A Two-Point Visual Control Model of Steering

Prospective control of steering through multiple waypoints

Some locomotor tasks involve steering at high speeds through multiple waypoints within cluttered environments. Although in principle actors could treat each individual waypoint in isolation, skillful performance would seem to require them to adapt their trajectory to the most immediate waypoint in anticipation of subsequent waypoints. To date, there have been few studies of such behavior, and the evidence that does exist is inconclusive about whether steering is affected by multiple future waypoints. The present study was designed to address the need for a clearer understanding of how humans adapt their steering movements in anticipation of future goals. Subjects performed a simulated drone flying task in a forest-like virtual environment that was presented on a monitor while their eye movements were tracked. They were instructed to steer through a series of gates while the distance at which gates first became visible (i.e., lookahead distance) was manipulated between trials. When gates became visible at least 1-1/2 segments in advance, subjects successfully flew through a high percentage of gates, rarely collided with obstacles, and maintained a consistent speed. They also approached the most immediate gate in a way that depended on the angular position of the subsequent gate. However, when the lookahead distance was less than 1-1/2 segments, subjects followed longer paths and flew at slower, more variable speeds. The findings demonstrate that the control of steering through multiple waypoints does indeed depend on information from beyond the most immediate waypoint. Discussion focuses on the possible control strategies for steering through multiple waypoints.

Multitasking While Driving: Central Bottleneck or Problem State Interference?

OBJECTIVE

The objective of this work was to investigate if visuospatial attention and working memory load interact at a central control resource or at a task-specific, information processing resource during driving.

BACKGROUND

In previous multitasking driving experiments, interactions between different cognitive concepts (e.g., attention and working memory) have been found. These interactions have been attributed to a central bottleneck or to the so-called problem-state bottleneck, related to working memory usage.

METHOD

We developed two different cognitive models in the cognitive architecture ACT-R, which implement the central vs. problem-state bottleneck. The models performed a driving task, during which we varied visuospatial attention and working memory load. We evaluated the model by conducting an experiment with human participants and compared the behavioral data to the model's behavior.

RESULTS

The problem-state-bottleneck model could account for decreased driving performance due to working memory load as well as increased visuospatial attentional demands as compared to the central-bottleneck model, which could not account for effects of increased working memory load.

CONCLUSION

The interaction between working memory and visuospatial attention in our dual tasking experiment can be best characterized by a bottleneck in the working memory. The model results suggest that as working memory load becomes higher, drivers manage to perform fewer control actions, which leads to decreasing driving performance.

APPLICATION

Predictions about the effect of different mental loads can be used to quantify the contribution of each subtask allowing for precise assessments of the current overall mental load, which automated driving systems may adapt to.

A Review of Human Performance Models for Prediction of Driver Behavior and Interactions With In-Vehicle Technology

OBJECTIVE

This study investigated the use of human performance modeling (HPM) approach for prediction of driver behavior and interactions with in-vehicle technology.

BACKGROUND

HPM has been applied in numerous human factors domains such as surface transportation as it can quantify and predict human performance; however, there has been no integrated literature review for predicting driver behavior and interactions with in-vehicle technology in terms of the characteristics of methods used and variables explored.

METHOD

A systematic literature review was conducted using Compendex, Web of Science, and Google Scholar. As a result, 100 studies met the inclusion criteria and were reviewed by the authors. Model characteristics and variables were summarized to identify the research gaps and to provide a lookup table to select an appropriate method.

RESULTS

The findings provided information on how to select an appropriate HPM based on a combination of independent and dependent variables. The review also summarized the characteristics, limitations, applications, modeling tools, and theoretical bases of the major HPMs.

CONCLUSION

The study provided a summary of state-of-the-art on the use of HPM to model driver behavior and use of in-vehicle technology. We provided a table that can assist researchers to find an appropriate modeling approach based on the study independent and dependent variables.

APPLICATION

The findings of this study can facilitate the use of HPM in surface transportation and reduce the learning time for researchers especially those with limited modeling background.

Modeling Human Steering Behavior in Haptic Shared Control of Autonomy-Enabled Unmanned Ground Vehicles

OBJECTIVE

A human steering model for teleoperated driving is extended to capture the human steering behavior in haptic shared control of autonomy-enabled Unmanned Ground Vehicles (UGVs).

BACKGROUND

Prior studies presented human steering models for teleoperation of a passenger-sized Unmanned Ground Vehicle, where a human is fully in charge of driving. However, these models are not applicable when a human needs to interact with autonomy in haptic shared control of autonomy-enabled UGVs. How a human operator reacts to the presence of autonomy needs to be studied and mathematically encapsulated in a module to capture the collaboration between human and autonomy.

METHOD

Human subject tests are conducted to collect data in haptic shared control for model development and validation. The ACT-R architecture and two-point steering model used in the previous literature are adopted to predict the operator's desired steering angle. A torque conversion module is developed to convert the steering command from the ACT-R model to human torque input, thus enabling haptic shared control with autonomy. A parameterization strategy is described to find the set of model parameters that optimize the haptic shared control performance in terms of minimum average lane keeping error (ALKE).

RESULTS

The model predicts the minimum ALKE human subjects achieve in shared control.

CONCLUSIONS

The extended model can successfully predict the best haptic shared control performance as measured by ALKE.

APPLICATION

This model can be used in place of human operators, enabling fully simulation-based engineering, in the development and evaluation of haptic shared control technologies for autonomy-enabled UGVs, including control negotiation strategies and autonomy capabilities.

Influence of continuous edge-line delineation on drivers' lateral positioning in curves: a gaze-steering approach

Recent research indicates that installing shoulders on rural roads for safety purposes causes drivers to steer further inside on right bends and thus exceed lane boundaries. The present simulator study examined whether continuous rather than broken edge-line delineation would help drivers to keep their vehicles within the lane. The results indicated that continuous delineation significantly impacts the drivers' gaze and steering trajectories. Drivers looked more towards the lane centre and shifted their steering trajectories accordingly. This was accompanied by a significant decrease in lane-departure frequency when driving on a 3.50-m lane but not on a 2.75-m lane. Overall, the findings provide evidence that continuous delineation influences steering control by altering the visual processes underlying trajectory planning. It is concluded that continuous edge-line delineation between lanes and shoulders may induce safer driver behaviour on right bends, which has potential implications for preventing run-off-road crashes and cyclist safety.Practitioner summary: This study examined how continuous and broken edge lines influence driving behaviour around bends with shoulders. With continuous delineation, drivers gazed and steered in the bend further from the edge line and thus had fewer lane departures. Continuous marking can therefore help prevent run-off-road crashes and improve cyclists' safety.

