A review of human sensory dynamics for application to models of driver steering and speed control

Exploring physiological stress response evoked by passive translational acceleration in healthy adults: a pilot study utilizing electrodermal activity and heart rate variability measurements

Passive translational acceleration (PTA) has been demonstrated to induce the stress response and regulation of autonomic balance in healthy individuals. Electrodermal activity (EDA) and heart rate variability (HRV) measurements are reliable indicators of the autonomic nervous system (ANS) and can be used to assess stress levels. The objective of this study was to investigate the potential of combining EDA and HRV measurements in assessing the physiological stress response induced by PTA. Fourteen healthy subjects were randomly assigned to two groups of equal size. The experimental group underwent five trials of elevator rides, while the control group received a sham treatment. EDA and HRV indices were obtained via ultra-short-term analysis and compared between the two groups to track changes in the ANS. In addition, the complexity of the EDA time series was compared between the 4 s before and the 2-6 s after the onset of PTA to assess changes in the subjects' stress levels in the experimental group. The results revealed a significant increase in the skin conductance response (SCR) frequency and a decrease in the root mean square of successive differences (RMSSD) and high frequency (HF) components of HRV. In terms of stress assessment, the results showed an increase in the complexity of the EDA time series 2-6 s after the onset of PTA. These results indicate an elevation in sympathetic tone when healthy subjects were exposed to a translational transport scenario. Furthermore, evidence was provided for the ability of EDA complexity to differentiate stress states in individual trials of translational acceleration.

Investigating the influence of visuospatial stimuli on driver's speed perception: a laboratory study

Driving at an inappropriate speed is a major accident cause in the EU. Understanding the underlying sensory mechanisms can help to reduce speed and increase traffic safety. The present study investigated the effect of visuospatial stimuli on speed perception using an adaptive countermeasure to speeding based on a manipulation of optic flow. We added red lights on both sides of a simulated road. We expected speed to be perceived as faster when lights moved toward drivers due to increased optic flow, whereas we expected static light stimuli to not alter the optic flow and thus not influence speed perception. Two experiments applied the method of constant stimuli. To this end, participants encountered several trials of two video sequences on a straight road. A reference sequence showed the same traveling speed while test sequences varied around different traveling speeds. Participants indicated which sequence they perceived as faster, leading to the calculation of the point of subjective equality (PSE). A lower PSE indicates that the speed in this experimental condition is perceived as faster than in another experimental condition. Experiment 1A did not show a difference between PSEs of static and oncoming lights. Because participants had counted reflector posts for speed estimation, we removed these reflector posts in Experiment 1B and found a lower PSE for oncoming lights. Thus, such light stimuli may have an effect only in situations without other competing visual stimuli supporting speed perception. Future research should investigate whether speed perception is indeed a primarily visuospatial control task or whether other sensory information such as auditory factors can have an influence as well.

Driver Attention Assessment Using Physiological Measures from EEG, ECG, and EDA Signals

In this paper, we consider the evaluation of the mental attention state of individuals driving in a simulated environment. We tested a pool of subjects while driving on a highway and trying to overcome various obstacles placed along the course in both manual and autonomous driving scenarios. Most systems described in the literature use cameras to evaluate features such as blink rate and gaze direction. In this study, we instead analyse the subjects' Electrodermal activity (EDA) Skin Potential Response (SPR), their Electrocardiogram (ECG), and their Electroencephalogram (EEG). From these signals we extract a number of physiological measures, including eye blink rate and beta frequency band power from EEG, heart rate from ECG, and SPR features, then investigate their capability to assess the mental state and engagement level of the test subjects. In particular, and as confirmed by statistical tests, the signals reveal that in the manual scenario the subjects experienced a more challenged mental state and paid higher attention to driving tasks compared to the autonomous scenario. A different experiment in which subjects drove in three different setups, i.e., a manual driving scenario and two autonomous driving scenarios characterized by different vehicle settings, confirmed that manual driving is more mentally demanding than autonomous driving. Therefore, we can conclude that the proposed approach is an appropriate way to monitor driver attention.

