A study on objective evaluation of vehicle steering comfort based on driver's electromyogram and movement trajectory

The evaluation of driver's steering comfort, which is mainly concerned with the haptic driver–vehicle interaction, is important for the optimization of advanced driver assistance systems. The current approaches to investigating steering comfort are mainly based on the driver's subjective evaluation, which is time-consuming, expensive, and easily influenced by individual variations. This paper makes some tentative investigation of objective evaluation, which is based on the electromyogram (EMG) and movement trajectory of the driver's upper limbs during steering maneuvers. First, a steering experiment with 21 subjects is conducted, and EMG and movement trajectories of the driver's upper limbs are measured, together with their subjective evaluation of steering comfort. Second, five evaluation indices including EMG and movement information are defined based on the measurements from the first step. Correlation analyses are conducted between each evaluation index and steering comfort rating (SCR), and the results show that all of the indices have significant correlations with SCR. Then, an artificial neural network model is devised based on the aforementioned indices and its predicting performance of SCR is demonstrated as acceptable. The results reveal that it may be feasible to establish an objective evaluation approach for vehicle steering comfort

Sitting behaviour-based pattern recognition for predicting driver fatigue

The proposed approach based on physiological characteristics of sitting behaviours and sophisticated machine learning techniques would enable an effective and practical solution to driver fatigue prognosis since it is insensitive to the illumination of driving environment, non-obtrusive to driver, without violating driver’s privacy, more acceptable by drivers

Characterization of driver neuromuscular dynamics for human-automation collaboration design of automated vehicles

In order to design an advanced human-automation collaboration system for highly automated vehicles, research into the driver's neuromuscular dynamics is needed. In this paper a dynamic model of drivers' neuromuscular interaction with a steering wheel is firstly established. The transfer function and the natural frequency of the systems are analyzed. In order to identify the key parameters of the driver-steering-wheel interacting system and investigate the system properties under different situations, experiments with driver-in-the-loop are carried out. For each test subject, two steering tasks, namely the passive and active steering tasks, are instructed to be completed. Furthermore, during the experiments, subjects manipulated the steering wheel with two distinct postures and three different hand positions. Based on the experimental results, key parameters of the transfer function model are identified by using the Gauss-Newton algorithm. Based on the estimated model with identified parameters, investigation of system properties is then carried out. The characteristics of the driver neuromuscular system are discussed and compared with respect to different steering tasks, hand positions and driver postures. These experimental results with identified system properties provide a good foundation for the development of a haptic take-over control system for automated vehicles

Hybrid-learning-based driver steering intention prediction using neuromuscular dynamics

The emerging automated driving technology poses a new challenge on driver-automation collaboration. In this study, oriented by human-machine mutual understanding, a driver steering intention prediction method is proposed to better understand human driver's expectation during driver-vehicle interaction. The steering intention is predicted based on a novel hybrid-learning-based time-series model with deep learning networks. Two different driving modes, namely, both hands and single right-hand driving modes, are studied. Different electromyography (EMG) signals from the upper limb muscles are collected and used for the steering intention prediction. The relationship between the neuromuscular dynamics and the steering torque is analyzed first. Then, the hybrid-learning-based model is developed to predict both the continuous and discrete steering intentions. The two intention prediction networks share the same temporal pattern exaction layer, which is built with the Bi-directional Recurrent Neural Network (RNN) and Long short-term memory (LSTM) cells. The model prediction performance is evaluated with a varied history and prediction horizon to exploit the model capability further. The experimental data are collected from 21 participants of varied ages and driving experience. The results show that the proposed method can achieve a prediction accuracy of around 95% steering under the two driving modes

Dynamics and Model-Based Control of Electric Power Steering Systems

Many automobile manufacturers are switching to Electric Power Steering (EPS) systems for their better performance and cost advantages over traditional Hydraulic Power Steering (HPS) systems. EPS compared to HPS offer lower energy consumption, lower total weight, and package flexibility at no cost penalty. Furthermore, since EPS systems can provide assistance to drivers independent of the vehicle driving conditions, new technologies can be implemented to improve the steering feel and safety, simultaneously. In this thesis, a neuromusculoskeletal driver and a high-fidelity vehicle model are developed in MapleSim to provide realistic simulations to study the driver-vehicle interactions and EPS systems. The vehicle model consists of MacPherson and multilink suspensions at front and rear equipped with a column-type EPS system. The driver model is a fully neuromusculoskeletal model of a driver arm holding the steering wheel, controlled by the driver's central nervous system. A hierarchical approach is used to capture the complexity of the neuromuscular dynamics and the central nervous system in the coordination of the driver's upper extremity activities. The proposed motor control framework has three layers: the first layer, or the path-planning layer, plans a desired vehicle trajectory and the required steering angles to perform the desired trajectory, the second layer (or the force distribution controller) actuates the musculoskeletal arm, and the final layer is added to ensure the precision control and disturbance rejection of the motor control units. The overall goal of this thesis is to study vehicle-driver interactions and to design a model-based EPS controller that considers the driver's characteristics. To design such an EPS controller, the high-fidelity driver-vehicle model is simplified to reduce the computational burden associated with the multibody and biomechanical systems. Then, four driver types are introduced based on the physical characteristics of drivers such as age and gender, and the corresponding parameters are incorporated in the model. Last but not least, a new model-based EPS controller is developed to provide appropriate assistance to each of the predefined driver types. To do this, the characteristic curves are tuned using a systematic optimization procedure to provide appropriate assistance to drivers with different physical strength, in order to have a similar road and steering feel. In this thesis, it is recommended that muscle fatigue be used as a measure of steering feel. Then, based on the tuned EPS characteristic curves, an observer-based optimal disturbance rejection controller, consisting of a linear quadratic regulator controller and a Kalman filter observer augmented with a shaping filter, is developed to deliver the assistance while attenuating external disturbances. The results show that it is possible to develop a model-based EPS controller that is optimized for a given driver population