Driver behavior and mental workload for takeover safety in automated driving: ACT-R prediction modeling approach

OBJECTIVE

Conditional automated driving (SAE level 3) requires the driver to take over the vehicle if the automated system fails. The mental workload that can occur in these takeover situations is an important human factor that can directly affect driver behavior and safety, so it is important to predict it. Therefore, this study introduces a method to predict mental workload during takeover situations in automated driving, using the ACT-R (Adaptive Control of Thought-Rational) cognitive architecture. The mental workload prediction model proposed in this study is a computational model that can become the basis for emerging crash avoidance technologies in future autonomous driving situations.

METHODS

The methodology incorporates the ACT-R cognitive architecture, known for its robustness in modeling cognitive processes and predicting performance. The proposed takeover cognitive model includes the symbolic structure for repeatedly checking the driving situation and performing decision-making for takeover as well as Non-Driving-Related Tasks (NDRT). We employed the ACT-R cognitive model to predict mental workload during takeover in automated driving scenarios. The model's predictions are validated against physiological data and performance data from the validation test.

RESULTS

The model demonstrated high accuracy, with an r-square value of 0.97, indicating a strong correlation between the predicted and actual mental workload. It successfully captured the nuances of multitasking in driving scenarios, showcasing the model's adaptability in representing diverse cognitive demands during takeover.

CONCLUSIONS

The study confirms the efficacy of the ACT-R model in predicting mental workload for takeover scenarios in automated driving. It underscores the model's potential in improving driver-assistance systems, enhancing vehicle safety, and ensuring the efficient integration of human-machine roles. The research contributes significantly to the field of cognitive modeling, providing robust predictions and insights into human behavior in automated driving tasks.

Modeling novice law enforcement officers' interaction with in-vehicle technology

Cognitive performance models have been used in several human factors domains such as driving and human-computer interaction. However, most models are limited to expert performance with rough adjustments to consider novices despite prior studies suggesting novices' cognitive, perceptual, and motor behaviors are different from experts. The objective of this study was to develop a cognitive performance model for novice law enforcement officers (N-CPM) to model their performance and memory load while interacting with in-vehicle technology. The model was validated based on a ride-along study with 10 novice law enforcement officers (nLEOs). The findings suggested that there were no significant differences between the N-CPM and observation data in most cases, while the results of the benchmark model were different from that of N-CPM. The model can be applied to improve future nLEO's patrol mission performance through redesigning in-vehicle technologies and training methods to reduce their workload and driving distraction.

Applying the Accumulator model to predict driver's reaction time based on looming in approaching and braking conditions

INTRODUCTION

The prediction of when the driver will react to a change in the lead vehicle motion is critical for assessing rear-end crash risk using car-following models. Past studies have assumed constant reaction time and driver's continuous reaction. However, these assumptions are not valid as the driver's reaction time can vary in different car-following situations and the driver does not continuously react to the lead vehicle motion. Thus, this study predicted the driver's reaction time using the Wiedemann car-following model and the Accumulator model. The Accumulator model assumes the driver's start of reaction based on the accumulation of looming and thereby reflects the driver's intermittent reaction.

METHOD

Fifty drivers' behavior was observed using a driving simulator in two scenarios: (1) approach and follow a moving lead vehicle and (2) approach a stopped lead vehicle. The Accumulator model predicted the reaction times based on different looming variables (angular velocity and tau-inverse), lead vehicle type (car and truck), and lead vehicle brake lights (on or off).

RESULTS

The Accumulator model showed lower prediction errors of the reaction time than the Wiedemann model, which assumes reaction based on the fixed looming threshold. The Accumulator model predicted the reaction times more accurately when it was calibrated with the angular velocity due to width and height of lead vehicles. Moreover, the Accumulator model with tau-inverse produced the smallest prediction error of reaction times among different Accumulator models and the Wiedemann model when lead vehicle brake lights were on.

CONCLUSIONS

This study demonstrates that the Accumulator model is a promising method of predicting the driver's reaction time in car-following situations, which affects rear-end crash risk.

PRACTICAL APPLICATIONS

The Accumulator model can be incorporated into a car-following model for the prediction of reaction times and can estimate the rear-end collision risk of vehicles more accurately.

Self-evaluation of automated vehicles based on physics, state-of-the-art motion prediction and user experience

Legal restrictions allow to give full control to automated vehicles for longer time periods either in restricted areas or when moving with reduced speed. Although being technically feasible for a wide range of driving scenarios, the restrictions are still in place due to the lack of a clear safety strategy. An essential step towards safety is the introduction of a self-monitoring component. In this study, a self-evaluation concept is presented which assesses a system based on a physics-defined minimum prediction horizon for state-of-the-art Deep Learning-based trajectory prediction models. Since User Experience is a key metric for car manufacturers, a further manoeuvre constraint is added to the model. We emphasize the generalizability of the presented assessment concept, however, in order to demonstrate feasibility in practical use, three specific scenarios are discussed. The results are gained with real data from publicly available driving campaigns as well as synthetically generated simulation data. Two exemplary models, a simple LSTM-based model and VectorNet, a prominent motion prediction model, are evaluated. A quantitative assessment shows a lack of training data in the public datasets for vehicle speeds > 25 m/s in order to offer safe driving above such vehicle speeds.

