, it is able to contribute significantly to the safety and stability of motion simulation.

Traffic accidents are a major public safety challenge around the world and are often influenced by the cognitive and physiological states of drivers. Among the multiple in-vehicle factors, listening to music has shown complex effects on driver behavior, particularly in relation to music tempo. This study proposes TransBiNet, a novel deep learning architecture that integrates Transformer-based attention mechanisms with Bidirectional Long Short-Term Memory layers to classify driver states under different auditory conditions using internal biometric signals. Data were collected from 26 participants driving in a simulated environment, where each subject completed scenarios involving fast-tempo music, slow-tempo music, and no music. Physiological signals (heart rate, breathing rate, galvanic skin response, and skin temperature) and head motion data (gyroscope and accelerometer) were gathered via wearable sensors and used as input to the model. The architecture was optimized through Hyperband-based hyperparameter tuning and showed a test accuracy of 97.62% as well as strong precision and recall across all classes. The results showed that internal physiological and motion-based signals are sufficient for robust classification of music-induced driver states, supporting the potential for real-time, sensor-driven driver monitoring systems in intelligent transportation.

Automated patient monitoring solutions greatly benefit from audio emotion classification, although the considerable variance in individual expression and interpretation of emotions poses a challenge. Current approaches often employ standard Audio Spectrogram Transformer (AST) and deep learning models such as Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN)-based networks. However, their performance can be enhanced by integrating neural architecture search techniques using swarm optimisation algorithms. In this research, we explore AST with hyperparameter optimisation for speech emotion recognition. Three deep learning architectures with optimisable τ b -block structures and variable filter numbers, i.e. 1DCNN, bidirectional LSTM (BiLSTM) and CNN-BiLSTM, are also proposed, enabling the optimisation of network depth and width. A novel Cluster Search Optimisation (CSO) algorithm is introduced. It incorporates Cluster Centroid Search, a Cluster Distance Improvement metric and reinforcement learning to dispatch different search actions based on clustering convergence and Q -learning strategies, respectively. A novel Noise Tempered K-means (NTKM) clustering model is also proposed with the integration of Gaussian-based noise insertion and cluster compactness-separation measurement, to further fine-tune the cluster centriods obtained using OPTICS clustering. CSO is used for hyperparameter and architecture search for AST and aforementioned deep networks. Attention mechanisms are also integrated with CSO-optimised networks to further enhance feature learning. We evaluate the resulting models against those devised by other optimisation algorithms across the EMO-DB, SAVEE, and TESS datasets. The empirical results demonstrate that CSO-optimised AST and CNN-BiLSTM with attention mechanisms outperform other architectures and yield favourable comparison results against those from existing state-of-the-art audio emotion classification methods. • Evolving transformer and deep networks are devised for audio emotion recognition. • A Cluster Search Optimisation algorithm is proposed to adapt hyperparameters. • It incorporates Noise Tempered K-means clustering and Cluster Distance Improvement. • The Q-learning algorithm is used to optimise search behaviours. • Our study indicates CSO-optimised deep networks’ effectiveness across datasets.

Abstract Traffic accidents are a major global health and economic concern. As such, research into understanding driving behaviors becomes essential to minimize the associated risks. Among various factors that can influence driving behaviors, listening to music while driving is a complex task that needs investigation. Music can enhance arousal and manage stress, which could potentially improve one’s driving performance. Listening to music, however, also competes for cognitive resources, increasing one’s mental workload and potentially degrading the driving ability. This study investigates the impact of listening to music with different tempos on drivers and their performance using an Artificial Intelligence (AI) approach. A total of 26 participants are subjected to three driving scenarios in a unique simulated experiment, while utilizing a motion platform. The conditions are driving while listening to slow-tempo music, and fast-tempo music, and with no music. A dataset is created by collecting data through Tobii eye-tracking glasses, Equivital sensor belts, and a software tool. This dataset is preprocessed and used to train a convolutional neural network-long short-term memory (CNN-LSTM) model. This model’s performance is optimized through hyperparameter tuning and Chi-squared feature selection, in order to maximize accuracy and minimize computation time. The model performance is compared with those from a densely layered deep learning model and several classical machine learning models. The devised CNN-LSTM model outperforms other machine learning models, achieving an average accuracy rate of 99.29% with minimal variance across multiple evaluations, demonstrating its effectiveness and consistency in classifying drivers’ behaviors under varying auditory conditions.

The extraction of fine-grained spatial-temporal characteristics for emotion classification is a challenging task owing to the subtlety and ambiguity of emotional expressions through video and audio channels. In this research, we propose an audio-visual ensemble model, comprising a two-stream 3D Convolutional Neural Network (CNN) architecture with RGB and optical flow as inputs for video emotion classification, as well as a variant of Wav2Vec2 for audio emotion recognition. The Wav2Vec2 variant integrates additional recurrent and attention layers with each transformer block to extract long- and short-term dependencies. A new Particle Swarm Optimisation (PSO) algorithm is proposed to fine-tune hyper-parameters of 3D CNNs and the enhanced Wav2Vec2, and formulate audio-visual ensemble models with the smallest sizes. It integrates a reinforcement learning (RL) algorithm, i.e. Asynchronous Advantage Actor-Critic (A3C), for search parameter and hybrid leader construction, and another RL algorithm, Proximal Policy Optimisation (PPO), for search action selection, as well as hypotrochoid and super formula-based search operations. Evaluated using audio-visual emotion datasets, our evolving ensemble model outperforms those devised by other search methods and existing state-of-the-art deep networks, significantly.

Cybersickness remains a significant barrier to the widespread adoption of virtual reality (VR) and enhancing passenger comfort and safety in autonomous vehicles (AVs). Predicting and mitigating cybersickness is crucial for creating safer and more comfortable VR experiences. This study introduces a one-dimensional convolutional neural network-gated recurrent unit-attention (1CNN–GRU–Attention) model trained on galvanic skin response (GSR) data collected from 24 participants in VR under various weather conditions. Participants reported cybersickness severity on a 10-point scale every minute, providing real-time labels for evaluation. The model’s performance was compared with that of k-nearest neighbours (KNN) and support vector machine (SVM) classifiers in binary (two-class) and multi-class (four-class: none, low, acute, high) classification settings, both with and without class balancing using the synthetic minority oversampling technique (SMOTE). Evaluation metrics included accuracy (ACC), Matthews correlation coefficient (MCC), and unified performance metric (UPM), with performance assessed via 5-fold cross-validation and statistical t-tests. Results show that the 1CNN–GRU–Attention model significantly outperforms both SVM and KNN across all metrics. Applying SMOTE improved binary classification accuracy from 75.15% to 86.94% in binary classification and from 48.77% to 64.63% in multiclass classification, with notable improvements in MCC. These findings highlight the importance of balanced data and the effectiveness of the 1CNN–GRU–Attention model in assessing cybersickness severity, advancing physiological monitoring methods for VR, and facilitating broader VR adoption.

