Analysis of isometric cervical strength with a nonlinear musculoskeletal model with 48 degrees of freedom

Background: Musculoskeletal models served to analyze head–neck motion and injury during automotive impact. Although muscle activation is known to affect the kinematic response, a model with properly validated muscle contributions does not exist to date. The goal of this study was to enhance a musculoskeletal neck model and to validate passive properties, muscle moment arms, maximum isometric strength, and muscle activity. Methods: A dynamic nonlinear musculoskeletal model of the cervical spine with 48 degrees of freedom was extended with 129 bilateral muscle segments. The stiffness of the passive ligamentous spine was validated in flexion/extension, lateral bending, and axial rotation. Instantaneous joint centers of rotation were validated in flexion/extension, and muscle moment arms were validated in flexion/extension and lateral bending. A linearized static model was derived to predict isometric strength and muscle activation in horizontal head force and axial rotation tasks. Results: The ligamentous spine stiffness, instantaneous joint centers of rotation, muscle moment arms, cervical isometric strength, and muscle activation patterns were in general agreement with biomechanical data. Taking into account equilibrium of all neck joints, isometric strength was strongly reduced in flexion (46 %) and axial rotation (81 %) compared to a simplified solution only considering equilibrium around T1–C7, while effects were marginal in extension (3 %). Conclusions: For the first time, isometric strength and muscle activation patterns were accurately predicted using a neck model with full joint motion freedom. This study demonstrates that model strength will be overestimated particularly in flexion and axial rotation if only muscular moment generation at T1–C7 is taken into account and equilibrium in other neck joints is disregarded.     

A multi-level model on automated vehicle acceptance (MAVA): a review-based study

Abstract

Automated vehicle acceptance (AVA) is a necessary condition for the realisation of higher-level objectives such as improvements in road safety, reductions in traffic congestion and environmental pollution. On the basis of a systematic literature review of 124 empirical studies, the present study proposes MAVA, a multi-level model to predict AVA. It incorporates a process-oriented view on AVA, considering acceptance as the result of a four-stage decision-making process that ranges from the exposure of the individual to automated vehicles (AVs) in Stage 1, the formation of favourable or unfavourable attitudes towards AVs in Stage 2, making the decision to adopt or reject AVs in Stage 3, to the implementation of AVs into practice in Stage 4. MAVA incorporates 28 acceptance factors that represent seven main acceptance classes. The acceptance factors are located at two levels, i.e., micro and meso. Factors at the micro-level constitute individual difference factors (i.e., socio-demographics, personality and travel behaviour). The meso-level captures the exposure of individuals to AVs, instrumental domain-specific, symbolic-affective and moral-normative factors of AVA. The literature review revealed that 6% of the studies investigated the exposure of individuals to AVs (i.e., knowledge and experience). 22% of the studies investigated domain-specific factors (i.e., performance and effort ­expectancy, safety, facilitating conditions, and service and vehicle ­characteristics), 4% symbolic-affective factors (i.e., hedonic motivation and social influence), and 12% moral-normative factors (i.e., perceived benefits and risks). Factors related to a person’s socio-demographic profile, travel behaviour and personality were investigated by 28%, 15% and 14% of the studies, respectively. We recommend that future studies empirically verify MAVA using longitudinal or experimental studies.     

How do drivers merge heavy goods vehicles onto freeways? A semi-structured interview unveiling needs for communication and support

Cognition, Technology & Work - Freeway merging of heavy goods vehicles (HGV) is a safety–critical manoeuvre. However, at present, it is largely unknown how HGV drivers perceive and...     