Modelling communication-enabled traffic interactions

A major challenge for autonomous vehicles is handling interactions with human-driven vehicles-for example, in highway merging. A better understanding and computational modelling of human interactive behaviour could help address this challenge. However, existing modelling approaches predominantly neglect communication between drivers and assume that one modelled driver in the interaction responds to the other, but does not actively influence their behaviour. Here, we argue that addressing these two limitations is crucial for the accurate modelling of interactions. We propose a new computational framework addressing these limitations. Similar to game-theoretic approaches, we model a joint interactive system rather than an isolated driver who only responds to their environment. Contrary to game theory, our framework explicitly incorporates communication between two drivers and bounded rationality in each driver's behaviours. We demonstrate our model's potential in a simplified merging scenario of two vehicles, illustrating that it generates plausible interactive behaviour (e.g. aggressive and conservative merging). Furthermore, human-like gap-keeping behaviour emerged in a car-following scenario directly from risk perception without the explicit implementation of time or distance gaps in the model's decision-making. These results suggest that our framework is a promising approach to interaction modelling that can support the development of interaction-aware autonomous vehicles.

Review on Haptic Assistive Driving Systems Based on Drivers’ Steering-Wheel Operating Behaviour

With the availability of modern assistive techniques, ambient intelligence, and the Internet of Things (IoT), various innovative assistive environments have been developed, such as driving assistance systems (DAS), where the human driver can be provided with physical and emotional assistance. In this human–machine collaboration system, haptic interaction interfaces are commonly employed because they provide drivers with a more manageable way to interact with other components. From the view of control system theory, this is a typical closed-loop feedback control system with a human in the loop. To make such a system work effectively, both the driving behaviour factors, and the electrical–mechanical components should be considered. However, the main challenge is how to deal with the high degree of uncertainties in human behaviour. This paper aims to provide an insightful overview of the relevant work. The impact of various types of haptic assistive driving systems (haptic guidance and warning systems) on driving behaviour performance is discussed and evaluated. In addition, various driving behaviour modelling systems are extensively investigated. Furthermore, the state-of-the-art driving behaviour controllers are analysed and discussed in driver–vehicle–road systems, with potential improvements and drawbacks addressed. Finally, a prospective approach is recommended to design a robust model-free controller that accounts for uncertainties and individual differences in driving styles in a haptic assistive driving system. The outcome indicated that the haptic feedback system applied to the drivers enhanced their driving performance, lowered their response time, and reduced their mental workload compared to a system with auditory or visual signals or without any haptic system, despite some annoyances and system conflicts. The driving behaviour modelling techniques and the driving behaviour control with a haptic feedback system have shown good matching and improved the steering wheel’s base operation performance. However, mathematical principles, a statistical approach, and the lack of consideration of the individual differences in the driver–vehicle–road system make the modelling and the controller less robust and inefficient for different driving styles.

Modeling Human Steering Behavior in Teleoperation of Unmanned Ground Vehicles With Varying Speed

OBJECTIVE

This paper extends a prior human operator model to capture human steering performance in the teleoperation of unmanned ground vehicles (UGVs) in path-following scenarios with varying speed.

BACKGROUND

A prior study presented a human operator model to predict human steering performance in the teleoperation of a passenger-sized UGV at constant speeds. To enable applications to varying speed scenarios, the model needs to be extended to incorporate speed control and be able to predict human performance under the effect of accelerations/decelerations and various time delays induced by the teleoperation setting. A strategy is also needed to parameterize the model without human subject data for a truly predictive capability.

METHOD

This paper adopts the ACT-R cognitive architecture and two-point steering model used in the previous work, and extends the model by incorporating a far-point speed control model to allow for varying speed. A parameterization strategy is proposed to find a robust set of parameters for each time delay to maximize steering performance. Human subject experiments are conducted to validate the model.

RESULTS

Results show that the parameterized model can predict both the trend of average lane keeping error and its lowest value for human subjects under different time delays.

CONCLUSIONS

The proposed model successfully extends the prior computational model to predict human steering behavior in a teleoperated UGV with varying speed.

APPLICATION

This computational model can be used to substitute for human operators in the process of development and testing of teleoperated UGV technologies and allows fully simulation-based development and studies.

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.

Gaze Strategies in Driving-An Ecological Approach

Human performance in natural environments is deeply impressive, and still much beyond current AI. Experimental techniques, such as eye tracking, may be useful to understand the cognitive basis of this performance, and "the human advantage." Driving is domain where these techniques may deployed, in tasks ranging from rigorously controlled laboratory settings through high-fidelity simulations to naturalistic experiments in the wild. This research has revealed robust patterns that can be reliably identified and replicated in the field and reproduced in the lab. The purpose of this review is to cover the basics of what is known about these gaze behaviors, and some of their implications for understanding visually guided steering. The phenomena reviewed will be of interest to those working on any domain where visual guidance and control with similar task demands is involved (e.g., many sports). The paper is intended to be accessible to the non-specialist, without oversimplifying the complexity of real-world visual behavior. The literature reviewed will provide an information base useful for researchers working on oculomotor behaviors and physiology in the lab who wish to extend their research into more naturalistic locomotor tasks, or researchers in more applied fields (sports, transportation) who wish to bring aspects of the real-world ecology under experimental scrutiny. Part of a Research Topic on Gaze Strategies in Closed Self-paced tasks, this aspect of the driving task is discussed. It is in particular emphasized why it is important to carefully separate the visual strategies driving (quite closed and self-paced) from visual behaviors relevant to other forms of driver behavior (an open-ended menagerie of behaviors). There is always a balance to strike between ecological complexity and experimental control. One way to reconcile these demands is to look for natural, real-world tasks and behavior that are rich enough to be interesting yet sufficiently constrained and well-understood to be replicated in simulators and the lab. This ecological approach to driving as a model behavior and the way the connection between "lab" and "real world" can be spanned in this research is of interest to anyone keen to develop more ecologically representative designs for studying human gaze behavior.

The effect of inconsistent steering guidance during transitions from Highly Automated Driving

This driving simulator study investigated the effect of inconsistent steering guidance during system and user-initiated transitions from Highly Automated Driving (HAD). In particular, the aim of the study was to understand if steering conflicts could be achieved by adopting inconsistent steering guidance and whether these conflicts could be exploited to accelerate drivers' steering engagement within a limited time. Inconsistent steering guidance was generated by switching the guidance on and off at 3 different frequencies (0.1, 0.2 and 0.3 Hz). Results revealed that steering engagement has more to do with the initiation rather than the quality of the steering guidance. In fact, drivers were more engaged with the steering task when they initiated the transition themselves. Compared to system-initiated transitions, in user-initiated ones, drivers exerted stronger steering inputs throughout the transition, which allowed them to maintain larger Time To Lane Crossing (TTLC) values with fewer steering corrections. During system-initiated transitions, drivers started to actively engage with the steering activity only after more than 5 s from the start of the transition but were able to achieve a steering behaviour close to the one shown during user-initiated transitions at 10 s.