Simulator sickness poses a significant challenge to the effectiveness of training programs in virtual environments. Early diagnosis of this syndrome can significantly prevent its onset and enhance user safety by enabling the management of symptoms. In our study, thirty-one participants were exposed to a recorded video of a flight in the Helimond simulator. Simulator sickness was then quantified using the Simulator Sickness Questionnaire, a Forced Binary Choice method, and an Equivital sensor. We analyzed the correlations between physiological signals and self-assessment scores, as well as evaluated the performance of various machine learning algorithms in predicting simulator sickness. The results indicated a strong correlation between SSQ scores and forced choice responses. Among the machine learning algorithms tested, Random Forest outperformed the others, achieving the highest accuracy (0.937) and AUC (0.932). Decision Trees followed in performance, while Logistic Regression showed the worst performance. Furthermore, based on our dataset, heart rate and Inter-Beat Interval were identified as the most significant factors for predicting simulator sickness. Our approach emphasizes the importance of instant assessment and prediction of simulator sickness, providing a simple yet effective method to improve the evaluation process. This method facilitates prompt evaluation and proactive symptom prevention, benefiting virtual environment design and user safety.

This paper explores the application of driving simulator across multiple sectors, highlighting the challenges associated with refining motion cueing algorithms (MCA) through model predictive control (MPC). Through these platforms, drivers can simulate the sensation of motion. The implementation of MPC-based MCA, while advantageous for its precision in controlling motion simulations, encounters significant hurdles such as the requirement for highly accurate system models and the extensive parameter tuning needed for each specific control scenario. These issues create a critical gap in achieving optimal simulation fidelity and efficiency with lower computational time, necessitating a novel approach to improve the MCA domain. Addressing these challenges, the study pioneers the use of Deep QNetwork (DQN), a reinforcement learning (RL) technique, to optimise the horizons of MPC within the MCA domain. This innovation is significant as it introduces, for the first time, a method to dynamically adjust MPC-based MCA horizons using DQN, which learns through continuous interaction with the simulation environment. This approach is set to overcome the limitations of traditional meta-heuristic optimisation methods, such as the Grasshopper Optimisation Algorithms (GOA) and Butterfly Optimisation Algorithms (BOA), by offering a more flexible and adaptable solution. The overarching goal of this research is to minimise the system's cost function by maximising a reward function that encompasses key performance metrics such as specific force sensation, angular velocity, linear displacement, linear velocity, and angular displacement. By integrating DQN into the MPC-based MCA environment, this study demonstrates a faster computational running time and improves the precision and efficiency of the simulations. This innovative approach enhances the efficiency of the horizon determination process, showcasing promising implications for the MCA domain's advancement.

Cognitive load theory suggests that overloading of working memory may negatively affect the performance of human in cognitively demanding tasks. Evaluation of cognitive load is a difficult task; it is often assessed through feedback and evaluation from experts. Cognitive load classification based on Functional Near-InfraRed Spectroscopy (fNIRS) is now one of the key research areas in recent years, due to its resistance of artefacts, cost-effectiveness, and portability. To make fNIRS more practical in various applications, it is necessary to develop robust algorithms that can automatically classify fNIRS signals and less reliant on trained signals. Many of the analytical tools used in cognitive sciences have used Deep Learning (DL) modalities to uncover relevant information for mental workload classification. This review investigates the research questions on the design and overall effectiveness of DL as well as its key characteristics. We have identified 38 studies published between 2011 and 2022, that specifically proposed Machine Learning (ML) models for classifying cognitive load using data obtained from fNIRS devices. Those studies were analyzed based on type of feature selection methods, input, and DL model architectures. Most of the existing cognitive load studies are based on ML algorithms, which follow signal filtration and hand-crafted features. It is observed that hybrid DL architectures that integrate convolution and LSTM operators performed significantly better in comparison with other models. However, DL models especially hybrid models have not been extensively investigated for the classification of cognitive load captured by fNIRS devices. The current trends and challenges are highlighted to provide directions for the development of DL models pertaining to fNIRS research.

The recent breakthrough of deep learning based generative models has led to the escalated generation of photo-realistic synthetic videos with significant visual quality. Automated reliable detection of such forged videos requires the extraction of fine-grained discriminative spatial-temporal cues. To tackle such challenges, we propose weighted and evolving ensemble models comprising 3D Convolutional Neural Networks (CNNs) and CNN-Recurrent Neural Networks (RNNs) with Particle Swarm Optimization (PSO) based network topology and hyper-parameter optimization for video authenticity classification. A new PSO algorithm is proposed, which embeds Muller's method and fixed-point iteration based leader enhancement, reinforcement learning-based optimal search action selection, a petal spiral simulated search mechanism, and cross-breed elite signal generation based on adaptive geometric surfaces. The PSO variant optimizes the RNN topologies in CNN-RNN, as well as key learning configurations of 3D CNNs, with the attempt to extract effective discriminative spatial-temporal cues. Both weighted and evolving ensemble strategies are used for ensemble formulation with aforementioned optimized networks as base classifiers. In particular, the proposed PSO algorithm is used to identify optimal subsets of optimized base networks for dynamic ensemble generation to balance between ensemble complexity and performance. Evaluated using several well-known synthetic video datasets, our approach outperforms existing studies and various ensemble models devised by other search methods with statistical significance for video authenticity classification. The proposed PSO model also illustrates statistical superiority over a number of search methods for solving optimization problems pertaining to a variety of artificial landscapes with diverse geometrical layouts.

In this research, the effective indexes in the cold roll-forming procedure that can affect the energy utilization and required maximum torque of the forming line have been investigated and optimised using NSGA-II and type-2 fuzzy neural networks. The effective parameters were strip thickness, bending angle increment, flange width, inter-distance between the rolling stands and bending radius. Recently, traditional machine-learning applications have been employed in roll-forming technology for different purposes, such as prediction of web-warping, energy efficiency, and strip breakage. A finite element model (FEM) roll-forming procedure was utilised to extract the appropriate datasets for this study. type-2 fuzzy neural network (T2FNN) is not employed in cold roll-forming technology. In this study, T2FNN is employed to imitate the dynamic model of the cold roll-forming procedure to estimate energy consumption and torque. In the following, the NSGA-II extracts the optimal cold roll-forming procedure parameters to reach the lowest energy consumption and maximum torque as the process’s most economical solution. The proposed model is designed and developed under MATLAB software. Fourteen optimal solutions are suggested based on the extracted Pareto-Front of the NSGA-II using the T2FNN of the process.Note to Practitioners—In this research, a hybrid machine-learning method is designed and developed with a combination of T2FNN and NSGA-II to extract the optimal roll-forming procedure parameters to reach the lowest usage of energy as well as the lowest requirement of maximum torque. Implementing the proposed method in cold roll-forming production lines can save millions of dollars in massive factories by reducing the usage of energy and the emission of greenhouse gases. The proposed algorithm is quite fast. Then there is no need for high graphic computers for real-time implementation of the proposed method.