A bicycle can be balanced by stochastic optimal feedback control but only with accurate speed estimates

Balancing a bicycle is typical for the balance control humans perform as a part of a whole range of behaviors (walking, running, skating, skiing, etc.). This paper presents a general model of balance control and applies it to the balancing of a bicycle. Balance control has both a physics (mechanics) and a neurobiological component. The physics component pertains to the laws that govern the movements of the rider and his bicycle, and the neurobiological component pertains to the mechanisms via which the central nervous system (CNS) uses these laws for balance control. This paper presents a computational model of this neurobiological component, based on the theory of stochastic optimal feedback control (OFC). The central concept in this model is a computational system, implemented in the CNS, that controls a mechanical system outside the CNS. This computational system uses an internal model to calculate optimal control actions as specified by the theory of stochastic OFC. For the computational model to be plausible, it must be robust to at least two inevitable inaccuracies: (1) model parameters that the CNS learns slowly from interactions with the CNS-attached body and bicycle (i.e., the internal noise covariance matrices), and (2) model parameters that depend on unreliable sensory input (i.e., movement speed). By means of simulations, I demonstrate that this model can balance a bicycle under realistic conditions and is robust to inaccuracies in the learned sensorimotor noise characteristics. However, the model is not robust to inaccuracies in the movement speed estimates. This has important implications for the plausibility of stochastic OFC as a model for motor control.

Egocentric Chunking in the Predictive Brain: A Cognitive Basis of Expert Performance in High-Speed Sports

What principles and mechanisms allow humans to encode complex 3D information, and how can it be so fast, so accurately and so flexibly transformed into coordinated action? How do these processes work when developed to the limit of human physiological and cognitive capacity—as they are in high-speed sports, such as alpine skiing or motor racing? High-speed sports present not only physical challenges, but present some of the biggest perceptual-cognitive demands for the brain. The skill of these elite athletes is in many ways an attractive model for studying human performance “in the wild”, and its neurocognitive basis. This article presents a framework theory for how these abilities may be realized in high-speed sports. It draws on a careful analysis of the case of the motorsport athlete, as well as theoretical concepts from: (1) cognitive neuroscience of wayfinding, steering, and driving; (2) cognitive psychology of expertise; (3) cognitive modeling and machine learning; (4) human-in-the loop modellling in vehicle system dynamics and human performance engineering; (5) experimental research (in the laboratory and in the field) on human visual guidance. The distinctive contribution is the way these are integrated, and the concept of chunking is used in a novel way to analyze a high-speed sport. The mechanisms invoked are domain-general, and not specific to motorsport or the use of a particular type of vehicle (or any vehicle for that matter); the egocentric chunking hypothesis should therefore apply to any dynamic task that requires similar core skills. It offers a framework for neuroscientists, psychologists, engineers, and computer scientists working in the field of expert sports performance, and may be useful in translating fundamental research into theory-based insight and recommendations for improving real-world elite performance. Specific experimental predictions and applicability of the hypotheses to other sports are discussed.

Determination of Turning Radius and Lateral Acceleration of Vehicle by GNSS/INS Sensor

In this article, we address the determination of turning radius and lateral acceleration acting on a vehicle up to 3.5 t gross vehicle mass (GVM) and cargo in curves based on turning radius and speed. Global Navigation Satellite System with Inertial Navigation System (GNSS/INS) dual-antenna sensor is used to measure acceleration, speed, and vehicle position to determine the turning radius and determine the proper formula to calculate long average lateral acceleration acting on vehicle and cargo. The two methods for automatic selection of events were applied based on stable lateral acceleration value and on mean square error (MSE) of turning radiuses. The models of calculation of turning radius are valid for turning radius within 5-70 m for both methods of automatic selection of events with mean root mean square error (RMSE) 1.88 m and 1.32 m. The models of calculation of lateral acceleration are valid with mean RMSE of 0.022 g and 0.016 g for both methods of automatic selection of events. The results of the paper may be applied in the planning and implementation of packing and cargo securing procedures to calculate average lateral acceleration acting on vehicle and cargo based on turning radius and speed for vehicles up to 3.5 t GVM. The results can potentially be applied for the deployment of autonomous vehicles in solutions grouped under the term of Logistics 4.0.

