Search Results

A method for detecting drivers' intentions is essential to facilitate operating mode transitions between driver and driver assistance systems. We propose a driver behavior recognition method using Hidden Markov Models (HMMs) to characterize and detect driving maneuvers and place it in the framework of a cognitive model of human behavior. HMM-based steering behavior models for emergency and normal lane changes as well as for lane keeping were developed using a moving base driving simulator. Analysis of these models after training and recognition tests showed that driver behavior modeling and recognition of different types of lane changes is possible using HMMs.

We discuss three factors that could alter human operators' perception of a remote worksite and adversely affect their task performance. First, we discuss the effect of image degradation on task performance. Our experimental results are similar to corresponding visual psychophysical experimental results, suggesting that the psychophysical results might be helpful for predicting the performance under other viewing conditions. The second factor is the control of the different viewing parameters. Dynamic control could be disorienting, but if the parameters are fixed, the operator might not feel telepresent. The interface through which the parameters are controlled also requires careful consideration and we discuss the advantages of using a helmet-mounted display. The third factor, the display update rate, can be affected by hardware limitations, transmission delays, or long rendering times.