Sharing Control With Haptics: Seamless Driver Support From Manual to Automatic Control
ABSTRACT Haptic shared control was investigated as a human-machine interface that can intuitively share control between drivers and an automatic controller for curve negotiation.
As long as automation systems are not fully reliable, a role remains for the driver to be vigilant to the system and the environment to catch any automation errors. The conventional binary switches between supervisory and manual control has many known issues, and haptic shared control is a promising alternative.
A total of 42 respondents of varying age and driving experience participated in a driving experiment in a fixed-base simulator, in which curve negotiation behavior during shared control was compared to during manual control, as well as to three haptic tunings of an automatic controller without driver intervention.
Under the experimental conditions studied, the main beneficial effect of haptic shared control compared to manual control was that less control activity (16% in steering wheel reversal rate, 15% in standard deviation of steering wheel angle) was needed for realizing an improved safety performance (e.g., 11% in peak lateral error). Full automation removed the need for any human control activity and improved safety performance (e.g., 35% in peak lateral error) but put the human in a supervisory position.
Haptic shared control kept the driver in the loop, with enhanced performance at reduced control activity, mitigating the known issues that plague full automation.
Haptic support for vehicular control ultimately seeks to intuitively combine human intelligence and creativity with the benefits of automation systems.
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ABSTRACT: Recent technological developments have shown a transition from informative driving support systems to more automated vehicles. Although automated vehicles are designed to overcome limitations in human perception, decision making and response, there may be a downside to introducing these technologies. The downside is based on the new cooperation between the driver and the vehicle, leaving room for misinterpretation, overreliance on system performance and loss of situation awareness in case of requested transfer of control from the automated vehicle back to the driver. This article raises several human factors issues that are of importance when designing (semi-)automated vehicles, such as: the driver as a system monitor, situation awareness and system limitations. Various implications for the design of automated systems are discussed.2013 16th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2013); 10/2013
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ABSTRACT: Several publications have shown that it is beneficial to design a driver assistance system using a shared control structure. For the steering task this structure can be realized with a setup in which driver and automation can apply a torque on the steering wheel in parallel. Thereby both, driver and assistance system, interact with the vehicle and each other over the haptical channel. In the system the driver is given and cannot be changed. The question is how to design the assistance system controller as an ideal complement to the driver. In this paper a formal design concept is applied to this problem which utilizes the fact that adding a controller to the overall system has to lead to a Nash equilibrium. Remaining degrees of freedom are used to optimize the designed controller with respect to a global objective function that specifies overall system performance. We refer the concept as “cooperative shared control design”. For the concept driver and vehicle are modeled as a differential game. We show systematically that this concept can be used to determine the optimal assistance system if the driver characteristics are known. Simulations prove the applicability of this concept.Systems, Man and Cybernetics (SMC), 2014 IEEE International Conference on; 10/2014
Conference Paper: Driver Distraction Assessment Using Driver Modeling[Show abstract] [Hide abstract]
ABSTRACT: Characterizing individual human drivers is of increasing interest for applications like adaptive driver assistance or monitoring. Describing the human driver by means of control-theoretic driver models constitutes a promising approach. In this paper, we apply a driver model adopted from literature to real-road driving of a distraction experiment in order to assess the driver state. The control-theoretic driver model features an anticipatory and a compensatory tracking component as well as a processing delay and a neuromuscular motor component. The distraction experiment data comprises real road driving with a visuomotor and an auditory secondary task, as well as reference driving. By means of prediction error identification, we continuously and individually estimate the model parameters from driving data of eleven drivers. We evaluate the distributions of the driver model parameters and the predictive capability of the estimated driver models. The estimated driver model parameters reflect distracted driving behavior according to the driving task. As a promising experimental result, the driver model parameters and predictive performance are significantly associated with driver distraction.2013 IEEE International Conference on Systems, Man and Cybernetics (SMC 2013); 10/2013