The motion cueing algorithm (MCA) enables lifelike motion in simulators resembling real driving. Regenerated motions must adhere to workspace constraints. Vehicle motion signals (linear acceleration, angular velocity) are generated in a simulated vehicle environment utilised in MCA for motion cues. These signals are categorised into levels (slow, medium, fast) based on frequency and amplitude. The commonly used MCA, the classical washout filter, is typically fine-tuned using worst-case (fast-driving) scenarios to meet the simulator’s requirements across various situations. However, this approach reduces the MCA’s effectiveness in handling slower driving scenarios, resulting in conservatism in platform workspace usage for slow and medium driving. Consequently, a noticeable motion sensation error arises between real vehicle drivers and motion simulator users. To rectify this issue, a novel neural network-based MCA is developed in this study. Three distinct classical washout filters are meticulously tuned to cater to slow, medium, and fast driving scenarios. These filters generate precise motion cues for simulator users at corresponding levels of driving scenarios. The neural network-based MCA is constructed using the synthesised signals from these classical washout filters. This proposed method is thoroughly validated through the utilisation of MATLAB software. In direct comparison with the standard classical washout filter, the proposed MCA significantly reduces the motion sensation error, enriches motion fidelity, and optimises the utilisation of the simulator’s workspace.

Semantic segmentation is vital for many emerging surveillance applications, but current models cannot be relied upon to meet the required tolerance, particularly in complex tasks that involve multiple classes and varied environments. To improve performance, we propose a novel algorithm, neural inference search (NIS), for hyperparameter optimization pertaining to established deep learning segmentation models in conjunction with a new multiloss function. It incorporates three novel search behaviors, i.e., Maximized Standard Deviation Velocity Prediction, Local Best Velocity Prediction, and n -dimensional Whirlpool Search. The first two behaviors are exploratory, leveraging long short-term memory (LSTM)-convolutional neural network (CNN)-based velocity predictions, while the third employs n -dimensional matrix rotation for local exploitation. A scheduling mechanism is also introduced in NIS to manage the contributions of these three novel search behaviors in stages. NIS optimizes learning and multiloss parameters simultaneously. Compared with state-of-the-art segmentation methods and those optimized with other well-known search algorithms, NIS-optimized models show significant improvements across multiple performance metrics on five segmentation datasets. NIS also reliably yields better solutions as compared with a variety of search methods for solving numerical benchmark functions.

Vehicle trajectory prediction is essential in ensuring the safe and efficient operation of advanced driver assistance systems (ADAS) and autonomous vehicles (AVs), as it enables highly efficient collision avoidance, path planning, and traffic control. However, existing models for vehicle trajectory prediction predominantly focus on limited driving scenarios, resulting in limited applicability. To address this limitation, we present a novel vehicle trajectory prediction approach that employs a Convolutional Long Short-Term Memory (CNN-LSTM) model, incorporating simulated environments and vehicle dynamic time series data, including longitudinal, vertical, and latitudinal position and acceleration. Our approach is distinguished by its ability to handle diverse urban driving scenarios, such as highways, roundabouts, intersections, and turns, which enhances its applicability and generalizability. We experimented and collected vehicle data from 17 drivers using a stationary driving simulator and the Euro Truck Simulator software. For the model implementation and validation, we utilized Python 3.9 and Google Colab, as well as the Scikit-learn library for Deep learning algorithms. The proposed CNN-LSTM model leverages a convolutional layer to learn local patterns and an LSTM layer to capture long-term temporal dependencies, improving performance in predicting vehicle trajectories. The experimental results demonstrate that the CNN-LSTM model provides more accurate predictions for longitudinal and lateral positions compared to traditional vehicle trajectory prediction methods that employ LSTM and Recurrent Neural Network (RNN). This research contributes to developing robust and reliable vehicle trajectory prediction systems vital for ADAS and AVs' safe and efficient operation. The proposed approach broadens the applicability of trajectory prediction models, enabling better-informed decision-making in various driving conditions and ultimately improving road safety and efficiency in the rapidly evolving field of autonomous transportation.

Autonomous vehicles, commonly known as self-driving cars, are rapidly gaining popularity due to their numerous advantages, such as reducing traffic, pollution, and emissions while increasing safety, convenience, and transportation connectivity. In order to accurately track the motion signal, these vehicles are now utilising advanced control techniques, such as model predictive control (MPC). However, the efficiency of MPCs heavily relies on properly tuning their weights. The primary function of the MPC is to recalculate the optimal values for the vehicle control commands, such as desired speed, steering angle, etc., while considering the dynamic model of the autonomous vehicle. The existing linear MPC models cannot reach higher efficiency because of using fixed weights without considering the error. This paper introduces a novel approach for developing an MPC model with a time-varying weights algorithm for autonomous vehicles. The study aims to minimise motion tracking errors such as lateral position and yaw angle errors. Relevant MPC weights are calculated online using fuzzy logic-based units considering the lateral position and yaw angle errors. The proposed linear time-varying MPC was designed and developed using MATLAB software, resulting in improved motion tracking performance with 31.62% and 20.89% reduction of the root means square error of lateral position and yaw angle.

The issue of cognitive distraction during driving has been identified as a major cause of road accidents. Detecting cognitive distraction in real-time can be a valuable strategy for preventing accidents. In this study, a novel approach is presented for the purpose of detecting cognitive distraction in real-time using artificial intelligence while taking into account eye-tracking and head movement data, combined with driving performance measures. This methodology involved collecting data from participants in a driving simulator, on a motion platform, while they performed a cognitive task as well as a control driving scenario. The data collected included eye-tracking data, head movement data, driving performance measures, and subjective ratings of distraction. To develop an accurate model for detecting cognitive distraction, a Deep Feedforward Neural Network (D-FFNN) model was employed while considering binocular gaze direction, pupil diameter, orientation of each eye, head rotational velocities, and head acceleration. The developed model was trained using the collected data and achieved an accuracy of 96.09% in detecting cognitive distraction. The results of our study demonstrate the effectiveness of the proposed method in identifying cognitive distraction in real-time. Also, the accuracy of this model was compared with other AI based classification algorithms. The proposed method has significant implications for preventing vehicle accidents caused by cognitive distraction. The proposed method can be integrated into existing driver-assistance systems to alert drivers and assist them in returning their focus to the road.

