• Gustav Markkula, Johan Engström, Johan Lodin, Jonas Bärgman, and Trent Victor.
• Volvo Group Trucks Technology, Advanced Technology and Research, M1.6, 405 08 Göteborg, Sweden; Institute for Transport Studies, University of Leeds, LS2 9JT, Leeds, United Kingdom. Electronic address: g.markkula@leeds.ac.uk.
• Accid Anal Prev. 2016 Oct 1; 95 (Pt A): 209-26.

AbstractDriver braking behavior was analyzed using time-series recordings from naturalistic rear-end conflicts (116 crashes and 241 near-crashes), including events with and without visual distraction among drivers of cars, heavy trucks, and buses. A simple piecewise linear model could be successfully fitted, per event, to the observed driver decelerations, allowing a detailed elucidation of when drivers initiated braking and how they controlled it. Most notably, it was found that, across vehicle types, driver braking behavior was strongly dependent on the urgency of the given rear-end scenario's kinematics, quantified in terms of visual looming of the lead vehicle on the driver's retina. In contrast with previous suggestions of brake reaction times (BRTs) of 1.5s or more after onset of an unexpected hazard (e.g., brake light onset), it was found here that braking could be described as typically starting less than a second after the kinematic urgency reached certain threshold levels, with even faster reactions at higher urgencies. The rate at which drivers then increased their deceleration (towards a maximum) was also highly dependent on urgency. Probability distributions are provided that quantitatively capture these various patterns of kinematics-dependent behavioral response. Possible underlying mechanisms are suggested, including looming response thresholds and neural evidence accumulation. These accounts argue that a naturalistic braking response should not be thought of as a slow reaction to some single, researcher-defined "hazard onset", but instead as a relatively fast response to the visual looming cues that build up later on in the evolving traffic scenario. Copyright © 2016 Elsevier Ltd. All rights reserved.

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