Performance evaluation and optimization of human control strategy

Abstract

Modeling human control strategy (HCS) is becoming an increasingly popular paradigm in a number of different research areas, ranging from robotics and intelligent vehicle highway systems to expert training and virtual reality computer games. Usually, HCS models are derived empirically, rather than analytically, from real-time human input-output data. While these empirical models offer an effective means of transferring intelligent behaviors from humans to robots and other machines, there is a great need to develop adequate performance criteria for these models. It is our goal in this paper to develop several such criteria for the task of human driving. We first collect driving data from different individuals through a real-time graphic driving simulator that we have developed, and identify each individual's control strategy model through the flexible cascade neural network learning architecture. We then define performance measures for evaluating two aspects of the resultant HCS models. The first is based on event analysis, while the second is based on inherent analysis. Using the proposed performance criteria, we demonstrate the procedure for evaluating the relative skill of different HCS models. Finally, we propose an iterative algorithm for optimizing an initially stable HCS model with respect to independent, user-specified performance criteria, by applying the simultaneously perturbed stochastic approximation (SPSA) algorithm. The methods proposed herein offer a means for modeling and transferring HCS in response to real-time inputs, and improving the intelligent behaviors of artificial machines.

A Personalized Behavior Learning System for Human-Like Longitudinal Speed Control of Autonomous Vehicles

“…Two indicators suggested by [36] are used here to evaluate and compare the performance of PBLS and ACC on driving comfort and smoothness. These two indicators are given by:J1=ameanvmean, J2=dadt, where the driving comfort is measured by J1, which is obtained by dividing the average acceleration amean by the average speed vmean, and the driving smoothness is measured by J2, which is the jerk of the vehicle.…”

System Identification Technique Application to Revealing Human-Operator Skills

“…Numerous papers are devoted to investigation of various issues of design and performance of human-machine systems with regard for the human factor (Blasch, 2000, Calhoun, 2000, Celik and Ertugrul, 2010, Damveld, 2010, Dismukes, 2010, Doman and Anderson, 2000, Ertugrul, 2008, Iiguni et al, 1998, Kontogiannis and Malakis, 2009, Lee and Sanquist, 2000, Menendez and Bernard, 2001, O'Connor et al, 2008, Ramesh and Sylla, 1990, Redling, 2001, Rouse et al, 1989, Shorrock and Kirwan, 2002, Stengel, 1993, Stonge and Becker, 1988, Sutton, 1990, Tsvetkova, 1999, Xu et al, 2002, Yinhua Jin, 2004, paying special attention to revealing/identification of human-operator skills and experience. Human-operator decision making under abnormal situations is the most main and the most difficult task of their professional activity, with the task being considerably complicated by incomplete or false information, stress, or lack of time, as well as aggravation by the huge cost of the human-operator errors.…”

Stochastic approximation to optimize the performance of human operators

“…The stochastic approximation algorithms also have been applied to human factor research. For example, they are used to estimate human operator model parameters in [17] and optimize driving control strategy in [18]. However, no previously stochastic approximation algorithms that aim at optimizing human decision making were found in the literature.…”