Effects of verbal tasks on driving simulator performance

We report results from a driving simulator paradigm we developed to test the fine temporal effects of verbal tasks on simultaneous tracking performance. A total of 74 undergraduate students participated in two experiments in which they controlled a cursor using the steering wheel to track a moving target and where the dependent measure was overall deviation from target. Experiment 1 tested tracking performance during slow and fast target speeds under conditions involving either no verbal input or output, passive listening to spoken prompts via headphones, or responding to spoken prompts. Experiment 2 was similar except that participants read written prompts overlain on the simulator screen instead of listening to spoken prompts. Performance in both experiments was worse during fast speeds and worst overall during responding conditions. Most significantly, fine scale time-course analysis revealed deteriorating tracking performance as participants prepared and began speaking and steadily improving performance while speaking. Additionally, post-block survey data revealed that conversation recall was best in responding conditions, and perceived difficulty increased with task complexity. Our study is the first to track temporal changes in interference at high resolution during the first hundreds of milliseconds of verbal production and comprehension. Our results are consistent with load-based theories of multitasking performance and show that language production, and, to a lesser extent, language comprehension tap resources also used for tracking. More generally, our paradigm provides a useful tool for measuring dynamical changes in tracking performance during verbal tasks due to the rapidly changing resource requirements of language production and comprehension.

Speed behaviour upon approaching freeway curves

The actual speed behaviour when drivers approach a curve is very relevant to assess the road design and safety but is mostly overlooked in the scientific literature. Most research into curve driving behaviour is focussed at the behaviour inside the curve, although the speed selection is done before curve entry. The main objective of this research is to identify which freeway characteristics play a role in driving speed selection. High Frequency Floating Car Data, detailed reconstruction of the curves and their surroundings, as well as three dimensional sight distance analysis, were used to analyse individual speed profiles on 153 Dutch freeway curves. By defining the positions where the acceleration approaches 0 m/s² before and after a curve starts, the positions when the driver started and stopped decelerating upon curve entry were defined. Further correlation and regression analysis of those positions revealed that the radius of the curve is indeed a main explaining variable, as well as the speed driven before deceleration starts. Sight distances and cross section characteristics play a further role in determining the position where deceleration starts. Deceleration ends at approximately 135 m after curve start, and the speed in a curve is also correlated with the deflection angle and length of a curve. Sight distances do not play a role in selecting the speed in a curve based on this research. Overall, the findings indicate a non-constant nature and variability of speed behaviour upon curve entry. This can be used for safer freeway curve design and to assess traffic safety based on actual speed behaviour.

Communication and Interaction With Semiautonomous Ground Vehicles by Force Control Steering

While full automation of road vehicles remains a future goal, shared-control and semiautonomous driving-involving transitions of control between the human and the machine-are more feasible objectives in the near term. These alternative driving modes will benefit from new research toward novel steering control devices, more suitably where machine intelligence only partially controls the vehicle. In this article, it is proposed that when the human shares the control of a vehicle with an autonomous or semiautonomous system, a force control, or nondisplacement steering wheel (i.e., a steering wheel which does not rotate but detects the applied torque by the human driver) can be advantageous under certain schemes: tight rein or loose rein modes according to the H -metaphor. We support this proposition with the first experiments to the best of our knowledge, in which human participants drove in a simulated road scene with a force control steering wheel (FCSW). The experiments exhibited that humans can adapt promptly to force control steering and are able to control the vehicle smoothly. Different transfer functions are tested, which translate the applied torque at the FCSW to the steering angle at the wheels of the vehicle; it is shown that fractional order transfer functions increment steering stability and control accuracy when using a force control device. The transition of control experiments is also performed with both: a conventional and an FCSW. This prototypical steering system can be realized via steer-by-wire controls, which are already incorporated in commercially available vehicles.

A driving simulation study on the effects of different wine types on the performance of young drivers

BACKGROUND

Alcohol consumption is responsible for a significant number of road fatalities. To contrast this phenomenon, a more responsible attitude to the wine consumption, especially among young, inexperienced drivers prone to risky behaviour on the road must be promoted.

METHOD

This is a simplified single-blind, placebo-controlled experiment aimed at evaluating 44 young drivers monitored during a driving simulation following the consumption of natural and conventional wines, with a reference blood alcohol concentration (BAC) of 0.5 g/l. Two hypotheses are tested: (1) the legal consumption of wine has no significant impact on young drivers' performance in both ordinary and unusual road events; (2) natural and conventional wines are expected to produce negligible and acceptable impairments in young drivers the same BAC. Two reference groups (BAC = 0 g/l), one a placebo-controlled group with drivers treated with a dealcoholized wine, were included.

RESULTS AND CONCLUSIONS

Significant differences between the groups in terms of perception and reaction times (PRT) to visual and auditory stimuli, and to speeding were observed, with young drivers treated with conventional wine displaying more aggressive behaviours. In contrast, participants treated with natural wine showed PRT which were not significantly different from those belonging to control groups. The gaze attention levels of wine treated drivers were found to be dose dependant, with young drivers of the two control groups and those of the treated ones with BAC < 0.3 g/l able to focus on wider area ahead and, thereby, collect more information from the road environment.

Drivers' Spatio-Temporal Attentional Distributions Are Influenced by Vehicle Dynamics and Displayed Point of View

OBJECTIVE

The aim of this study is to measure drivers' attention to preview and their velocity and acceleration tracking error to evaluate two- and three-dimensional displays for following a winding roadway.

BACKGROUND

Display perturbation techniques and Fourier analysis of steering movements can be used to infer drivers' spatio-temporal distribution of attention to preview. Fourier analysis of tracking error time histories provides measures of position, velocity, and acceleration error.