Automatic segmentation of salient objects in real-world images has gained increasing interests owing to its popularity in diverse real-world applications, such as autonomous driving, medical diagnosis, aviation security, and underwater surveillance. In this research, we propose Firefly Algorithm (FA)-enhanced evolving ensemble deep networks for semantic segmentation and visual saliency prediction. An improved FA model is proposed to optimize network hyper-parameters. Specifically, it employs mutation operators and a neighbouring search strategy with granular search steps to establish search intensification. It also emphasizes search diversification by adopting multiple dynamic hybrid leaders and diverse adaptive sine and cosine search trajectories in full and randomly selected sub-dimensions to overcome stagnation. Because of its competent segmentation performance, DeepLabV3+ is fine-tuned using transfer learning with FA-based hyper-parameter identification. We optimize the learning rate, momentum and weight decay of the transfer learning network. A number of optimized DeepLabV3+ networks with distinguishing learning configurations are yielded. An ensemble model is subsequently constructed by incorporating three optimized base networks to further strengthen segmentation performance. Evaluated using diverse challenging semantic segmentation and saliency prediction tasks using underwater and medical image data sets, our evolving ensemble deep network illustrates significant superiority over other state-of-the-art deep networks and existing studies. The proposed FA model also outperforms other search methods in solving diverse mathematical landscapes with statistical significance.

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.

In the absence of physical motion, people sometimes experience the illusory sensation of self-motion which is known as vection. Vection research could contribute to the improvement of the fidelity of simulators as vection and presence appear to be positively related. However, when utilizing virtual reality technology for simulators, visually-induced motion sickness (VIMS) in the form of Cybersickness (CS) sometimes co-occurs with the experience of vection. Nonetheless, the relationship between vection and CS is not evident. Past research mainly investigated the relationship between the vection and CS using environments with a certain level of ecological relevance. Herein we investigated whether CS negatively affects the relationship between vection and presence across different virtual environments with varying levels of ecological relevance. We immersed twenty-nine participants visually and audibly in virtual environments and after each trial participants reported their vection intensity, CS, and presence. Our results showed that the relationship between vection intensity and presence was unaffected by CS. We conclude that the relationship between vection and presence is unaffected by CS with low levels of discomfort.

Advances in utilisng motion simulators for skill training and related applications have yielded numerous benefits, such as safety, availability, and serviceability, environmentally friendly, and economically beneficial. To give simulator users a sense of realistic feeling of driving, an accurate motion cueing algorithm (MCA) is essential, in order to respect the simulator platform limitation and avoid motion sickness. The use of Model Predictive Control (MPC) in MCA designs leads to respecting the constraints and considering the future dynamic behaviors of the simulator. However, the tuning process of the MPC prediction horizon and control horizon still need to be improved. These horizons are normally selected manually by the designer. Previous studies on meta-heuristic algorithms produce a large prediction horizon with a heavy computational load or a small prediction horizon that sacrifices the stability and accuracy of the simulator system. In this study, the Grasshopper Optimization Algorithm (GOA) is adopted to yield optimal prediction and control horizons in MPC-based MCA models. The results are compared with those from the Butterfly Optimization Algorithm (BOA) and Genetic Algorithm (GA) in terms of sensation error and computation time. The GOA technique depicts the fastest process time to promptly detect proper MPC horizons. It does not affect the simulator's efficiency in utilising the workspace, as evidenced by the correlation coefficient and root mean square error between sensation from a real-world vehicle and the simulator.

Driving simulators are utilized for many applications including basic driver training, human factor studies, human-machine interaction, and vehicle prototyping in automobile industries. The main purpose of using driving simulator is to provide realistic driving experience. Since simulator platforms have physical limitations, Motion Cueing Algorithms (MCAs) are used to generate driving sensation for the simulator user while considering the simulator's physical and dynamical constraints. When using a model predictive control (MPC)-based MCA, the principle of MPC is leveraged to predict an optimized future behavior of the simulator where a series of control actions is developed across a defined future horizon using the explicitly specified process model. Corresponding to the pre-positioning or time-varying reference MPC, it is crucial to predict the future vehicle motion signals for the simulator accurately. The existing methods for predicting vehicle motion signals do not excel in predicting time-series of a long sequence due to the missing feedback loop or limited memory size. To address this issue, the Long Short-Term Memory (LSTM) model is developed to predict motion signals using Python. The performance of LSTM is compared with those from different traditional methods using several measurements criteria, which include the root mean squared error (RMSE), mean absolute error (MAE), and Pearson’s correlation coefficient (r). The results indicate that LSTM outperforms RNN by producing more accurate motion allowing the MCA to deliver realistic motion sensations, the LSTM model can be employed in a wide range of applications including autonomous vehicles trajectory prediction, and other prediction problems.

A Motion Cueing Algorithm (MCA) is an algorithm that transforms the movement of a simulated vehicle into movement that can be reproduced with a Motion Simulator (MS) while respecting its physical constraints. Crucially, MCAs aim to provide a realistic driving experience to simulator users. Adaptive MCAs are a type of MCA that is flexible, computationally light and designed to adjust behaviour based on the current MS state. However, adaptive MCAs require extensive manual tuning which is difficult, time consuming and a sub-optimal process. This paper presents an optimisation-based method using Particle Swarm Optimisation (PSO) for automatically tuning the free parameters of the Adaptive Gradient Descent-based MCA (AGDA) while accounting for MS physical constraints and motion fidelity. The cost function of the tuning routine considers the RMSE, correlation coefficient (CC) and error oscillation of the motion sensation signals of the MS driver with respect to those of the simulated vehicle driver. The displacement, velocity and acceleration of the MS are also considered. The proposed method was implemented using MATLAB and Simulink and the effectiveness of the approach was tested with a Rigs of Rods simulation of a ground vehicle. Compared to the existing manually tuned AGDA, the optimally tuned AGDA obtained with the proposed method performs 32.6% and 23.7% better in terms of RMSE and CC of the motion sensation signals, respectively. The observed performance improvement and moderate computational load of the AGDA renew its relevance in the context of modern MCAs.

