Abstract
Performance of in-vehicle, secondary tasks requires a driver to alternate his or her glances between the inside of the vehicle and the forward roadway. While previous research has shown that thresholds of off-road and durations are critical to latent hazard detection, there has been no research to predict the probability of hazard detection in a time series considering all possible forward glance durations. To determine the minimum forward roadway duration to adequately anticipate hazards, 45 drivers were asked to navigate eight unique virtual scenarios on a driving simulator while alternating their glances inside (2s) and outside (1s, 2s, 3s, or 4s) the vehicle in a uniform sequence (consistent on-road and off-road durations). A microbenchmark approach based on Hidden Markov models is explored to infer the transition probability of hazard detection that changes dynamically between the detection and nondetection stages. The model is cross validated and demonstrated to be accurate and robust. Three characteristics of total sequence time were tested in the model. Using the ground truth transition probability from fixed duration, the probability of hazard detection with variable duration within an alternation sequence was computed. Across nonuniform alternation sequences, different permutations of sequences show that a short time series of alternation (10s) of glance behavior is sufficient for hazard detection (greater than 50.0%). Appropriate countermeasures can be developed to increase a driver’s forward glance duration whenever the detection probability is predicted low.
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