A Steering Wheel Reversal Rate Metric for Assessing Effects of Visual and Cognitive Secondary Task Load

This paper presents a steering wheel reversal rate metric intended for assessment of the effects of secondary tasks, such as interacting with in-vehicle information systems, on vehicle lateral control performance. The metric was compared to a number of other common steering wheel metrics with respect to the sensitivity to visual and cognitive secondary task load. It was shown that the proposed reversal rate metric, together with the existing steering entropy metric, was the most sensitive across experimental conditions. Different parameter settings for the metric were systematically investigated and suitable values for capturing the effects of visual and cognitive secondary task load recommended

Modeling driver control behavior in both routine and near-accident driving

Building on ideas from contemporary neuroscience, a framework is proposed in which drivers’ steering and pedal behavior is modeled as a series of individual control adjustments, triggered after accumulation of sensory evidence for the need of an adjustment, or evidence that a previous or ongoing adjustment is not achieving the intended results. Example simulations are provided. Specifically, it is shown that evidence accumulation can account for previously unexplained variance in looming detection thresholds and brake onset timing. It is argued that the proposed framework resolves a discrepancy in the current driver modeling literature, by explaining not only the short-latency, well-tuned, closed-loop type of control of routine driving, but also the degradation into long-latency, ill-tuned open-loop control in more rare, unexpected, and urgent situations such as near-accidents

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

Validation of driving behaviour as a step towards the investigation of Connected and Automated Vehicles by means of driving simulators

Connected and Automated Vehicles (CAVs) are likely to become an integral part of the traffic stream within the next few years. Their presence is expected to greatly modify mobility behaviours, travel demands and habits, traffic flow characteristics, traffic safety and related external impacts. Tools and methodologies are needed to evaluate the effects of CAVs on traffic streams, as well as the impact on traffic externalities. This is particularly relevant under mixed traffic conditions, where human-driven vehicles and CAVs will interact. Understanding technological aspects (e.g. communication protocols, control algorithms, etc.) is crucial for analysing the impact of CAVs, but the modification induced in human driving behaviours by the presence of CAVs is also of paramount importance. For this reason, the definition of appropriate CAV investigations methods and tools represents a key (and open) issue. One of the most promising approaches for assessing the impact of CAVs is operator in the loop simulators, since having a real driver involved in the simulation represents an advantageous approach. However, the behaviour of the driver in the simulator must be validated and this paper discusses the results of some experiments concerning car-following behaviour. These experiments have included both driving simulators and an instrumented vehicle, and have observed the behaviours of a large sample of drivers, in similar conditions, in different experimental environments. Similarities and differences in driver behaviour will be presented and discussed with respect to the observation of one important quantity of car-following, the maintained spacing

Answering questions about consciousness by modeling perception as covert behavior.

Two main open questions in current consciousness research concern (i) the neural correlates of consciousness (NCC) and (ii) the relationship between neural activity and first-person, subjective experience. Here, possible answers are sketched for both of these, by means of a model-based analysis of what is required for one to admit having a conscious experience. To this end, a model is proposed that allows reasoning, albeit necessarily in a simplistic manner, about all of the so called "easy problems" of consciousness, from discrimination of stimuli to control of behavior and language. First, it is argued that current neuroscientific knowledge supports the view of perception and action selection as two examples of the same basic phenomenon, such that one can meaningfully refer to neuronal activations involved in perception as covert behavior. Building on existing neuroscientific and psychological models, a narrative behavior model is proposed, outlining how the brain selects covert (and sometimes overt) behaviors to construct a complex, multi-level narrative about what it is like to be the individual in question. It is hypothesized that we tend to admit a conscious experience of X if, at the time of judging consciousness, we find ourselves acceptably capable of performing narrative behavior describing X. It is argued that the proposed account reconciles seemingly conflicting empirical results, previously presented as evidence for competing theories of consciousness, and suggests that well-defined, experiment-independent NCCs are unlikely to exist. Finally, an analysis is made of what the modeled narrative behavior machinery is and is not capable of. It is discussed how an organism endowed with such a machinery could, from its first-person perspective, come to adopt notions such as "subjective experience," and of there being "hard problems," and "explanatory gaps" to be addressed in order to understand consciousness

Applicability of risky decision-making theory to understand drivers' behaviour during transitions of control in vehicle automation

This work presents a consideration of the applicability of risky decision-making theory models as a tool to understand drivers’ take-over behaviour from vehicle automation, while also incorporating the “Out of the Loop” concept and the process of Situation Awareness Recovery. A methodological discussion is provided, and implications for the processes involved in system design developments are presented. Finally, the paper concludes that the process of evidence accumulation in risky decision-making theory models has strong parallels with the process of Situation Awareness recovery. We argue that evidence accumulation models can be used as a tool to understand what information is used by drivers for achieving safe transitions of control from automation so that this knowledge can be used for a better, and more human-centred design of future in-vehicle interfaces

Evidence Accumulation Account of Human Operators' Decisions in Intermittent Control During Inverted Pendulum Balancing

Human operators often employ intermittent, discontinuous control strategies in a variety of tasks. A typical intermittent controller monitors control error and generates corrective action when the deviation of the controlled system from the desired state becomes too large to ignore. Most contemporary models of human intermittent control employ simple, threshold-based trigger mechanism to model the process of control activation. However, recent experimental studies demonstrate that the control activation patterns produced by human operators do not support threshold-based models, and provide evidence for more complex activation mechanisms. In this paper, we investigate whether intermittent control activation in humans can be modeled as a decision-making process. We utilize an established drift-diffusion model, which treats decision making as an evidence accumulation process, and study it in simple numerical simulations. We demonstrate that this model robustly replicates the control activation patterns (distributions of control error at movement onset) produced by human operators in previously conducted experiments on virtual inverted pendulum balancing. Our results provide support to the hypothesis that intermittent control activation in human operators can be treated as an evidence accumulation process

When Should the Chicken Cross the Road? - Game Theory for Autonomous Vehicle - Human Interactions

Autonomous vehicle localization, mapping and planning in un-reactive environments are well-understood, but the human factors of complex interactions with other road users are not yet developed. This study presents an initial model for negotiation between an autonomous vehicle and another vehicle at an unsigned intersections or (equivalently) with a pedestrian at an unsigned road-crossing (jaywalking), using discrete sequential game theory. The model is intended as a basic framework for more realistic and data-driven future extensions. The model shows that when only vehicle position is used to signal intent, the optimal behaviors for both agents must include a non-zero probability of allowing a collision to occur. This suggests extensions to reduce this probability in future, such as other forms of signaling and control. Unlike most Game Theory applications in Economics, active vehicle control requires real-time selection from multiple equilibria with no history, and we present and argue for a novel solution concept, meta-strategy convergence, suited to this task