This paper proposes transferred initialization with modified fully connected layers for COVID-19 diagnosis. Convolutional neural networks (CNN) achieved a remarkable result in image classification. However, training a high-performing model is a very complicated and time-consuming process because of the complexity of image recognition applications. On the other hand, transfer learning is a relatively new learning method that has been employed in many sectors to achieve good performance with fewer computations. In this research, the PyTorch pre-trained models (VGG19_bn and WideResNet -101) are applied in the MNIST dataset for the first time as initialization and with modified fully connected layers. The employed PyTorch pre-trained models were previously trained in ImageNet. The proposed model is developed and verified in the Kaggle notebook, and it reached the outstanding accuracy of 99.77% without taking a huge computational time during the training process of the network. We also applied the same methodology to the SIIM-FISABIO-RSNA COVID-19 Detection dataset and achieved 80.01% accuracy. In contrast, the previous methods need a huge compactional time during the training process to reach a high-performing model. Codes are available at the following link: github.com/dipuk0506/Spina1Net

The illusory sensation of self-motion is defined as vection. Vection research can help enhance Virtual Reality applications and improve simulator fidelity as vection appears to be a desired sensation in motion simulators. The experience of vection can be modulated by cognitive factors and potentially personal traits, such as the vividness of imagination. Previous research investigating the relationship between auditory vection and kinesthetic imagery presented conflicting findings. However, the relationship between visually-induced vection and imagination has not been investigated. Herein we investigated the relationship between kinesthetic imagery and unimodal visual and bimodal visual-auditory vection. Twenty-nine participants were visually and audibly immersed in virtual environments with varying degrees of ecological relevance wherein they reported on their vection experience. No differences were found for vection intensity and latency measures between participants with high and low kinesthetic imagery. We conclude that imagery does not appear to play a role in the experience of visually-induced vection.

Vection is commonly defined as the illusory sensation of self-motion. Research on vection can assist in improving the fidelity of motion simulators. Vection can be influenced through top-down factors, such as attention, but previous research on the effect of a secondary task on vection presented conflicting findings. We investigated the effect of a visual discrimination reaction time task on vection. Twenty-nine participants were visually and audibly immersed in virtual environments with different levels of ecological relevance wherein they used a joystick to continuously report on their vection experience. In contrast to previous research, our results showed no significant effect of a secondary task on vection measures nor an effect of sensory cues and environment context on secondary task performance. We conclude that participants’ ability to report their vection experience was unaffected whilst performing a visual attention reaction time task.

Self-driving vehicles, also known as Autonomous Vehicles (AVs), are steadily becoming very popular due to their huge benefits. They can improve safety, convenience and transport interconnectivity as well as reduce congestion, pollution and emissions. The generation of the comfort motion signal for AVs passenger via the calculation of accurate motion cues with lower motion discomforts is important to promote the adoption of Avs in society. Model predictive control (MPC) is currently used in AVs for tracking the motion signal with good accuracy. However, the higher efficiency of MPC is directly related to the right setting of the weights. In addition, the tracking of time-varying longitudinal velocity is not possible without using linear time-varying (LTV) MPC. In this study, an LTV MPC system is designed and developed as a highly efficient motion tracking mechanism for AVs to reduce the motion tracking error and motion discomfort. In addition, biogeography-based optimisation (BBO) is employed to determine the optimal weights of the LTV MPC controller, which further reduces the motion tracking error and increases the motion comfort for users. The empirical study demonstrates that a BBO-tuned LTV MPC controller decreases the mean square error of motion tracking by 4.79% as compared with that of a manually-tuned version. Moreover, the mean square errors of the lateral deviation and relative yaw decrease by 91.22% and 19.14% as compared with those from a manually-tuned LTV MPC counterpart, respectively.

Motion signal should be generated via the AV control system targeting the maximum motion comfort for the users. Nonlinear model predictive control (MPC) is recently used in AVs to achieve this critical task. However, nonlinear MPC has lots of hyperparameters, including weights and MPC horizons, that should be tuned systematically to reach the system’s high efficiency. The energy usage and motion comfort have a direct relationship. The generation of high-fidelity motion cues for AV users leads to higher energy usage. Hence, there is a need for the use of a multi-objective optimisation technique to tune the weights wisely to satisfy the appropriate energy usage and motion comfort for the AV users. In this study, multi-objective NSGA-II is employed, for the first time, to tune the weights of a nonlinear MPC-based controller in AVs. The proposed method is designed and developed using MATLAB/SIMULINK software. The simulation results show minimum energy usage by generation of smooth motion signals, delivering maximum comfort to AV users.

The autonomous vehicle (AV) has been studied by many researchers recently because of its valuable points in transportation, aviation, military, smart city, and aerospace. The model predictive control (MPC) is employed to track the artificial intelligent regenerated motion signals with higher accuracy than other error-and model-based controllers as it can consider the constraints of the system in extracting the optimal solution. However, the accuracy and applicability of the MPC rely on the MPC horizons, including prediction and control horizons. The higher prediction horizons mean a higher computational load of the system, which reduces the real-time applicability of the system. On the other hand, a higher prediction horizon increases the system’s stability in facing abrupt motion signals. In addition, higher control horizons mean more dexterity in the system facing an unknown situation. On the other hand, a longer control horizon increases the computational load of the system exponentially. This study employs particle swarm optimisation (PSO) to extract the optimal MPC horizons considering the accuracy and computational load. The cost function is defined to increase the accuracy of the longitudinal time-varying velocity tracking, decrease the lateral deviation, decrease the relative yaw angle and decrease the computational load of the system. It should be noted that the lateral deviation and relative yaw angle are extracted using the vehicle four wheels dynamic model in order to evaluate the AVs’ passenger motion comfort. The proposed method is designed and developed under MATLAB/Simulink. The extracted optimal MPC horizon is compared with some other arrangements of the MPC horizons to prove the efficiency of the proposed method compared with the trial-and-error method.

The motion signals are generated for a simulator user based on the visual understanding of the environment using virtual reality. In this respect, a motion cueing algorithm (MCA) is employed to reproduce the motion signals based on the real driving motion scenarios. Advanced MCAs are required to predict precise driving motion scenarios. Nonetheless, investigations on effective methods for predicting the driving motion scenarios accurately are limited. Current state-of-the-art studies mainly focus on the averaged motion signals from several simulator users pertaining to a specific map or from feedforward neural network and non-linear autoregressive. The existing methods are unable to yield precise predictions of the driving scenarios. In this research, the echo state network and long short-term memory models are employed for the first time in MCA to forecast the driving motion signals. Our evaluation proves the efficiency of our proposed methods in comparison with existing methods.

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.

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.

The conflicts between the perceived sensation of the different sensory systems can cause adverse effects which is known as motion sickness (MS) and the side effects of MS include nausea, dizziness, stomach awareness etc. Virtual reality sickness (also called Cybersickness or visually induced motion sickness (VIMS)) happens during exposure to a virtual environment when senses transfer conflicting sensation signals to the brain. The symptoms of Cybersickness are similar to motion sickness symptoms. The adverse effects of this common phenomenon can negatively affect the training outcome and benefits using VR, undermine users’ health and usefulness of simulators as it involves health risk and contributes to the increase of dropout rates. Therefore, to mitigate these issues, MS should be detected and measured. The primary objective of this study is to subjectively and objectively detect and quantify cybersickness level using a helicopter simulator. This study has also investigated the change in cybersickness self-reported scores in different weather conditions such as clear and stormy. Simulator sickness questionnaire (SSQ) has been employed for subjective scoring. This research also aimed to correlate SSQ scores with physiological data such as Galvanic Skin Response (GSR). The findings demonstrated that the SSQ total score (TS) has increased significantly from clear weather to stormy for the participants. There is also a positive correlation found between the change in TS and the amount of GSR but not significant.

