Two performance measures for evaluating human control strategy
In the last few years, modeling dynamic human control strategy (HCS) is becoming an increasingly popular paradigm in a number of different research areas, such as the intelligent vehicle highway system, virtual reality and robotics. Usually, these models are derived empirically, rather than analytically, from real human input-output control data. As such, there is a great need to develop adequate performance criteria for these models, as few guarantees exist about their theoretical performance. It is our goal in this paper to develop several such criteria. In this paper, we first collect driving data from different individuals through a real-time graphic driving simulator. We then model each individual's control strategy through the flexible cascade neural network learning architecture. Next, we develop two performance measures for evaluating the resulting HCS models, one dealing with obstacle avoidance, the other with tight-turning behavior. Finally, we evaluate the relative skill of different HCS models through the proposed performance criteria.