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

Behavioural validity of driving simulators for prototype HMI evaluation

In-vehicle interfaces are now part of the vast majority of production vehicles. Such interfaces need to be thoroughly evaluated to ensure they do not pose any risks to the drivers using them. Driving simulators have extensively been used in such a context, yet their reliability in terms of how realistic a driving behaviour they elicit is still in question. An investigation on driving simulator behavioural validity in the context of prototype human-machine interface evaluation is presented in this study. Using data collected in a dual setting driving study (driving simulator and real world), as well as results from existing related literature, a comparison between driving behaviour in different types of driving simulators and in reality was carried out, for a variety of behavioural metrics. The results are presented in the form of a `validity matrix' that aggregates the level of behavioural validity different simulator settings can achieve for different behavioural metrics

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

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

Incidence and prevalence of mental disorders among immigrants and native Finns: a register-based study

Migrants appear to have a higher risk of mental disorders, but findings vary across country settings and migrant groups. We aimed to assess incidence and prevalence of mental disorders among immigrants and Finnish-born controls in a register-based cohort study.A register-based cohort study of 184.806 immigrants and 185.184 Finnish-born controls (1.412.117 person-years) was conducted. Information on mental disorders according to ICD-10 was retrieved from the Hospital Discharge Register, which covers all public health care use.The incidence of any mental disorder was lower among male (adjusted HR 0.82, 95% CI 0.77-0.87) and female (aHR 0.76, 95% CI 0.72-0.81) immigrants, being lowest among Asian and highest among North African and Middle Eastern immigrants. The incidence of bipolar, depressive and alcohol use disorders was lower among immigrants. Incidence of psychotic disorders was lower among female and not higher among male immigrants, compared with native Finns. Incidence of PTSD was higher among male immigrants (aHR 4.88, 95% CI 3.38-7.05).The risk of mental disorders varies significantly across migrant groups and disorders and is generally lower among immigrants than native Finns

Defining interactions: a conceptual framework for understanding interactive behaviour in human and automated road traffic

Rapid advances in technology for highly automated vehicles (HAVs) have raised concerns about coexistence of HAVs and human road users. Although there is a long tradition of research into human road user interactions, there is a lack of shared models and terminology to support cross-disciplinary research and development towards safe and acceptable interaction-capable HAVs. Here, we review the main themes and findings in previous theoretical and empirical interaction research, and find large variability in perspectives and terminologies. We unify these perspectives in a structured, cross-theoretical conceptual framework, describing what road traffic interactions are, how they arise, and how they get resolved. Two key contributions are: (1) a stringent definition of “interaction”, as “a situation where the behaviour of at least two road users can be interpreted as being influenced by the possibility that they are both intending to occupy the same region of space at the same time in the near future”, and (2) a taxonomy of the types of behaviours that road users exhibit in interactions. We hope that this conceptual framework will be useful in the development of improved empirical methodology, theoretical models, and technical requirements on vehicle automation

Models of human decision-making as tools for estimating and optimising impacts of vehicle automation

With the development of increasingly automated vehicles (AVs) comes the increasingly difficult challenge of comprehensively validating these for acceptable, and ideally beneficial, impacts on the transport system. There is a growing consensus that virtual testing, where simulated AVs are deployed in simulated traffic, will be key for cost-effective testing and optimisation. The least mature model components in such simulations are those generating the behaviour of human agents in or around the AVs. In this paper, human models and virtual testing applications are presented for two example scenarios: (i) a human pedestrian deciding whether to cross a street in front of an approaching automated vehicle, with or without external human-machine interface elements, and (ii) an AV handing over control to a human driver in a critical rear-end situation. These scenarios have received much recent research attention, yet simulation-ready human behaviour models are lacking. They are discussed here in the context of existing models of perceptual decision-making, situational awareness, and traffic interactions. It is argued that the human behaviour in question might be usefully conceptualised as a number of interrelated decision processes, not all of which are necessarily directly associated with externally observable behaviour. The results show that models based on this type of framework can reproduce qualitative patterns of behaviour reported in the literature for the two addressed scenarios, and it is demonstrated how computer simulations based on the models, once these have been properly validated, could allow prediction and optimisation of the AV

Cognitive Load During Automation Affects Gaze Behaviours and Transitions to Manual Steering Control

Automated vehicles (AVs) are being tested on-road with plans for imminent large-scale deployment. Many AVs are being designed to control vehicles without human input, whilst still relying on a human driver to remain vigilant and responsible for taking control in case of failure. Drivers are likely to use AV control periods to perform additional non-driving related tasks, however the impact of this load on successful steering control transitions (from AV to the human) remains unclear. Here, we used a driving simulator to examine the effect of an additional cognitive load on gaze behavior during automated driving, and on subsequent manual steering control. Drivers were asked to take-over control after a short period of automation caused trajectories to drift towards the outside edge of a bending road. Drivers needed to correct lane position when there was no additional task (“NoLoad”), or whilst also performing an auditory detection task (“Load”). Load might have affected gaze patterns, so to control for this we used either: i) Free gaze, or ii) Fixed gaze (to the road center). Results showed that Load impaired steering, causing insufficient corrections for lane drift. Free gaze patterns were influenced by the added cognitive load, but impaired steering was also observed when gaze was fixed. It seems then that the driver state (cognitive load and gaze direction) during automation may have important consequences for whether the takeover of manual vehicle control is successful

Empirical game theory of pedestrian interaction for autonomous vehicles

Autonomous vehicles (AV’s) are appearing on roads, based on standard robotic mapping and navigation algorithms. However their ability to interact with other road-users is much less well understood. If AVs are programmed to stop every time another road user obstructs them, then other road users simply learn that they can take priority at every interaction, and the AV will make little or no progress. This issue is especially important in the case of a pedestrian crossing the road in front of the AV. The present methods paper expands the sequential chicken model introduced in (Fox et al., 2018), using empirical data to measure behavior of humans in a controlled plus-maze experiment, and showing how such data can be used to infer parameters of the model via a Gaussian Process. This providing a more realistic, empirical understanding of the human factors intelligence required by future autonomous vehicles