Eleventh Annual Conference on Manual Control

Human operator performance and servomechanism analyses for manual vehicle control tasks are studied

An experimental approach for the characterization of prolonged sitting postures using pressure sensitive mats

The adoption of prolonged sitting posture,which is a condition commonly encountered in several working tasks,is known to induce a wide range of negative effects,including discomfort,which has been recognized as an early predictor for musculoskeletal disorders (particularly low back pain).In this regard,the continuous monitoring of worker’s psychophysical state while sitting for long periods of time, may result useful in to preventing and managing potentially risky situations and to promote ergonomics and macroergonomics interventions,aimed to better organize work shifts and workplaces.The aim of this dissertation is to provide and test the reliability of a set of monitoring parameters,based on the use of quantitative information derived from body-seat contact pressure sensors.In particular, he study was focused on the assessment of trunk postural sway (the small oscillations resulting from the stabilization control system) and the number of In Chair Movements (ICM) or postural shifts performed while sitting, proven as a reliable tool for discomfort prediction. This thesis is articulated into four experimental campaigns.The first is a pilot study which aimed to define the most reliable algorithm and the set of parameters useful to assess the performed postural shifts or In chair Movements (ICM), which result useful to characterize postural strategies in the long term-monitoring. In this regard, a pilot study was conducted in which two different algorithms for the ICM computing were tested, based on different parameters and having different thresholds. The chosen algorithm was used, together with trunk sway parameters, to evaluate postural strategies in the other three experiments of this thesis. The second and the third studies evaluated sitting postural strategies among bus drivers during regular, long-term work shifts performed on urban and extra-urban routes. The results, in this case, showed that, all drivers reported a constant increase in perceived discomfort levels and a correspondent increase in trunk sway and overall number of ICM performed. This may indicate the adoption of specific strategies in order to cope with discomfort onset, a fatigue-induced alteration of postural features, or both simultaneously. However, it was interesting to observe differences in ICM vs trunk sway trend considering the single point-to-point route in the case of urban drivers. This difference between may indicate that these parameters refer to different aspects of sitting postural strategies: ICM may be more related to discomfort while sway may be more representative of task-induced fatigue. Trunk sway monitoring, as well as the count of ICM performed by bus drivers may thus be a useful tool in detecting postural behaviors potentially associated with deteriorating performance and onset of discomfort. Finally, the last experiment aimed to characterize modifications in sitting behavior, in terms of trunk sway and ICM among office workers during actual shifts. Surprisingly, results showed a decreasing trend in trunk sway parameters and ICM performed over time, with significant modifications in sitting posture in terms of trunk flexion-extension. Subjects were also stratified basing on their working behavior (staying seated or making short breaks during the trial) and significant differences were identified among these two groups in terms of postural sway and perceived discomfort. This may indicate that the adoption of specific working strategies can significantly influence sitting behavior and discomfort onset. In conclusion, the trunk sway monitoring and the ICM assessment in actual working environments may represent a useful tool to detect specific postural behaviors potentially associated with deteriorating performance and onset of discomfort, both among professional drivers and office workers.They might effectively support the evaluation of specific working strategies,as well as the set-up of macroergonomics interventions

Proceedings of the Seventeenth Annual Conference on Manual Control

Manual control is considered, with concentration on perceptive/cognitive man-machine interaction and interface

Proceedings of the 3rd International Mobile Brain/Body Imaging Conference : Berlin, July 12th to July 14th 2018

The 3rd International Mobile Brain/Body Imaging (MoBI) conference in Berlin 2018 brought together researchers from various disciplines interested in understanding the human brain in its natural environment and during active behavior. MoBI is a new imaging modality, employing mobile brain imaging methods like the electroencephalogram (EEG) or near infrared spectroscopy (NIRS) synchronized to motion capture and other data streams to investigate brain activity while participants actively move in and interact with their environment. Mobile Brain / Body Imaging allows to investigate brain dynamics accompanying more natural cognitive and affective processes as it allows the human to interact with the environment without restriction regarding physical movement. Overcoming the movement restrictions of established imaging modalities like functional magnetic resonance tomography (MRI), MoBI can provide new insights into the human brain function in mobile participants. This imaging approach will lead to new insights into the brain functions underlying active behavior and the impact of behavior on brain dynamics and vice versa, it can be used for the development of more robust human-machine interfaces as well as state assessment in mobile humans.DFG, GR2627/10-1, 3rd International MoBI Conference 201