METHOD

Participants tracked a winding roadway with 1 s of preview in low-fidelity driving simulations. Position and rate-aided vehicle dynamics were paired with top-down and windshield displays of the roadway.

RESULTS

For both vehicle dynamics, tracking was smoother with the windshield display. This display emphasizes nearer preview positions and has a closer correspondence to the control-theoretic optimal attentional distributions for these tasks than the top-down display. This correspondence is interpreted as a form of stimulus-response compatibility. The position error and attentional signal-to-noise ratios did not differ between the two displays with position control, but with more complex rate-aided control much higher position error and much lower attentional signal-to-noise ratios occurred with the top-down display.

CONCLUSION

Display-driven influences on the distribution of attention may facilitate tracking with preview when they are similar to optimal attentional distributions derived from control theory.

APPLICATION

Display perturbation techniques can be used to assess spatially distributed attention to evaluate displays and secondary tasks in the context of driving. This methodology can supplement eye movement measurements to determine what information is guiding drivers' actions.

Memory Pattern Identification for Feedback Tracking Control in Human-Machine Systems

OBJECTIVE

The aim of this paper was to identify the characteristics of memory patterns with respect to a visual input, perceived by the human operator during a manual control task, which consisted in following a moving target on a display with a cursor.

BACKGROUND

Manual control tasks involve nondeclarative memory. The memory encodings of different motor skills have been referred to as procedural memories. The procedural memories have a pattern, which this paper sought to identify for the particular case of a one-dimensional tracking task. Specifically, data recorded from human subjects controlling dynamic systems with different fractional order were investigated.

METHOD

A finite impulse response (FIR) controller was fitted to the data, and pattern analysis of the fitted parameters was performed. Then, the FIR model was further reduced to a lower order controller; from the simplified model, the stability analysis of the human-machine system in closed-loop was conducted.

RESULTS

It is shown that the FIR model can be used to identify and represent patterns in human procedural memories during manual control tasks. The obtained procedural memory pattern presents a time scale of about 650 ms before decay. Furthermore, the fitted controller is stable for systems with fractional order less than or equal to 1.

CONCLUSION

For systems of different fractional order, the proposed control scheme-based on an FIR model-can effectively characterize the linear properties of manual control in humans.

APPLICATION

This research supports a biofidelic approach to human manual control modeling over feedback visual perceptions. Relevant applications of this research are the following: the development of shared-control systems, where a virtual human model assists the human during a control task, and human operator state monitoring.

Dynamic scan paths investigations under manual and highly automated driving

Active visual scanning of the scene is a key task-element in all forms of human locomotion. In the field of driving, steering (lateral control) and speed adjustments (longitudinal control) models are largely based on drivers’ visual inputs. Despite knowledge gained on gaze behaviour behind the wheel, our understanding of the sequential aspects of the gaze strategies that actively sample that input remains restricted. Here, we apply scan path analysis to investigate sequences of visual scanning in manual and highly automated simulated driving. Five stereotypical visual sequences were identified under manual driving: forward polling (i.e. far road explorations), guidance, backwards polling (i.e. near road explorations), scenery and speed monitoring scan paths. Previously undocumented backwards polling scan paths were the most frequent. Under highly automated driving backwards polling scan paths relative frequency decreased, guidance scan paths relative frequency increased, and automation supervision specific scan paths appeared. The results shed new light on the gaze patterns engaged while driving. Methodological and empirical questions for future studies are discussed.

Spatio-Temporal Flexibility of Attention Inferred from Drivers' Steering Movements

Participants attempted to center a cursor on a video display of a winding roadway with a rate control system. Fourier analysis of their steering movements in response to sinusoidal perturbations of the roadway revealed how much attention they allocated to different roadway preview locations. We compared a full 1.0 s of preview with preview restricted to a narrow slit around 0.3 s or 0.6 s. Participants were able to flexibly shift their attention to either slit. However, they performed better in terms of root-mean-squared error, velocity error, and acceleration error with the fuller view. They concentrated their attention over a range from 0.1 s to 0.3 s of preview in a manner qualitatively consistent with Miller's optimal control model.

Drivers use active gaze to monitor waypoints during automated driving

Automated vehicles (AVs) will change the role of the driver, from actively controlling the vehicle to primarily monitoring it. Removing the driver from the control loop could fundamentally change the way that drivers sample visual information from the scene, and in particular, alter the gaze patterns generated when under AV control. To better understand how automation affects gaze patterns this experiment used tightly controlled experimental conditions with a series of transitions from 'Manual' control to 'Automated' vehicle control. Automated trials were produced using either a 'Replay' of the driver's own steering trajectories or standard 'Stock' trials that were identical for all participants. Gaze patterns produced during Manual and Automated conditions were recorded and compared. Overall the gaze patterns across conditions were very similar, but detailed analysis shows that drivers looked slightly further ahead (increased gaze time headway) during Automation with only small differences between Stock and Replay trials. A novel mixture modelling method decomposed gaze patterns into two distinct categories and revealed that the gaze time headway increased during Automation. Further analyses revealed that while there was a general shift to look further ahead (and fixate the bend entry earlier) when under automated vehicle control, similar waypoint-tracking gaze patterns were produced during Manual driving and Automation. The consistency of gaze patterns across driving modes suggests that active-gaze models (developed for manual driving) might be useful for monitoring driver engagement during Automated driving, with deviations in gaze behaviour from what would be expected during manual control potentially indicating that a driver is not closely monitoring the automated system.

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.

Driver's Preview Modeling Based on Visual Characteristics through Actual Vehicle Tests

This paper proposes a method for obtaining driver's fixation points and establishing a preview model based on actual vehicle tests. Firstly, eight drivers were recruited to carry out the actual vehicle test on the actual straight and curved roads. The curvature radii of test curved roads were selected to be 200, 800, and 1500 m. Subjects were required to drive at a speed of 50, 70 and 90 km/h, respectively. During the driving process, eye movement data of drivers were collected using a head-mounted eye tracker, and road front scene images and vehicle statuses were collected simultaneously. An image-world coordinate mapping model of the visual information of drivers was constructed by performing an image distortion correction and matching the images from the driving recorder. Then, fixation point data for drivers were accordingly obtained using the Identification-Deviation Threshold (I-DT) algorithm. In addition, the Jarque-Bera test was used to verify the normal distribution characteristics of these data and to fit the distribution parameters of the normal function. Furthermore, the preview points were extracted accordingly and projected into the world coordinate. At last, the preview data obtained under these conditions are fit to build general preview time probability density maps for different driving speeds and road curvatures. This study extracts the preview characteristics of drivers through actual vehicle tests, which provides a visual behavior reference for the humanized vehicle control of an intelligent vehicle.

