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Modeling effects of forward glance durations on latent hazard detection

TitleModeling effects of forward glance durations on latent hazard detection
Publication TypeJournal Article
Year of Publication2017
AuthorsPark H, Gao S, Samuel S
JournalTransportation Research Record Journal of the Transportation Research Board
Start Page90
Date Published01/2017

Performance of in-vehicle, secondary tasks while driving requires a driver to alternate his/her glances between the inside of the vehicle and the forward roadway. While previous research has shown that thresholds of off-road and on-road (forward) glances 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 within an alternating sequence between off-road and on-road glances. To determine the minimum forward roadway duration to adequately anticipate hazards, 45 drivers were asked to navigate 8 unique virtual scenarios on a driving simulator, while alternating their glances inside (2s) and outside (either 1s, 2s, 3s, or 4s) the vehicle in a uniform sequence (consistent on-road and off-road durations). A micro-benchmark approach based on Hidden Markov Models is explored to infer the transition probability of hazard detection that changes dynamically between the detection and non-detection stages. The model is cross-validated, and demonstrated to be accurate and robust. Three different characteristics of total sequence time were tested in the model. Using the ground truth transition probability from fixed forward glance duration, the probability of hazard detection with variable forward glance duration within an alternation sequence was computed. Across non-uniform 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.