People perform most of their activities while being in an upright position. Nonetheless, there are some circumstances where they are required to adapt to different positions other than the upright position as in air manoeuvres and sport gymnastics. In these unexpected scenarios, the physiological signals are likely to change which can affect their performance. While some studies investigated the correlation between physiological signals and different body positions, to our best knowledge, these studies were limited to a rotating chair (1 or 2 degree of freedoms). Here, we investigated and evaluated human physiological responses (such as pupil diameter, skin temperature, heart rate, and breathing rate) to different seated positions including seated supine, seated side, seated inverted, and seated upright using Universal Motion Simulator (UMS), a 6 degree of freedom simulator with the most realistic acceleration and motion sensation. Open loop acrobatic flight motion sensation for the 11 participants were created and accompanied with a series of pre- and post-questionnaires to subjectively assess the physical wellbeing of each participant. The results of the study based on the objective assessment of collected physiological data showed that the mean heart rate decreases during an inverted position (82 beats per minutes bpm) and increased by an average of 7 beats per minute in an upright position 89.5 bpm. Moreover, the mean breathing rate in an upright position was 18.3 respirations per minutes (rpm) which is higher than mean breathing rate in the side position 19.8 rpm. Furthermore, it was found that the mean pupil diameter (PD) in an upright position was 4.33 mm which is higher compared to other positions. Independent from the motion scenarios and body positions, the Skin Temperature kept increasing which might be because of excitement and other emotional factors.

The purpose of the motion cueing algorithm is to reproduce the motion sensation for the drivers considering the physical limitations of this platform. Newly, the model predictive control-based methods have been used in motion cueing algorithms. This control respects the constraints and considers the future dynamics of the model for finding the optimum solution to the problem. However, the tuning of the weights for model predictive control is incredibly challenging to reduce the motion sensation errors. In this paper, a whale optimisation algorithm is used to gain the optimised weights of the model predictive control. The weights are optimized to reduce the cost function which is defined based on the motion inputs, input rates, and outputs. The recalculated weights via the whale optimisation algorithm should consider the limitations of the applications such as maximum tolerated error of motion sensation via the motion platform user, maximum linear and angular displacements, and maximum linear velocity. The proposed method is simulated seven times to demonstrate the accuracy and repeatability of the algorithm. The results show that the whale optimisation algorithm reaches the best solution quickly with minimised motion sensation error compared with the genetic algorithm.

The usage of autonomous vehicles in the transportation sector can achieve the objective of a safe environment. To increase riding comfort in an autonomous vehicle, one main challenge is to implement motion scenarios according to the passenger’s driving behaviours. This leads to customization of the driving style of an autonomous vehicle according to the preference of its passenger. The main disadvantage of the current autonomous vehicles is the regeneration of driving motion signals without taking into consideration the comfort/discomfort of the passengers according to their driving behaviours and preferred driving styles such as acceleration/deceleration rate and steering styles. In this paper, a nonlinear autoregressive network model is developed and trained based on the generated motion scenarios of the passenger and the position of the autonomous vehicle, in order to predict and replicate the motion signals based on the passenger’s driving behaviours. The MATLAB toolbox is used to train the network and forecast the motion signals. The results show the usefulness of the proposed method in terms of a higher shape similarity level and a lower mean square error rate between the actual and forecasted motion signals. These regenerated motion signals can increase the riding comfort of autonomous vehicle’s passengers as it is able to imitate the behaviour of the passengers.

The motion simulator platform can be used in many sectors, including transportation, aviation, and education. The motion cueing algorithm (MCA) is the main component of the motion simulator with the responsibility of motion cues regeneration while respecting the motion simulator’s joint limitations. Recently, a model predictive control (MPC) method has been introduced in the MCA, which is able to extract an optimum input signal within the model constraints. The washout speed of the end-effector using the existing MPC-based MCA model is not considered because the integral of linear displacement of the platform is omitted as an output. As a result, the motion simulator platform returns to the neutral position without the consideration of the motion behavior. In this study, the integral of the end-effector linear displacement is consider inside the MPC-based MCA model to select the best washout speed of the end-effector. Moreover, a grey wolf optimizer is utilized to identify the best MPC weighting indexes, in order to increase the model efficiency for both existing and proposed models. The proposed method outperforms the MPC-based MCA model in producing better regeneration of the motion cues. It yields a higher correlation coefficient and a lower root means square error between the motion sensation signal pertaining to the real vehicle and motion simulator platform users.

The motorized antenna mechanism is the central part of healthy satellite communication using a real-time motion tracking system. Typically, parallel manipulators are employed in the space industry for tracking the satellites with the antenna mounted on the end-effector. However, the workspace limitations of the parallel manipulators are highly restricted in terms of the end-effector’s linear and angular motions compared with the serial manipulators. In order to take the privilege of a serial manipulator advantages, the antenna is mounted on the ABB irb6600 manipulator’s end-effector while only generating 2-degree-of-freedom (2-DoF) motions for simplification. Unfortunately, the current system cannot track the satellite motion accurately as it cannot generate the smooth motion while following a path, especially when the end-effector is in the vertical position. In this study, a real-time motion control tracking system using the 6-DoF motion of the ABB irb6600 robot is designed and developed using TCP/IP communication technique between MATLAB and IRC5 controller aiming to accurately track the satellite path with the ability to decrease the jerkiness of the motion. The proposed method has been tested in simulation environment using the small prototype of the ABB robot (irb120) while Simulink Desktop Real-Time and RoboStudio are used. The inertial measurement unit (IMU) sensor is used to prove the tracking accuracy of the proposed method and elimination of the jerky motions using the proposed algorithm.

