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Field Study of Heavy Vehicle Crash Avoidance System Performance
ISSN: 2327-5626, e-ISSN: 2327-5634
Published September 27, 2016 by SAE International in United States
Citation: Grove, K., Atwood, J., Blanco, M., Krum, A. et al., "Field Study of Heavy Vehicle Crash Avoidance System Performance," SAE Int. J. Trans. Safety 5(1):1-12, 2017, https://doi.org/10.4271/2016-01-8011.
This study evaluated the performance of heavy vehicle crash avoidance systems (CASs) by collecting naturalistic driving data from 150 truck tractors equipped with Meritor WABCO OnGuardTM or Bendix® Wingman® AdvancedTM products. These CASs provide drivers with audio-visual alerts of potential conflicts, and can apply automatic braking to mitigate or prevent a potential collision. Each truck tractor participated for up to one year between 2013 and 2015. Videos of the forward roadway and drivers’ faces were collected along with vehicle network data while drivers performed their normal duties on revenue-producing routes. The study evaluated the performance of CAS activations by classifying them into three categories based on whether a valid object was being tracked and whether drivers needed to react immediately. The highest urgency activations (e.g., Automatic Emergency Braking events and Impact Alerts) were most likely to require an immediate driver response, as opposed to “advisory” activations, which identified a valid threat but may not have required an immediate response. False activations were also observed, and suggestions are offered to reduce their prevalence in future technology generations. Drivers typically received more advisory alerts than high urgency alerts. Advisory alerts may help drivers maintain awareness of surroundings, but may also be bothersome. Speed, headway, brake reaction time, and decelerations were analyzed for changes, and participants did not appear to adapt these behaviors while using CASs. CAS activation, likewise, did not change meaningfully over time. Characterizations of CAS activations, including environmental conditions, were provided to help model their potential benefits across the industry.