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Understanding Drivers’ Steering Behavior: Chain And One-Time Corrections
Abstract
Drivers’ steering adjustments can be categorized into one-time and chain corrections. One-time corrections lead to no further steering corrections for a minimum of one second, while chain corrections have at least two consecutive steering actions. Chain corrections represent a novel indicator of steering instability. Evolving vehicle dynamics along with drivers’ state and situational factors can cause these different correction types. In a driving simulator study, drivers’ experienced different roadway widths with and without distraction. The results show that higher steering wheel angle values at the beginning or end of a correction lead to chain corrections and the duration of these corrections tends to be shorter than adjustments not leading to chain corrections. Exploring the underlying causes of different corrections can guide efforts to model drivers’ control actions in recovering from distractions and in taking over control during automation failures.
... Monitoring the steering wheel angle has also shown effective detection of impairment. Impaired drivers are known to steer more sporadically, which can cause the user to drift from the center and over correct the mistake [61], [62]. However, any behavioral monitoring should not be done in isolation from environmental monitoring, as defensive driving may dictate that the driver regularly deviates from the ideal driver profile. ...
According to the United States Department of Justice, 28 people in the United States die every day because of drunk driving. As self-driving vehicles become more prevalent, the ability for automated cars to determine when the driver is impaired and to then take control, could save many lives. Past research has looked at the certain indicators for impaired driving, but to date there has been relatively little consideration of the potential interaction between an impaired driver and a self-driving vehicle. In this paper, we will review the existing literature in order to recap the possible approaches vehicle manufacturers could take in establishing that a driver is impaired: physiological, behavioral, and vigilance monitoring. These approaches will be contrasted with one another. Following the review, we will propose several design solutions to be developed and tested. These solutions include a 'full', 'partial', and 'supervisory' takeover by the vehicle. Our full takeover proposed design will provide no opportunity for driver input at any point. Our partial takeover proposed design will involve a full takeover, but with additional impairment tests that the driver can perform in order to demonstrate capacity to drive. Finally, our supervisory takeover proposed design will involve the system actively monitoring performance in order to more quickly engage safety procedures (e.g. lane keep and emergency braking). The relative benefits and consequences of each design will be discussed with an eye towards existing theories on human-automation interaction. Finally, we will propose a path forward for design and testing. Taken together, this paper will present a novel consideration of a new avenue for human-machine teaming. Such considerations could be instrumental in saving thousands of lives each year, and helping to prevent countless other injuries.
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 observations from a driving simulator provide support for some of the framework assumptions: 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 steering adjustments, than as continuous control. Furthermore, the amplitudes of individual steering adjustments are well predicted by a compound visual cue signalling steering error, and even better so if also adjusting for predictions of how the same cue is affected by previous control. Finally, evidence accumulation is shown to explain observed covariability between inter-adjustment durations and adjustment amplitudes, seemingly better so than the type of threshold mechanisms that are typically assumed in existing models of intermittent control.
In this review, we summarize findings supporting the existence of multiple behavioral strategies for controlling reward-related behavior, including a dichotomy between the goal-directed or model-based system and the habitual or model-free system in the domain of instrumental conditioning and a similar dichotomy in the realm of Pavlovian conditioning. We evaluate evidence from neuroscience supporting the existence of at least partly distinct neuronal substrates contributing to the key computations necessary for the function of these different control systems. We consider the nature of the interactions between these systems and show how these interactions can lead to either adaptive or maladaptive behavioral outcomes. We then review evidence that an additional system guides inference concerning the hidden states of other agents, such as their beliefs, preferences, and intentions, in a social context. We also describe emerging evidence for an arbitration mechanism between model-based and model-free reinforcement learning, placing such a mechanism within the broader context of the hierarchical control of behavior. Expected final online publication date for the Annual Review of Psychology Volume 68 is January 03, 2017. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
In this paper, a general and fundamental property of steering is demonstrated: It is shown that steering corrections generally follow bell-shaped profiles of steering rate. The finding is strongly related to what is already known about reaching movements. Also, a strong linear relationship was found between the maximum steering wheel rate and the steering wheel deflection, something that indicates a constant movement time for the correction. Furthermore, by closer examination of those corrections that cannot be described by a single bell-shaped rate profile, it was found that they typically can be described using two or, in some cases three or four, overlapping profiles, something which relates to superposition of motor primitives.