Comparative Diagnosis of the Urban Noise Problem from Infrastructural and Social Sensing Approaches: A Case Study in Ningbo, China

Urban noise causes a variety of health problems, and its prevention and control have thus become an important research topic in urban governance. Although existing literature is fairly comprehensive in revealing the physical noise patterns, it lacks the concern of people's perceived seriousness, especially at the macroscopic, i.e., citywide scale. In this paper, we borrow from the "exposure-perception-behavior" theory in environmental psychology, and propose an analytical framework for diagnosing the urban noise problem that integrates the Infrastructural and Social Sensing perspectives. Utilizing noise monitoring data that fills the spatiotemporal granularity gaps of official noise monitoring, as well as the "12345" urban complaint hotline records which serve as a proxy for residents' perceived noise levels, we empirically examine the mechanisms for physical magnitude and perceived seriousness of urban noise, respectively, by taking the Jiangbei District of Ningbo City, China as an example. Results show that the existence of perceptual bias and behavioral preference effects did shape people's perceived noise problem map that is vastly different from that of the physical noise magnitude, in which the semantics of urban places, temporal rhythms of life, and population demographics significantly influenced people's tolerance of noise. We conclude the paper with suggestions on updating the existing National Standard for urban noise regulation to reflect the perceptual aspect, and also methodological discussions on possible ways to recognize and utilize the perceptual bias in social-sensing big-data to better accommodate urban governance.

Assessing the contribution of active somatosensory stimulation to self-acceleration perception in dynamic driving simulators

In dynamic driving simulators, the experience of operating a vehicle is reproduced by combining visual stimuli generated by graphical rendering with inertial stimuli generated by platform motion. Due to inherent limitations of the platform workspace, inertial stimulation is subject to shortcomings in the form of missing cues, false cues, and/or scaling errors, which negatively affect simulation fidelity. In the present study, we aim at quantifying the relative contribution of an active somatosensory stimulation to the perceived intensity of self-motion, relative to other sensory systems. Participants judged the intensity of longitudinal and lateral driving maneuvers in a dynamic driving simulator in passive driving conditions, with and without additional active somatosensory stimulation, as provided by an Active Seat (AS) and Active Belts (AB) integrated system (ASB). The results show that ASB enhances the perceived intensity of sustained decelerations, and increases the precision of acceleration perception overall. Our findings are consistent with models of perception, and indicate that active somatosensory stimulation can indeed be used to improve simulation fidelity.

Evaluation of augmented reality cues to improve the safety of left-turn maneuvers in a connected environment: A driving simulator study

Left-turns are some of the most dangerous maneuvers drivers face as they involve a complex decision-making process. Indeed, drivers must wait for an adequate gap in oncoming traffic to safely complete a left-turn maneuver. In this context, incorrectly assessed gaps can lead to severe crashes and severe traffic delays at intersections. This study tests the potential of Augmented Reality (AR) technology, built into connected vehicle technology, to improve the safety of left-turn maneuvers of connected vehicles by adding visual virtual information to the driver. To achieve this goal, a driving simulator study was carried out. The effectiveness of the system was tested, and the ability of young drivers to detect adequate gaps between vehicles in the opposite lane (with right of way) to safely turn left was assessed with and without AR warnings at a two-way stop-controlled intersection under a connected vehicles environment. In the scenario projected on the simulation screen, three different virtual warnings were displayed and tested: a green/red traffic light, which informs the driver of the availability of an appropriate gap between opposing vehicles; a traffic light with a timer showing the number of seconds available to safely perform the left-turn maneuver; a traffic light with an additionally activated audio warning system. Significant positive effects of AR warnings on driving performance and traffic safety were observed: the number of safe left-turns increased and the delays at the intersection decreased. In addition, AR signaling improved driving behavior both during the waiting time, with many more drivers waiting for the gap in front of the stop line to avoid disrupting oncoming traffic, and turning movement, reducing the average time it took to complete the left-turn maneuver. This study confirmed the great potential of AR and connected vehicle technologies to improve general safety conditions on the road network, especially under risky situations and difficult maneuvers.

Predicting takeover response to silent automated vehicle failures

Current and foreseeable automated vehicles are not able to respond appropriately in all circumstances and require human monitoring. An experimental examination of steering automation failure shows that response latency, variability and corrective manoeuvring systematically depend on failure severity and the cognitive load of the driver. The results are formalised into a probabilistic predictive model of response latencies that accounts for failure severity, cognitive load and variability within and between drivers. The model predicts high rates of unsafe outcomes in plausible automation failure scenarios. These findings underline that understanding variability in failure responses is crucial for understanding outcomes in automation failures.