The real-time motion satellite tracking mechanism of an antenna, allowing communication to and from the satellite is critical for space applications. Mostly, parallel-based mechanisms are employed in space fields for motorizing the antenna due to the cost-effectiveness, high stiffness, easiness of inverse kinematic solution, and high reachable acceleration. Unfortunately, the angular displacements of the end-effector using parallel-based mechanisms are highly limited due to the existence of the passive joints. More recently, serial-based mechanisms are being used to motorize the antenna for tracking the satellite motions in larger horizons compared with parallel-based mechanisms. The inverse kinematic solution complexity, the iso-metric configurations of the joints for a specific position of the end-effector, and highly advanced controller mechanisms are the main difficulties of serial-based mechanisms that should be considered in their implementations. The existing system is using a traditional proportional–integral–derivative (PID) controller along with the kinematic modelling which can cause tracking error, inaccuracies and consequently loss in receiving data from satellite. In this study, the ABB irb120 robot is employed as a serial-based mechanism with the attached antenna to efficiently track the low-, medium-, and high-altitudes orbiting satellites. The inverse kinematic model of the proposed robot has been incorporated to extract the robot joints’ configurations with a combination of the new fuzzy logic controller compared with a PID controller to increase the motion tracking performance. The simulation study is conducted using MATLAB/SimMechanic to model the mechanism with consideration of the constraints. The results prove satellite motion can be tracked with higher accuracy using the fuzzy logic controller compared to the existing PID controller.

The hexapod manipulator is the most common motion platform, which is widely used as a simulation-based motion platform (SBMP). As the hexapod manipulator has a limited workspace, it is not physically possible to regenerate the real vehicle motion signals using the SBMP. The motion cueing algorithm (MCA) is responsible for regenerating a realistic vehicle motion sensation for the user when the SBMP operates within its physical and dynamical limitations. Recently, model predictive control (MPC) has been introduced to extract the optimal input motion signals while considering the SBMP limitations in the Cartesian coordinate space, which leads to a linear time-invariant (LTI) MPC-based MCA methods. Unfortunately, the existing LTI MPC-based MCA methods are still not able to consider the parameters of the SBMP's hexapod mechanisms inside their models. In general, the current studies only consider the constraints in the Cartesian coordinate system of the hexapod mechanism, instead of its design parameters. This consideration results in a poor usage of the hexapod workspace due to the conservative assumptions; consequently, the SBMP users do not experience realistic motions. The main contribution of this article is to take the SBMP's physical limitations into account in the MPC model such that more precise motion cues can be extracted for the users. A linear time-varying (LTV) MPC-based MCA method is designed for the first time in this article to consider the parameters of the hexapod mechanism in the MPC model. The proposed model (LTV MPC-based MCA) is validated using the MATLAB software, and the results depict better motion sensation with more accurate motion signals as compared with those from the existing LTI MPC-based MCA methods.

The Motion Cueing Algorithm (MCA) oversees regenerating the motion feeling of the real vehicle for the simulation-based motion platform (SBMP) within the physical limitations. Model Predictive Control (MPC) is recently employed as an MCA, which is called MPC-based MCA due to the consideration of the plant's boundaries in finding the optimal input signal. The computational load of the MPC directly relates to the control horizon and prediction horizon of the MPC. In this paper, a new optimisation method using butterfly optimisation algorithm is developed to find the optimal control horizon and prediction horizon of MPC-based MCA. The proposed method reduces the time of the tuning process of the MPC-based MCA, which is usually carried out via trial-and-error and genetic algorithm methods. Also, the trial-and-error method increases the motion sensation error and insufficient usage of the SBMP. The model is validated using MATLAB simulation environment, and the outcomes show that the developed butterfly optimisation algorithm will lead better motion sensation with less wrong motion signals and low computational burden compared with the trial-and-error and genetic algorithm method.

Parallel manipulators are recently used in most motion simulation laboratories as they can easily generate six degrees of freedom motion. Recently, fuzzy logic-based adaptive motion cueing algorithms (MCAs) have been employed to reproduce the motion signals. The usage of fuzzy logic-based adaptive MCA reduces the movement sensation error between the real vehicle and the simulation-based motion platform (SBMP) user considering the end-effector limitations in Cartesian space.. In this paper, a new fuzzy logic-based adaptive MCA is introduced to generate motion signals based on the joints' limitations and the movement sensation error between the real vehicle and the SBMP user. Considering the parallel SBMP, joint limits enhance the ability of the introduced adaptive motion cueing algorithm to generate more accurate movement feelings with high fidelity. The simulation results prove that the proposed adaptive motion cueing algorithm can effectively use the large workspace of the parallel SBMP whilst reducing the motion sensation error.

Driving motion simulators are widely used for their reliable, safe and cost-effective abilities to replicate real vehicle driving experience for simulator drivers in virtual environment. As all motion simulators have physical limitations, Motion Cueing Algorithm (MCA) is the most necessary algorithm for transformation of the real vehicle's linear and rotational motions to motion platform aiming to regenerate realistic driving sensation. Model Predictive Control (MPC)-based MCA has recently become one of the most popular MCAs. Scaling and limiting is an important unit of MPC-based MCA to reduce the amplitude of motion signal uniformly aiming to improve the realism of produced motion within the physical limitations of workspace. The current implementations of MPC use a basic form of scaling. In this paper, a novel MPC-based MCA is developed using an optimised nonlinear scaling unit and Genetic Algorithm (GA). The goal is to reproduce accurate motion sensation for the motion simulator drivers as close as possible to real vehicle within the platform's physical constraints. This is achieved via a polynomial scaling unit which is optimized by GA. The aim is to overcome the disadvantages associated with the tuning based on trial-and-error for MPC-based MCA scaling unit which is the main cause of inefficient platform workspace usage and motion sensation error between real vehicle driver and motion simulator driver. The proposed optimization-based method enhances the function of the nonlinear scaling units by considering some important factors such as the motion simulator's physical constraints and motion sensation error between the drivers in a real vehicle and a motion simulator platform. The proposed method is verified via simulation results which show the superiority of the optimised nonlinear scaling compared with the current trial and error based scaling method for MPC-based MCA as it is able to reduce the sensation error between the motion simulator and real vehicle drivers, enhance motion fidelity, and use the platform workspace more wisely to reduce sensation error while respecting the platform's physical boundaries.

The regeneration of the motion signals of a real vehicle is not physically possible because of the workspace limitations of the platforms. The motion cueing algorithms (MCAs) are in charge of reproduction of the motion sensation for the drivers of simulation platforms as realistic as possible to the real vehicles. The model predictive control-based motion cueing algorithms (MPC-based MCAs) are recently used to find the optimal value of the input signals with consideration of the linear constraints of the platform in the Cartesian coordinate system of the mechanism. A new time-varying MPC-based MCA is introduced for the first time in this research by considering the joints' limitations of the mechanism inside the MPC model for longitudinal channel. The proposed model can consider the physical limitation of the active joints instead of substituting the limitation in the Cartesian coordinate system. The validation of the proposed model is performed using MATLAB software and the results prove that the proposed time-varying MPC-based MCA leads better motion sensation compared with the existing MPC-based MCA.