Behavioral data obtained with perceptual decision making experiments are typically analyzed with the drift-diffusion model. This parsimonious model accumulates noisy pieces of evidence toward a decision bound to explain the accuracy and reaction times of subjects. Recently, Bayesian models have been proposed to explain how the brain extracts information from noisy input as typically presented in perceptual decision making tasks. It has long been known that the drift-diffusion model is tightly linked with such functional Bayesian models but the precise relationship of the two mechanisms was never made explicit. Using a Bayesian model, we derived the equations which relate parameter values between these models. In practice we show that this equivalence is useful when fitting multi-subject data. We further show that the Bayesian model suggests different decision variables which all predict equal responses and discuss how these may be discriminated based on neural correlates of accumulated evidence. In addition, we discuss extensions to the Bayesian model which would be difficult to derive for the drift-diffusion model. We suggest that these and other extensions may be highly useful for deriving new experiments which test novel hypotheses.
We frequently need to make timely decisions based on sensory evidence that is weak, ambiguous, or noisy resulting from conditions in the external environment (e.g., a cluttered visual scene) or within the brain itself (e.g., inattention, neural noise). Here we examine how externally and internally driven variations in the quality of sensory evidence affect the build-to-threshold dynamics of a supramodal "decision variable" signal and, hence, the timing and accuracy of decision reports in humans. Observers performed a continuous-monitoring version of the prototypical two-alternative dot-motion discrimination task, which is known to strongly benefit from sequential sampling and temporal accumulation of evidence. A centroparietal positive potential (CPP), which we previously established as a supramodal decision signal based on its invariance to motor or sensory parameters, exhibited two key identifying properties associated with the "decision variable" long described in sequential sampling models: (1) its buildup rate systematically scaled with sensory evidence strength across four levels of motion coherence, consistent with temporal integration; and (2) its amplitude reached a stereotyped level at the moment of perceptual report executions, consistent with a boundary-crossing stopping criterion. The buildup rate of the CPP also strongly predicted reaction time within coherence levels (i.e., independent of physical evidence strength), and this endogenous variation was linked with attentional fluctuations indexed by the level of parieto-occipital α-band activity preceding target onset. In tandem with the CPP, build-to-threshold dynamics were also observed in an effector-selective motor preparation signal; however, the buildup of this motor-specific process significantly lagged that of the supramodal process.
Direct comparisons were made of expert highway engineers' use of analytical, quasirational, and intuitive cognition on three different tasks, each displayed in three different ways. Use of a systems approach made it possible to develop indices for measuring the location of each of the nine information display conditions on a continuum ranging from intuition-inducing to analysis-inducing and for measuring the location of each expert engineer's cognition on a continuum ranging from intuition to analysis. Individual analyses of each expert's performance over the nine conditions showed that the location of the task on the task index induced the expert's cognition to be located at the corresponding region on the cognitive continuum index. Intuitive and quasirational cognition frequently outperformed analytical cognition in terms of the empirical accuracy of judgments. Judgmental accuracy was related to the degree of correspondence between the type of task and the type of cognitive activity on the cognitive continuum.
Cognitive control is by now a large umbrella term referring collectively to multiple processes that plan and coordinate actions to meet task goals. A common feature of paradigms that engage cognitive control is the task requirement to select relevant information despite a habitual tendency (or bias) to select goal-irrelevant information. At least since the 1970s, researchers have employed proportion congruent (PC) manipulations to experimentally establish selection biases and evaluate the mechanisms used to control attention. PC manipulations vary the frequency with which irrelevant information conflicts (i.e., is incongruent) with relevant information. The purpose of this review is to summarize the growing body of literature on PC effects across selective attention paradigms, beginning first with Stroop, and then describing parallel effects in flanker and task-switching paradigms. The review chronologically tracks the expansion of the PC manipulation from its initial implementation at the list-wide level, to more recent implementations at the item-specific and context-specific levels. An important theoretical aim is demonstrating that PC effects at different levels (e.g., list-wide vs. item or context-specific) support a distinction between voluntary forms of cognitive control, which operate based on anticipatory information, and relatively automatic or reflexive forms of cognitive control, which are rapidly triggered by the processing of particular stimuli or stimulus features. A further aim is to highlight those PC manipulations that allow researchers to dissociate stimulus-driven control from other stimulus-driven processes (e.g., S-R responding; episodic retrieval). We conclude by discussing the utility of PC manipulations for exploring the distinction between voluntary control and stimulus-driven control in other relevant paradigms.
