Modeling of occupant's head movement behavior in motion sickness study via time delay neural network

Passengers are more susceptible to experiencing motion sickness (MS) than drivers. The difference in the severity of MS is due to their different head movement behavior during curve driving. When negotiating a curve, the passengers tilt their heads towards the lateral acceleration direction while the drivers tilt their heads against it. Thus, to reduce the passengers’ level of MS, they need to reduce their head’s tilting angle towards the lateral acceleration direction. Designing MS minimization strategies is easier if the correlation between the head movement and lateral acceleration is known mathematically. Therefore, this paper proposes the utilization of a time delay neural network (TDNN) to model the correlation of the occupant’s head movement and lateral acceleration. An experiment was conducted to gather real-time data for the modeling process. The results show that TDNN manages to model the correlation by producing a similar output response to the actual response. Thus, it is expected that the correlation model could be used as an occupant’s head movement predictor tool in future studies of MS

Optimised combinatorial control strategy for active anti-roll bar system for ground vehicle

The objective of this paper is to optimise the proposed control strategy for an active anti-roll bar system using non-dominated sorting genetic algorithm (NSGA-II) tuning method. By using an active anti-roll control strategy, the controller can adapt to current road conditions and manoeuvres unlike a passive anti-roll bar. The optimisation solution offers a rather noticeable improvement results compared to the manually-tuned method. From the application point of view, both tuning process can be used. However, using optimisation method gives a multiple choice of solutions and provides the optimal parameters compared to manual tuning method

Fully convolutional neural network for Malaysian road lane detection

Recently, a deep learning, Fully Convolutional Neural Network (FCN) has been widely studied because it can demonstrate promising results in the application of detection of objects in an image or video. Hence, the FCN approach has been proposed as one of the solution methods in mitigating the issues pertinent to Malaysia’s road lane detection. Previously, FCN model for lane detection has not been tested in Malaysian road conditions. Therefore, this study investigates the further performance of this model in the Malaysia. The network model is trained and validated using the datasets obtained from Machine Learning NanoDegree. In addition, the real-time data collection has been conducted to collect the data sets for the testing at the highway and urban areas in Malaysia. Then, the collected data is used to test the performance of the FCN network in detecting the lane markings on Malaysia road. The results demonstrated that the FCN method is achieving 99% of the training and validation accuracy

Radial basis function neural network for head roll prediction modelling in a motion sickness study

Motion Sickness (MS) is the result of uneasy feelings that occurs when travelling. In MS mitigation studies, it is necessary to investigate and measure the occupant’s Motion Sickness Incidence (MSI) for analysis purposes. One way to mathematically calculate the MSI is by using a 6-DOF Subjective Vertical Conflict (SVC) model. This model utilises the information of the vehicle lateral acceleration and the occupant’s head roll angle to determine the MSI. The data of the lateral acceleration can be obtained by using a sensor. However, it is impractical to use a sensor to acquire the occupant’s head roll response. Therefore, this study presents the occupant’s head roll prediction model by using the Radial Basis Function Neural Network (RBFNN) method to estimate the actual head roll responses. The prediction model is modelled based on the correlation between lateral acceleration and head roll angle during curve driving. Experiments have been conducted to collect real naturalistic data for modelling purposes. The results show that the predicted responses from the model are similar with the real responses from the experiment. In future, it is expected that the prediction model will be useful in measuring the occupant’s MSI level by providing the estimated head roll responses

Lateral control strategy based on head movement responses for motion sickness mitigation in autonomous vehicle

Passengers are more susceptible to motion sickness (MS) than the drivers because during cornering, they tilt their heads according to lateral acceleration direction, while the drivers tilt their heads against it. During slalom driving, high lateral acceleration that resulted from inappropriate wheel’s turning will increase the severity level of MS as it contributes to a larger passenger’s head roll angle towards the lateral acceleration direction. Thus, for an autonomous vehicle, it is necessary to design a smooth lateral control to obtain appropriate wheel angle to prevent high lateral acceleration. This study proposes an inner-loop lateral control strategy which utilized head roll angle as the controlled variable to generate corrective wheel angle to reduce the lateral acceleration. Firstly, an estimation model of driver’s and passenger’s head roll angle is developed by radial basis function network method based on the correlation between lateral acceleration and occupant’s head roll angle. The driver’s and passenger’s models are considered as the reference and the controlled subject, respectively. Secondly, a fuzzy logic controller is adopted to generate corrective wheel angle based on the head roll angle responses. The reduction of the lateral acceleration caused by the corrective wheel angle minimized the passenger’s head roll angle and hence mitigated their MS level. Simulation results show 3.25% and 10.86% reduction of motion sickness incidence in a single lap and ten laps after the proposed control strategy is applied. It is expected that the proposed control strategy will contribute to the MS mitigation study in autonomous vehicle field