Human-like driving behaviour emerges from a risk-based driver model

Current driving behaviour models are designed for specific scenarios, such as curve driving, obstacle avoidance, car-following, or overtaking. However, humans can drive in diverse scenarios. Can we find an underlying principle from which driving behaviour in different scenarios emerges? We propose the Driver’s Risk Field (DRF), a two-dimensional field that represents the driver’s belief about the probability of an event occurring. The DRF, when multiplied with the consequence of the event, provides an estimate of the driver’s perceived risk. Through human-in-the-loop and computer simulations, we show that human-like driving behaviour emerges when the DRF is coupled to a controller that maintains the perceived risk below a threshold-level. The DRF model predictions concur with driving behaviour reported in literature for seven different scenarios (curve radii, lane widths, obstacle avoidance, roadside furniture, car-following, overtaking, oncoming traffic). We conclude that our generalizable DRF model is scientifically satisfying and has applications in automated vehicles.

Most driver models were designed for specific scenario. Here, the authors developed a driver behaviour model that can be applied to multiple scenarios and show that human-like driving behaviour emerges when the Driver’s Risk Field is coupled to a controller that maintains the perceived risk below a threshold level.

Getting Back Into the Loop: The Perceptual-Motor Determinants of Successful Transitions out of Automated Driving

OBJECTIVE

To present a structured, narrative review highlighting research into human perceptual-motor coordination that can be applied to automated vehicle (AV)-human transitions.

BACKGROUND

Manual control of vehicles is made possible by the coordination of perceptual-motor behaviors (gaze and steering actions), where active feedback loops enable drivers to respond rapidly to ever-changing environments. AVs will change the nature of driving to periods of monitoring followed by the human driver taking over manual control. The impact of this change is currently poorly understood.

METHOD

We outline an explanatory framework for understanding control transitions based on models of human steering control. This framework can be summarized as a perceptual-motor loop that requires (a) calibration and (b) gaze and steering coordination. A review of the current experimental literature on transitions is presented in the light of this framework.

RESULTS

The success of transitions are often measured using reaction times, however, the perceptual-motor mechanisms underpinning steering quality remain relatively unexplored.

CONCLUSION

Modeling the coordination of gaze and steering and the calibration of perceptual-motor control will be crucial to ensure safe and successful transitions out of automated driving.

APPLICATION

This conclusion poses a challenge for future research on AV-human transitions. Future studies need to provide an understanding of human behavior that will be sufficient to capture the essential characteristics of drivers reengaging control of their vehicle. The proposed framework can provide a guide for investigating specific components of human control of steering and potential routes to improving manual control recovery.

Toward Computational Simulations of Behavior During Automated Driving Takeovers: A Review of the Empirical and Modeling Literatures

OBJECTIVE

This article provides a review of empirical studies of automated vehicle takeovers and driver modeling to identify influential factors and their impacts on takeover performance and suggest driver models that can capture them.

BACKGROUND

Significant safety issues remain in automated-to-manual transitions of vehicle control. Developing models and computer simulations of automated vehicle control transitions may help designers mitigate these issues, but only if accurate models are used. Selecting accurate models requires estimating the impact of factors that influence takeovers.

METHOD

Articles describing automated vehicle takeovers or driver modeling research were identified through a systematic approach. Inclusion criteria were used to identify relevant studies and models of braking, steering, and the complete takeover process for further review.

RESULTS

The reviewed studies on automated vehicle takeovers identified several factors that significantly influence takeover time and post-takeover control. Drivers were found to respond similarly between manual emergencies and automated takeovers, albeit with a delay. The findings suggest that existing braking and steering models for manual driving may be applicable to modeling automated vehicle takeovers.

CONCLUSION

Time budget, repeated exposure to takeovers, silent failures, and handheld secondary tasks significantly influence takeover time. These factors in addition to takeover request modality, driving environment, non-handheld secondary tasks, level of automation, trust, fatigue, and alcohol significantly impact post-takeover control. Models that capture these effects through evidence accumulation were identified as promising directions for future work.

APPLICATION

Stakeholders interested in driver behavior during automated vehicle takeovers may use this article to identify starting points for their work.