Stochastic accumulator models account for response time in perceptual decision-making tasks by assuming that perceptual evidence accumulates to a threshold. The present investigation mapped the firing rate of frontal eye field (FEF) visual neurons onto perceptual evidence and the firing rate of FEF movement neurons onto evidence accumulation to test alternative models of how evidence is combined in the accumulation process. The models were evaluated on their ability to predict both response time distributions and movement neuron activity observed in monkeys performing a visual search task. Models that assume gating of perceptual evidence to the accumulating units provide the best account of both behavioral and neural data. These results identify discrete stages of processing with anatomically distinct neural populations and rule out several alternative architectures. The results also illustrate the use of neurophysiological data as a model selection tool and establish a novel framework to bridge computational and neural levels of explanation.
In this article, the authors consider optimal decision making in two-alternative forced-choice (TAFC) tasks. They begin by analyzing 6 models of TAFC decision making and show that all but one can be reduced to the drift diffusion model, implementing the statistically optimal algorithm (most accurate for a given speed or fastest for a given accuracy). They prove further that there is always an optimal trade-off between speed and accuracy that maximizes various reward functions, including reward rate (percentage of correct responses per unit time), as well as several other objective functions, including ones weighted for accuracy. They use these findings to address empirical data and make novel predictions about performance under optimality.
Drivers engage in non-driving tasks while driving, such as interactions entertainment systems. Studies have identified glance patterns related to such interactions, and manual radio tuning has been used as a reference task to set an upper bound on the acceptable demand of interactions. Consequently, some view the risk associated with radio tuning as defining the upper limit of glance measures associated with visual-manual in-vehicle activities. However, we have little knowledge about the actual degree of crash risk that radio tuning poses and, by extension, the risk of tasks that have similar glance patterns as the radio tuning task. In the current study, we use counterfactual simulation to take the glance patterns for manual radio tuning tasks from an on-road experiment and apply these patterns to lead-vehicle events observed in naturalistic driving studies. We then quantify how often the glance patterns from radio tuning are associated with rear-end crashes, compared to driving only situations. We used the pre-crash kinematics from 34 crash events from the SHRP2 naturalistic driving study to investigate the effect of radio tuning in crash-imminent situations, and we also investigated the effect of radio tuning on 2,475 routine braking events from the Safety Pilot project. The counterfactual simulation showed that off-road glances transform some near-crashes that could have been avoided into crashes, and glance patterns observed in on-road radio tuning experiment produced 2.85-5.00 times more crashes than baseline driving.
Many modifications build on Tukey's original box plot. A proposed further adaptation, the violin plot, pools the best statistical features of alternative graphical representations of batches of data. It adds the information available from local density estimates to the basic summary statistics inherent in box plots. This marriage of summary statistics and density shape into a single plot provides a useful tool for data analysis and exploration.
"Research in progress on information seeking, intuitive statistics, sequential prediction, and Bayesian information processing is reviewed… . The relevance of mathematical developments in dynamic programming and Bayesian statistics to dynamic decision theory is examined. A man-computer system for probabilistic processing of fallible military information is discussed in some detail as an application of these ideas and as a setting and motivator for future research on human information processing and decision making." (PsycINFO Database Record (c) 2012 APA, all rights reserved)
The accumulator model of two-choice discrimination conceives the decision process as a race between competing evidence totals in discrete time and continuous state space. General expressions for the terminating probabilities for the model are derived, and a tractable version considered in which the increment distributions are exponential. Expressions for response probabilities, first passage time distributions, and a “balance of evidence” theory of response confidence are presented, and the resulting model compared to that obtained under the more usual assumption of truncated normal increments. The exponential model is fitted to data from an experiment exhibiting both faster and slower mean error times across a range of discriminability levels, and is shown to satisfactorily account for these when augmented with the assumption of negatively correlated within-condition variation in discriminability and decision criterion. Cross-paradigmatic support for the fitted model is provided by an estimate of sampling time of the order of 100 msec, in agreement with estimates obtained from a technique employing brief stimulus exposures.
Patterns of neural firing linked to eye movement decisions show that behavioral decisions are predicted by the differential firing rates of cells coding selected and nonselected stimulus alternatives. These results can be interpreted using models developed in mathematical psychology to model behavioral decisions. Current models assume that decisions are made by accumulating noisy stimulus information until sufficient information for a response is obtained. Here, the models, and the techniques used to test them against response-time distribution and accuracy data, are described. Such models provide a quantitative link between the time-course of behavioral decisions and the growth of stimulus information in neural firing data.
The study of decision making spans such varied fields as neuroscience, psychology, economics, statistics, political science, and computer science. Despite this diversity of applications, most decisions share common elements including deliberation and commitment. Here we evaluate recent progress in understanding how these basic elements of decision formation are implemented in the brain. We focus on simple decisions that can be studied in the laboratory but emphasize general principles likely to extend to other settings.
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