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.

Humans use Optokinetic Eye Movements to Track Waypoints for Steering

It is well-established how visual stimuli and self-motion in laboratory conditions reliably elicit retinal-image-stabilizing compensatory eye movements (CEM). Their organization and roles in natural-task gaze strategies is much less understood: are CEM applied in active sampling of visual information in human locomotion in the wild? If so, how? And what are the implications for guidance? Here, we directly compare gaze behavior in the real world (driving a car) and a fixed base simulation steering task. A strong and quantifiable correspondence between self-rotation and CEM counter-rotation is found across a range of speeds. This gaze behavior is "optokinetic", i.e. optic flow is a sufficient stimulus to spontaneously elicit it in naïve subjects and vestibular stimulation or stereopsis are not critical. Theoretically, the observed nystagmus behavior is consistent with tracking waypoints on the future path, and predicted by waypoint models of locomotor control - but inconsistent with travel point models, such as the popular tangent point model.

Visual control of steering in curve driving

This pair of studies investigated steering in the absence of continuous visual information. In a driving simulator, participants steered a curving path that was displayed either continuously or intermittently. Optic flow conditions were manipulated to alter the nature of the heading information with respect to the path being steered. Removing or biasing heading information had little effect on steering even during long and frequent path occlusions as long as turn rate was available. This demonstrates that participants can use intermittent views of the path to plan their steering actions and optic flow to accurately update vehicle turns with respect to that path.

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.

Looking at the Road When Driving Around Bends: Influence of Vehicle Automation and Speed

When negotiating bends car drivers perform gaze polling: their gaze shifts between guiding fixations (GFs; gaze directed 1–2 s ahead) and look-ahead fixations (LAFs; longer time headway). How might this behavior change in autonomous vehicles where the need for constant active visual guidance is removed? In this driving simulator study, we analyzed this gaze behavior both when the driver was in charge of steering or when steering was delegated to automation, separately for bend approach (straight line) and the entry of the bend (turn), and at various speeds. The analysis of gaze distributions relative to bend sections and driving conditions indicate that visual anticipation (through LAFs) is most prominent before entering the bend. Passive driving increased the proportion of LAFs with a concomitant decrease of GFs, and increased the gaze polling frequency. Gaze polling frequency also increased at higher speeds, in particular during the bend approach when steering was not performed. LAFs encompassed a wide range of eccentricities. To account for this heterogeneity two sub-categories serving distinct information requirements are proposed: mid-eccentricity LAFs could be more useful for anticipatory planning of steering actions, and far-eccentricity LAFs for monitoring potential hazards. The results support the idea that gaze and steering coordination may be strongly impacted in autonomous vehicles.

Effects of an Active Visuomotor Steering Task on Covert Attention

In complex dynamic tasks such as driving it is essential to be aware of potentially important targets in peripheral vision. While eye tracking methods in various driving tasks have provided much information about drivers’ gaze strategies, these methods only inform about overt attention and provide limited grounds to assess hypotheses concerning covert attention. We adapted the Posner cue paradigm to a dynamic steering task in a driving simulator. The participants were instructed to report the presence of peripheral targets while their gaze was fixed to the road. We aimed to see whether and how the active steering task and complex visual stimulus might affect directing covert attention to the visual periphery. In a control condition, the detection task was performed without a visual scene and active steering. Detection performance in bends was better in the control task compared to corresponding performance in the steering task, indicating that active steering and the complex visual scene affected the ability to distribute covert attention. Lower targets were discriminated slower than targets at the level of the fixation circle in both conditions. We did not observe higher discriminability for on-road targets. The results may be accounted for by either bottom-up optic flow biasing of attention, or top-down saccade planning.

Humans Use Predictive Gaze Strategies to Target Waypoints for Steering

A major unresolved question in understanding visually guided locomotion in humans is whether actions are driven solely by the immediately available optical information (model-free online control mechanisms), or whether internal models have a role in anticipating the future path. We designed two experiments to investigate this issue, measuring spontaneous gaze behaviour while steering, and predictive gaze behaviour when future path information was withheld. In Experiment 1 participants (N = 15) steered along a winding path with rich optic flow: gaze patterns were consistent with tracking waypoints on the future path 1–3 s ahead. In Experiment 2, participants (N = 12) followed a path presented only in the form of visual waypoints located on an otherwise featureless ground plane. New waypoints appeared periodically every 0.75 s and predictably 2 s ahead, except in 25% of the cases the waypoint at the expected location was not displayed. In these cases, there were always other visible waypoints for the participant to fixate, yet participants continued to make saccades to the empty, but predictable, waypoint locations (in line with internal models of the future path guiding gaze fixations). This would not be expected based upon existing model-free online steering control models, and strongly points to a need for models of steering control to include mechanisms for predictive gaze control that support anticipatory path following behaviours.

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.

Gaze doesn't always lead steering

In car driving, gaze typically leads the steering when negotiating curves. The aim of the current study was to investigate whether drivers also use this gaze-leads-steering strategy when time-sharing between driving and a visual secondary task. Fourteen participants drove an instrumented car along a motorway while performing a secondary task: looking at a specified visual target as long and as much as they felt it was safe to do so. They made six trips, and in each trip the target was at a different location relative to the road ahead. They were free to glance back at the road at any time. Gaze behaviour was measured with an eye tracker, and steering corrections were recorded from the vehicle's CAN bus. Both in-car 'Fixation' targets and outside 'Pursuit' targets were used. Drivers often used a gaze-leads-steering strategy, glancing at the road ahead 200-600 ms before executing steering corrections. However, when the targets were less eccentric (requiring a smaller change in glance direction relative to the road ahead), the reverse strategy, in which glances to the road ahead followed steering corrections with 0-400 ms latency, was clearly present. The observed use of strategies can be interpreted in terms of predictive processing: The gaze-leads-steering strategy is driven by the need to update the visual information and is therefore modulated by the quality/quantity of peripheral information. Implications for steering models are discussed.