How to keep drivers engaged while supervising driving automation? A literature survey and categorisation of six solution areas

Abstract

This work aimed to organise recommendations for keeping people engaged during human supervision of driving automation, encouraging a safe and acceptable introduction of automated driving systems. First, heuristic knowledge of human factors, ergonomics, and psychological theory was used to propose solution areas to human supervisory control problems of sustained attention. Driving and non-driving research examples were drawn to substantiate the solution areas. Automotive manufacturers might (1) avoid this supervisory role altogether, (2) reduce it in objective ways or (3) alter its subjective experiences, (4) utilize conditioning learning principles such as with gamification and/or selection/training techniques, (5) support internal driver cognitive processes and mental models and/or (6) leverage externally situated information regarding relations between the driver, the driving task, and the driving environment. Second, a cross-domain literature survey of influential human-automation interaction research was conducted for how to keep engagement/attention in supervisory control. The solution areas (via numeric theme codes) were found to be reliably applied from independent rater categorisations of research recommendations. Areas (5) and (6) were addressed by around 70% or more of the studies, areas (2) and (4) in around 50% of the studies, and areas (3) and (1) in less than around 20% and 5%, respectively. The present contribution offers a guiding organisational framework towards improving human attention while supervising driving automation.     

Galvanic vestibular stimulation elicits consistent head-neck motion in seated subjects

Humans actively stabilize the head-neck system based on vestibular, proprioceptive and visual information. Galvanic vestibular stimulation (GVS) has been used previously to demonstrate the role of vestibular feedback in standing balance. This study explores the effect of GVS on head-neck kinematics and evaluates the approach to investigate the vestibular contribution to head-neck stabilization. GVS was applied to 11 seated subjects using seven different stimuli (single sinusoids and multisines) at amplitudes of 0.5-2 mA and frequencies of 0.4-5.2 Hz using a bilateral bipolar configuration while 3-D head and torso kinematics were recorded using motion capture. System identification techniques were used evaluating coherence and frequency response functions (FRFs). GVS resulted in significant coherence in roll, yaw and lateral translation, consistent with effects of GVS while standing as reported in the literature. The gain of the FRFs varied with frequency and no modulation was observed across the stimulus amplitudes, indicating a linear system response for the stimulations considered. Compared to single sine stimulation, equivalent FRFs were observed during unpredictable multisine stimulation, suggesting the responses during both stimuli to be of a reflexive nature. These results demonstrate the potential of GVS to investigate the vestibular contribution to head-neck stabilization.     

Modelling driver behaviour: a rationale for multivariate statistics

This article provides a discussion of driver behaviour models. Based on a literature survey and using experimental driving simulator data, three types of driver behaviour models are evaluated: motivational models, adaptive control models and trait models. It is shown that motivational models are comprehensive but lack specificity. Many adaptive control models yield precise results but tend to be overly specific and psychologically implausible. Trait models have not been successful in the past and have been criticised for providing post hoc explanations and for the absence of a multifactorial structural approach. We demonstrate that if the right multivariate statistical techniques are used, trait models are suitable for the parsimonious modelling of predictive-valid driver characteristics, thereby taking an intermediate position on a dimension ranging from specific to unspecific models.     

Why do drivers maintain short headways in fog? A driving-simulator study evaluating feeling of risk and lateral control during automated and manual car following

Drivers in fog tend to maintain short headways, but the reasons behind this phenomenon are not well understood. This study evaluated the effect of headway on lateral control and feeling of risk in both foggy and clear conditions. Twenty-seven participants completed four sessions in a driving simulator: clear automated (CA), clear manual (CM), fog automated (FA) and fog manual (FM). In CM and FM, the drivers used the steering wheel, throttle and brake pedals. In CA and FA, a controller regulated the distance to the lead car, and the driver only had to steer. Drivers indicated how much risk they felt on a touchscreen. Consistent with our hypothesis, feeling of risk and steering activity were elevated when the lead car was not visible. These results might explain why drivers adopt short headways in fog. Practitioner Summary: Fog poses a serious road safety hazard. Our driving-simulator study provides the first experimental evidence to explain the role of risk-feeling and lateral control in headway reduction. These results are valuable for devising effective driver assistance and support systems.