A computational model for driver's cognitive state, visual perception and intermittent attention in a distracted car following task

We present a computational model of intermittent visual sampling and locomotor control in a simple yet representative task of a car driver following another vehicle. The model has a number of features that take it beyond the current state of the art in modelling natural tasks, and driving in particular. First, unlike most control theoretical models in vision science and engineering-where control is directly based on observable (optical) variables-actions are based on a temporally enduring internal representation. Second, unlike the more sophisticated engineering driver models based on internal representations, our model explicitly aims to be psychologically plausible, in particular in modelling perceptual processes and their limitations. Third, unlike most psychological models, it is implemented as an actual simulation model capable of full task performance (visual sampling and longitudinal control). The model is developed and validated using a dataset from a simplified car-following experiment (N = 40, in both three-dimensional virtual reality and a real instrumented vehicle). The results replicate our previously reported connection between time headway and visual attention. The model reproduces this connection and predicts that it emerges from control of action uncertainty. Implications for traffic psychological models and future developments for psychologically plausible yet computationally rigorous models of full natural task performance are discussed.

The Racer's Mind-How Core Perceptual-Cognitive Expertise Is Reflected in Deliberate Practice Procedures in Professional Motorsport

The exceptional performance of elite practitioners in domains like sports or chess is not a reflection of just exceptional general cognitive ability or innate sensorimotor superiority. Decades of research on expert performance has consistently shown that experts in all fields go to extraordinary lengths to acquire their perceptual-cognitive and motor abilities. Deliberate Practice (DP) refers to special (sub)tasks that are designed to give immediate and accurate feedback and performed repetitively with the explicit goal of improving performance. DP is generally agreed to be one of the key ingredients in acquisition of expertise (not necessarily the only one). Analyzing in detail the specific aspects of performance targeted by DP procedures may shed light on the underlying cognitive processes that support expert performance. Document analysis of professional coaching literature is one knowledge elicitation method that can be used in the early phases of inquiry to glean domain information about the skills experts in a field are required to develop. In this study this approach is applied to the domain of motor racing - specifically the perceptual-cognitive expertise enabling high-speed curve negotiation. A systematic review procedure is used to establish a corpus of texts covering the entire 60 years of professional motorsport textbooks. Descriptions of specific training procedures (that can be unambiguously interpreted as DP procedures) are extracted, and then analyzed within the hierarchical task analysis framework driver modeling. Hypotheses about the underlying cognitive processes are developed on the basis of this material. In the traditional psychological literature, steering and longitudinal control are typically considered “simple” reactive tracking tasks (model-free feedback control). The present findings suggest that—as in other forms expertise—expert level driving skill is in fact dependent on vast body of knowledge, and driven by top-down information. The knowledge elicitation in this study represents a first step toward a deeper psychological understanding of the complex cognitive underpinnings of expert performance in this domain.

A novel computational model to probe visual search deficits during motor performance

Successful execution of many motor skills relies on well-organized visual search (voluntary eye movements that actively scan the environment for task-relevant information). Although impairments of visual search that result from brain injuries are linked to diminished motor performance, the neural processes that guide visual search within this context remain largely unknown. The first objective of this study was to examine how visual search in healthy adults and stroke survivors is used to guide hand movements during the Trail Making Test (TMT), a neuropsychological task that is a strong predictor of visuomotor and cognitive deficits. Our second objective was to develop a novel computational model to investigate combinatorial interactions between three underlying processes of visual search (spatial planning, working memory, and peripheral visual processing). We predicted that stroke survivors would exhibit deficits in integrating the three underlying processes, resulting in deteriorated overall task performance. We found that normal TMT performance is associated with patterns of visual search that primarily rely on spatial planning and/or working memory (but not peripheral visual processing). Our computational model suggested that abnormal TMT performance following stroke is associated with impairments of visual search that are characterized by deficits integrating spatial planning and working memory. This innovative methodology provides a novel framework for studying how the neural processes underlying visual search interact combinatorially to guide motor performance.

NEW & NOTEWORTHY

Visual search has traditionally been studied in cognitive and perceptual paradigms, but little is known about how it contributes to visuomotor performance. We have developed a novel computational model to examine how three underlying processes of visual search (spatial planning, working memory, and peripheral visual processing) contribute to visual search during a visuomotor task. We show that deficits integrating spatial planning and working memory underlie abnormal performance in stroke survivors with frontoparietal damage.