Metacognitive judgements of perceptual-motor steering performance

Control of skilled actions requires rapid information sampling and processing, which may largely be carried out subconsciously. However, individuals often need to make conscious strategic decisions that ideally would be based upon accurate knowledge of performance. Here, we determined the extent to which individuals have explicit awareness of their steering performance (conceptualised as "metacognition"). Participants steered in a virtual environment along a bending road while attempting to keep within a central demarcated target zone. Task demands were altered by manipulating locomotor speed (fast/slow) and the target zone (narrow/wide). All participants received continuous visual feedback about position in zone, and one sub-group was given additional auditory warnings when exiting/entering the zone. At the end of each trial, participants made a metacognitive evaluation: the proportion of the trial they believed was spent in the zone. Overall, although evaluations broadly shifted in line with task demands, participants showed limited calibration to performance. Regression analysis showed that evaluations were influenced by two components: (a) direct monitoring of performance and (b) indirect task heuristics estimating performance based on salient cues (e.g., speed). Evaluations often weighted indirect task heuristics inappropriately, but the additional auditory feedback improved evaluations seemingly by reducing this weighting. These results have important implications for all motor tasks where conscious cognitive control can be used to influence action selection.

Steering bends and changing lanes: The impact of optic flow and road edges on two point steering control

Successful driving involves steering corrections that respond to immediate positional errors while also anticipating upcoming changes to the road layout ahead. In popular steering models these tasks are often treated as separate functions using two points: the near region for correcting current errors, and the far region for anticipating future steering requirements. Whereas two-point control models can capture many aspects of driver behavior, the nature of perceptual inputs to these two "points" remains unclear. Inspired by experiments that solely focused on road-edge information (Land & Horwood, 1995), two-point models have tended to ignore the role of optic flow during steering control. There is recent evidence demonstrating that optic flow should be considered within two-point control steering models (Mole, Kountouriotis, Billington, & Wilkie, 2016). To examine the impact of optic flow and road edges on two-point steering control we used a driving simulator to selectively and systematically manipulate these components. We removed flow and/or road-edge information from near or far regions of the scene, and examined how behaviors changed when steering along roads where the utility of far-road information varied. While steering behaviors were strongly influenced by the road-edges, there were also clear contributions of optic flow to steering responses. The patterns of steering were not consistent with optic flow simply feeding into two-point control; rather, the global optic flow field appeared to support effective steering responses across the time-course of each trajectory.

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.

When flow is not enough: evidence from a lane changing task

Humans are able to estimate their heading on the basis of optic flow information and it has been argued that we use flow in this way to guide navigation. Consistent with this idea, several studies have reported good navigation performance in flow fields. However, one criticism of these studies is that they have generally focused on the task of walking or steering towards a target, offering an additional, salient directional cue. Hence, it remains a matter of debate as to whether humans are truly able to control steering in the presence of optic flow alone. In this study, we report a set of maneuvers carried out in flow fields in the absence of a physical target. To do this, we studied the everyday task of lane changing, a commonplace multiphase steering maneuver which can be conceptualized without the need for a target. What is more (and here is the crucial quirk), previous literature has found that in the absence of visual feedback, drivers show a systematic, asymmetric steering response, resulting in a systematic final heading error. If optic flow is sufficient for controlling navigation through our environment, we would expect this asymmetry to disappear whenever optic flow is provided. However, our results show that this asymmetry persisted, even in the presence of a flow field, implying that drivers are unable to use flow to guide normal steering responses in this task.

Steering Control in a Low-Cost Driving Simulator: A Case for the Role of Virtual Vehicle Cab

OBJECTIVE

The aim of this study was to investigate steering control in a low-cost driving simulator with and without a virtual vehicle cab.

BACKGROUND

In low-cost simulators, the lack of a vehicle cab denies driver access to vehicle width, which could affect steering control, insofar as locomotor adjustments are known to be based on action-scaled visual judgments of the environment.

METHOD

Two experiments were conducted in which steering control with and without a virtual vehicle cab was investigated in a within-subject design, using cornering and straight-lane-keeping tasks.

RESULTS

Driving around curves without vehicle cab information made drivers deviate more from the lane center toward the inner edge in right (virtual cab = 4 ± 19 cm; no cab = 42 ± 28 cm; at the apex of the curve, p < .001) but not in left curves. More lateral deviation from the lane center toward the edge line was also found in driving without the virtual cab on straight roads (virtual cab = 21 ± 28 cm; no cab = 36 ± 27 cm; p < .001), whereas driving stability and presence ratings were not affected. In both experiments, the greater lateral deviation in the no-cab condition led to significantly more time driving off the lane.

CONCLUSION

The findings strongly suggest that without cab information, participants underestimate the distance to the right edge of the car (in contrast to the left edge) and thus vehicle width. This produces considerable differences in the steering trajectory.

APPLICATION

Providing a virtual vehicle cab must be encouraged for more effectively capturing drivers' steering control in low-cost simulators.

Modeling Human Steering Behavior During Path Following in Teleoperation of Unmanned Ground Vehicles

OBJECTIVE

This paper presents a behavioral model representing the human steering performance in teleoperated unmanned ground vehicles (UGVs).

BACKGROUND

Human steering performance in teleoperation is considerably different from the performance in regular onboard driving situations due to significant communication delays in teleoperation systems and limited information human teleoperators receive from the vehicle sensory system. Mathematical models capturing the teleoperation performance are a key to making the development and evaluation of teleoperated UGV technologies fully simulation based and thus more rapid and cost-effective. However, driver models developed for the typical onboard driving case do not readily address this need.

METHOD

To fill the gap, this paper adopts a cognitive model that was originally developed for a typical highway driving scenario and develops a tuning strategy that adjusts the model parameters in the absence of human data to reflect the effect of various latencies and UGV speeds on driver performance in a teleoperated path-following task.

