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Distinguishing Driving Behavior Using the Dynamical Systems Analysis (DSA) Toolbox: Implications for Trust in Automation
SessionPoster Session 1
DescriptionDriver-environment-automation systems exhibit a wide range of distinctive behavioral patterns that emerge without centralized instructions. To understand and quantify their emergence, we examine the nested processes that contribute to behavior at the measured scale using three dynamical systems analyses: multifractal detrended fluctuation analysis (MFDFA), recurrence quantification analysis (RQA), and wavelet coherence analysis (WCT). We present these analyses in tutorial fashion, explaining their appropriateness for each stage of discovery, the information each provides, and the application of that information to driving. Results revealed that driving behaviors are influenced by both long-range (e.g., decision-making) and short-range (e.g., reaction time) processes whose relative contribution differs for the easier straight sections and more challenging S-curve sections of the track. Methods discussed provide information about (a) the timescale at which driving behaviors are being coordinated with environmental and automation considerations and (b) the time points where peak coordination is localized.