Control Theory Models of the Driver

Jürgensohn, Thomas

doi:10.1007/978-1-84628-618-6_16

Cited by 23 publications

(14 citation statements)

References 19 publications

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“…1977; Donges 1978; Jürgensohn 2007), ecological psychology models (Fajen and Warren 2003; Wann and Wilkie 2004; Wilkie et al. 2008), optimal control models (MacAdam 1981; Sharp et al.…”

Section: Computational Framework For Stepwise One-dimensional Controlmentioning

confidence: 99%

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

2018

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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.

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“…1977; Donges 1978; Jürgensohn 2007), ecological psychology models (Fajen and Warren 2003; Wann and Wilkie 2004; Wilkie et al. 2008), optimal control models (MacAdam 1981; Sharp et al.…”

Section: Computational Framework For Stepwise One-dimensional Controlmentioning

confidence: 99%

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

2018

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“…The driver task of velocity control and lane keeping can be assumed to be fairly independent; see [14], [21]. The same observation holds for autonomous vehicles whose vehicle control is separately developed for lateral and longitudinal dynamics [34], or whose control has negligible dependency with respect to the safety assessment in road traffic.…”

Section: Combined Distributionmentioning

confidence: 99%

Comparison of Markov Chain Abstraction and Monte Carlo Simulation for the Safety Assessment of Autonomous Cars

Althoff

Mergel

2011

IEEE Trans. Intell. Transport. Syst.

126

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The probabilistic prediction of road traffic scenarios is addressed. One result is a probabilistic occupancy of traffic participants, the other result is the collision risk for autonomous vehicles when executing a planned maneuver. The probabilistic occupancy of surrounding traffic participants helps to plan the maneuver of an autonomous vehicle, while the computed collision risk helps to decide if a planned maneuver should be executed. Two methods for the probabilistic prediction are presented and compared: Markov chain abstraction and Monte Carlo simulation. The performance of both methods is evaluated with respect to the prediction of the probabilistic occupancy and the collision risk. For each comparison test we use the same models generating the probabilistic behavior of traffic participants, where the generation of these data is not compared to real world data. However, the results show independently of the behavior generation that Markov chains are preferred for the probabilistic occupancy, while Monte Carlo simulation is clearly preferred for determining the collision risk.

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“…There are a number of control-theoretical driver models (Jürgensohn, 2007;Weir and Chao, 2007) available. Salvucci and Gray (2004) and Salvucci (2007) proposed an integrated model (S&G-model), which has been implemented as a production system within the wide-spread cognitive architecture ACT-R (Anderson et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Modeling Lateral and Longitudinal Control of Human Drivers with Multiple Linear Regression Models

Lenk¹,

Claus²

2010

Advances in Human Factors and Ergonomics Series

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In this paper, we describe results to model lateral and longitudinal control behavior of drivers with simple linear multiple regression models. This approach fits into the Bayesian Programming (BP) approach (Bessière, 2008) because the linear multiple regression model suggests an action selection strategy which is an alternative to the BP action selection strategies draw and best. Furthermore, the inference process provided by a linear multiple regression model is a kind of short cut inference compared to the inference approach used in Bayesian networks or Bayesian Programming.

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Control Theory Models of the Driver

Cited by 23 publications

References 19 publications

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

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

Comparison of Markov Chain Abstraction and Monte Carlo Simulation for the Safety Assessment of Autonomous Cars

Modeling Lateral and Longitudinal Control of Human Drivers with Multiple Linear Regression Models

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