RESULTS

Based on data collected from a human subject test study, it is shown that the tuned model can predict both the trend of changes in driver performance for different driving conditions and the best steering performance of human subjects in all driving conditions considered.

CONCLUSIONS

The proposed model with the tuning strategy has a satisfactory performance in predicting human steering behavior in the task of teleoperated path following of UGVs.

APPLICATION

The established model is a suited candidate to be used in place of human drivers for simulation-based studies of UGV mobility in teleoperation systems.

Sustained sensorimotor control as intermittent decisions about prediction errors: computational framework and application to ground vehicle steering

A conceptual and computational framework is proposed for modelling of human sensorimotor control and is exemplified for the sensorimotor task of steering a car. The framework emphasises control intermittency and extends on existing models by suggesting that the nervous system implements intermittent control using a combination of (1) motor primitives, (2) prediction of sensory outcomes of motor actions, and (3) evidence accumulation of prediction errors. It is shown that approximate but useful sensory predictions in the intermittent control context can be constructed without detailed forward models, as a superposition of simple prediction primitives, resembling neurobiologically observed corollary discharges. The proposed mathematical framework allows straightforward extension to intermittent behaviour from existing one-dimensional continuous models in the linear control and ecological psychology traditions. Empirical data from a driving simulator are used in model-fitting analyses to test some of the framework's main theoretical predictions: it is shown that human steering control, in routine lane-keeping and in a demanding near-limit task, is better described as a sequence of discrete stepwise control adjustments, than as continuous control. Results on the possible roles of sensory prediction in control adjustment amplitudes, and of evidence accumulation mechanisms in control onset timing, show trends that match the theoretical predictions; these warrant further investigation. The results for the accumulation-based model align with other recent literature, in a possibly converging case against the type of threshold mechanisms that are often assumed in existing models of intermittent control.

Differences between racing and non-racing drivers: A simulator study using eye-tracking

Motorsport has developed into a professional international competition. However, limited research is available on the perceptual and cognitive skills of racing drivers. By means of a racing simulator, we compared the driving performance of seven racing drivers with ten non-racing drivers. Participants were tasked to drive the fastest possible lap time. Additionally, both groups completed a choice reaction time task and a tracking task. Results from the simulator showed faster lap times, higher steering activity, and a more optimal racing line for the racing drivers than for the non-racing drivers. The non-racing drivers’ gaze behavior corresponded to the tangent point model, whereas racing drivers showed a more variable gaze behavior combined with larger head rotations while cornering. Results from the choice reaction time task and tracking task showed no statistically significant difference between the two groups. Our results are consistent with the current consensus in sports sciences in that task-specific differences exist between experts and novices while there are no major differences in general cognitive and motor abilities.

Measuring Memory and Attention to Preview in Motion

Objective Use perceptual-motor responses to perturbations to reveal the spatio-temporal detail of memory for the recent past and attention to preview when participants track a winding roadway. Background Memory of the recently passed roadway can be inferred from feedback control models of the participants' manual movement patterns. Similarly, attention to preview of the upcoming roadway can be inferred from feedforward control models of manual movement patterns. Method Perturbation techniques were used to measure these memory and attention functions. Results In a laboratory tracking task, the bandwidth of lateral roadway deviations was found to primarily influence memory for the past roadway rather than attention to preview. A secondary auditory/verbal/vocal memory task resulted in higher velocity error and acceleration error in the tracking task but did not affect attention to preview. Attention to preview was affected by the frequency pattern of sinusoidal perturbations of the roadway. Conclusion Perturbation techniques permit measurement of the spatio-temporal span of memory and attention to preview that affect tracking a winding roadway. They also provide new ways to explore goal-directed forgetting and spatially distributed attention in the context of movement. More generally, these techniques provide sensitive measures of individual differences in cognitive aspects of action. Application Models of driving behavior and assessment of driving skill may benefit from more detailed spatio-temporal measurement of attention to preview.

Adaptive Gaze Strategies for Locomotion with Constricted Visual Field

In retinitis pigmentosa (RP), loss of peripheral visual field accounts for most difficulties encountered in visuo-motor coordination during locomotion. The purpose of this study was to accurately assess the impact of peripheral visual field loss on gaze strategies during locomotion, and identify compensatory mechanisms. Nine RP subjects presenting a central visual field limited to 10-25° in diameter, and nine healthy subjects were asked to walk in one of three directions-straight ahead to a visual target, leftward and rightward through a door frame, with or without obstacle on the way. Whole body kinematics were recorded by motion capture, and gaze direction in space was reconstructed using an eye-tracker. Changes in gaze strategies were identified in RP subjects, including extensive exploration prior to walking, frequent fixations of the ground (even knowing no obstacle was present), of door edges, essentially of the proximal one, of obstacle edge/corner, and alternating door edges fixations when approaching the door. This was associated with more frequent, sometimes larger rapid-eye-movements, larger movements, and forward tilting of the head. Despite the visual handicap, the trajectory geometry was identical between groups, with a small decrease in walking speed in RPs. These findings identify the adaptive changes in sensory-motor coordination, in order to ensure visual awareness of the surrounding, detect changes in spatial configuration, collect information for self-motion, update the postural reference frame, and update egocentric distances to environmental objects. They are of crucial importance for the design of optimized rehabilitation procedures.

Systematic Observation of an Expert Driver's Gaze Strategy-An On-Road Case Study

In this paper we present and qualitatively analyze an expert driver's gaze behavior in natural driving on a real road, with no specific experimental task or instruction. Previous eye tracking research on naturalistic tasks has revealed recurring patterns of gaze behavior that are surprisingly regular and repeatable. Lappi (2016) identified in the literature seven “qualitative laws of gaze behavior in the wild”: recurring patterns that tend to go together, the more so the more naturalistic the setting, all of them expected in extended sequences of fully naturalistic behavior. However, no study to date has observed all in a single experiment. Here, we wanted to do just that: present observations supporting all the “laws” in a single behavioral sequence by a single subject. We discuss the laws in terms of unresolved issues in driver modeling and open challenges for experimental and theoretical development.