Driving simulators are effective tools for training, virtual prototyping, and safety assessment which can minimize the cost and maximize road safety. Despite the aim of a realistic motion generation for the impression of real-world driving, motion simulators are bound in a limited workspace. Motion cueing algorithms (MCAs) aim to plan an acceptable motion feeling for drivers, without infringing the simulated boundaries. Recently, model predictive control (MPC) has been widely used in MCAs; however, the tuning process for finding the best weights of the MPC optimization is still a challenge. As there are several objectives for the optimization without any standard weighting for solution evaluations, a nonbiased scalarization of solutions for the purpose of comparison is impossible. In this paper, a clear method for obtaining the best MPC weighting has been proposed. This method searches for the best tune of MPC cost function weights, reduces the user burden for weight tuning while receiving feedback from the user satisfaction. The MPC-based MCA weights are optimized using a multiobjective genetic algorithm (GA) considering objectives, such as minimization of motion inputs (linear acceleration and angular velocity), input rates, output displacements and the sensed motion errors. Any process based on trial-and-error has been omitted. The adjusted weights have to satisfy a set of predefined conditions related to maximum tolerated error and maximum displacement. The obtained Pareto-front is used for decision making via an interactive GA (IGA), aiming for maximization of the decision maker's satisfaction. A Web interface is developed to interact with the IGA and to influence the region of searching. Simulation results show the superiority of the proposed method compared with the previous empirical tuning method. The sensed motion error is minimized using the proposed method and with the same available workspace, a more realistic motion can be rendered to the driver.

Autonomous vehicles (AVs) are considered one of the most promising solutions for enhancing road safety, saving individuals' time, and reducing energy consumption. Autonomous vehicles are still in their early stage to be publicly accepted and gain a high level of trust. They need to be comprehensively and continuously evaluated and improved through road tests which are risky, costly, and time-consuming. Motion simulators are capable of contributing to these tests by providing an immersive virtual environment and high fidelity ride experiences for subjective and objective evaluations of AVs' performance. This paper provides a simulation study on the capability of a serial motion platform, known as the Universal Motion Simulator (UMS), for emulation of an AVs' path tracking capabilities. For this purpose, a versatile path tracking model of an AV is initially introduced. The computational Multi-Body System (MBS) approach is then used to implement inverse kinematics and dynamics analyses of the UMS. The UMS is also equipped with an optimal Motion Cueing Algorithm (MCA) to emulate the motion sensation of the AV performing different manoeuvres. The results show that the UMS is an efficient tool for regenerating a realistic and high-fidelity AV ride experience when (1) curvature of the trajectory is not large and the AV does not experience large turning angles when it negotiates the path, and (2) the human motion sensation (vestibular system mathematical model) is taken into consideration in developing the MCA of the motion simulator.

In this paper, an open source C++ Genetic Algorithm library is proposed called openGA. This library is capable of optimization in each of single objective, multi-objective and interactive modes. The main motivation for proposing this library is to provide freedom to users for designing their custom solution data model without limitations which many currently available software/libraries suffer from such as forcing a user to define the solutions as vectors or limiting the output of evaluation functions to a predefined format. In addition, the user has the entire control over genetic operations such as solution creation, mutation and crossover. The multi-object mode performs a Non-dominated Sorting Genetic Algorithm known as NSGA-III to obtain the pareto-optimal front while preserving the solution diversity. This library can handle multi-threading computations for single and multi-objective problems to increase the speed of the calculations significantly. The interactive mode is suitable for applications where human subjectivity is involved for evaluation of the cost function. Several simulation and tests are performed to verify the effectiveness of this library for calculations of optimization problems.

The aim of this paper is to design and develop an optimal motion cueing algorithm (MCA) based on the genetic algorithm (GA) that can generate high-fidelity motions within the motion simulator's physical limitations. Both, angular velocity and linear acceleration are adopted as the inputs to the MCA for producing the higher order optimal washout filter. The linear quadratic regulator (LQR) method is used to constrain the human perception error between the real and simulated driving tasks. To develop the optimal MCA, the latest mathematical models of the vestibular system and simulator motion are taken into account. A reference frame with the center of rotation at the driver's head to eliminate false motion cues caused by rotation of the simulator to the translational motion of the driver's head as well as to reduce the workspace displacement is employed. To improve the developed LQR-based optimal MCA, a new strategy based on optimal control theory and the GA is devised. The objective is to reproduce a signal that can follow closely the reference signal and avoid false motion cues by adjusting the parameters from the obtained LQR-based optimal washout filter. This is achieved by taking a series of factors into account, which include the vestibular sensation error between the real and simulated cases, the main dynamic limitations, the human threshold limiter in tilt coordination, the cross correlation coefficient, and the human sensation error fluctuation. It is worth pointing out that other related investigations in the literature normally do not consider the effects of these factors. The proposed optimized MCA based on the GA is implemented using the MATLAB/Simulink software. The results show the effectiveness of the proposed GA-based method in enhancing human sensation, maximizing the reference shape tracking, and reducing the workspace usage.

A motion simulator is an effective tool for training a driver in a safe environment by mimicking motion similar to the real world. To give a realistic feeling of driving and avoid motion sickness, an accurate motion cueing algorithm is required to restrict the platform within the allowed workspace range while regenerating an appropriate motion feeling for the simulator driver. Recently, employing Model Predictive Control (MPC) in the motion cueing algorithm has become popular. In this control method, by predicting future dynamics, an input is optimized to minimize a cost function over a prediction horizon while respecting the constraints. Reducing the prediction horizon is desirable to minimize the computational burden; however it draws the system toward instability. In this research, applying a nonuniform weighting method is proposed to stabilize the motion cueing algorithm using MPC with short prediction horizon and optimized weighting adjustment. Simulation results show the effectiveness of the proposed method.

The washout filter for a driving simulator is able to regenerate high fidelity vehicle translational and rotational motions within the simulator's physical limitations and return the simulator platform back to its initial position. The classical washout filter provides a popular solution that has been broadly utilized in different commercial simulators due to its simplicity, short processing time, and reasonable performance. One limitation of the classical washout filter is its sub-optimal parameter tuning process, which is based on the trial-and-error method. This leads to an inefficient workspace usage and, consequently, generation of false motion cues that lead to simulator sickness. Ignorance of a human sensation model in its design is another drawback of classical washout filters. The purpose of this study is to use Particle Swarm Optimization (PSO) to design and tune the washout filter parameters, in order to increase motion fidelity, decrease the human sensation error, and improve efficiency of the workspace usage. The proposed PSO-based washout filter is designed and implemented using the MATLAB/Simulink software package. The results indicate the effectiveness of the PSO-based washout filter in reducing the human sensation error, increasing the capability of reference shape tracking, and improving efficiency of the workspace usage.