Vehicle collision avoidance motion planning strategy using artificial potential field with adaptive multi-speed scheduler

This study presents an adaptive motion planning strategy for automated vehicle collision avoidance systems to be associated with the variation of collision speed region based on the position of the obstacle. This is done by designing the motion planner using an artificial potential field (APF) with the incorporation of an adaptive multi-speed scheduler using fuzzy system in the motion planning structure. The knowledge database information is developed based on the risk perception of the driver that consists of APF parameters and was optimised by using particle swarm optimisation algorithm. This study contributes to the improvement of a feasible reference motion generated by the motion planner that can be converted into desired control signals. The reference motion resulted to provide the control command that managed to avoid collision successfully by evasive manoeuvre without lane departure when adapting to variation in the vehicle speeds with different obstacle positions. The results indicated the reduction of the lateral error with respect to the reference avoidance trajectory data of up to 87% compared to base-type APF with maximum reference lateral motion is reduced of up to 26%. Then, a hardware-in-loop test is conducted to verify the proposed strategy using a steering wheel system

Ownership strcuture, bank capital and bank lending/financing behaviour in a dual banking system

Credit supply in the market is crucial in order to ensure sustainable real production can survive in the market as well as to strengthen economic activity. Therefore, it is not surprising that when the financial crisis occurred in 2008 to 2009, policymakers continued to use a variety of mechanisms such that banks could continue to maintain their credit supply. Nevertheless, risk sharing based on the business model that was adopted by islamic banks displayed different behaviour from the conventional banks. Based on prior studies, the stability of financing growth by islamic banks as compared to lending growth of conventional banks showed the model used by islamic banks was more capable of effectively withstanding the financial crisis. Therefore, research into the quality of lending and financing is important to understand the growth of bank lending and financing behaviour in the market. Hence, the main objective of this study is to review the effect of ownership structure, bank capital and bank lending including financing behaviour in islamic versus conventional banks. In addition, this study proposes a conceptual framework to further comprehend the decisions made in undertaking ownership structure, bank capital and lending in the dual banking system

Empirical linkage of intellectual capital and performance of islamic banks

With the eminence of the era of knowledge-based economies, the concept of intellectual capital (IC) is of vital importance for organisations to survive in these vigorous environments. As one of the knowledge intensive sectors, there is no exception to banking institutions in enhancing their intellectual capital efficiency to the forefront especially for Islamic banks (IBs) that have to compete with the firmlyestablished conventional banks. Accordingly, this study intends to measure the relationship between intellectual capital efficiency and banks’ performance. In total, 59 Islamic banks are selected and their audited annual reports are compiled from the banks’ websites respectively from year 2006-2017. Value-added intellectual coefficient (VAIC) are applied in measuring IC efficiency. The findings provide empirical evidences of positive relationship between IC efficiency and banks performance, nonetheless, when decomposes into human capital efficiency (HCE), structural capital efficiency (SCE) and capital employed efficiency (CEE), only human capital efficiency shows significant positive relationship with performance of the banks while the other two components show significant negative linkage with bank performance. Furthermore, due to criticisms towards VAIC method, this study using modified value-added intellectual capital coefficient (MVAIC) and found that MVAIC has significant positive relationship with bank performance while relational capital as additional variable in MVAIC regression model has no significant effect with bank performance. This study provides better insights on the importance of utilisation of IC by banking institutions particularly for Islamic banks

Ownership Structure, Bank Capital and Bank Lending/Financing Behaviour in a Dual Banking System

Credit supply in the market is crucial in order to ensure sustainable real production can survive in the market as well as to strengthen economic activity. Therefore, it is not surprising that when the financial crisis occurred in 2008 to 2009, policymakers continued to use a variety of mechanisms such that banks could continue to maintain their credit supply. Nevertheless, risk sharing based on the business model that was adopted by islamic banks displayed different behaviour from the conventional banks. Based on prior studies, the stability of financing growth by islamic banks as compared to lending growth of conventional banks showed the model used by islamic banks was more capable of effectively withstanding the financial crisis. Therefore, research into the quality of lending and financing is important to understand the growth of bank lending and financing behaviour in the market. Hence, the main objective of this study is to review the effect of ownership structure, bank capital and bank lending including financing behaviour in islamic versus conventional banks. In addition, this study proposes a conceptual framework to further comprehend the decisions made in undertaking ownership structure, bank capital and lending in the dual banking system