How does glance behavior influence crash and injury risk? A 'what-if' counterfactual simulation using crashes and near-crashes from SHRP2. (November 2015)

• Record Type: Journal Article
• Authors: Bärgman, Jonas; Lisovskaja, Vera; Victor, Trent; Flannagan, Carol; Dozza, Marco
• Is Part Of: Transportation research. Volume 35(2016)
• Page Start: 152
• Page End: 169
• Publication Date: 2015-11
• Subjects: Lead-vehicle -- Secondary task -- Driver–vehicle interface -- HMI -- Eyes-off-road -- ADAS evaluation; Automobile drivers -- Psychology -- Periodicals; Automobile driving -- Psychological aspects -- Periodicals; Transportation -- Psychological aspects -- Periodicals; 629.283019
• DOI: 10.1016/j.trf.2015.10.011
• Languages: English
• ISSNs: 1369-8478

Abstract

Highlights: What-if simulation estimated the effect of driver glance behavior on crash risk. Crash and injury risk were calculated from SHRP2 crashes and near-crashes. The simulation applied glance behaviors of baseline and three tasks to SHRP2 data. Discussed trade-offs across glance metrics to maximize safety in the design of HMIs. Abstract: As naturalistic driving data become increasingly available, new analyses are revealing the significance of drivers' glance behavior in traffic crashes. Due to the rarity of crashes, even in the largest naturalistic datasets, near-crashes are often included in the analyses and used as surrogates for crashes. However, to date we lack a method to assess the extent to which driver glance behavior influences crash and injury risk across both crashes and near-crashes. This paper presents a novel method for estimating crash and injury risk from off-road glance behavior for crashes and near-crashes alike; this method can also be used to evaluate the safety impact of secondary tasks (such as tuning the radio). We apply a 'what-if' (counterfactual) simulation to 37 lead-vehicle crashes and 186 lead-vehicle near-crashes from lead-vehicle scenarios identified in the SHRP2 naturalistic driving data. The simulation combines the kinematics of the two conflicting vehicles with a model of driver glance behavior to estimate two probabilities: (1) that each event becomes a crash, and (2) that each event causes a specific level of injury. The usefulness of the method is demonstrated by comparing the crash and injury risk of normal driving with the risks of driving while performing one of three secondary tasks: the Rockwell radio-tuning task and two hypothetical tasks. Alternative applications of the method and its metrics are also discussed. The method presented in this paper can guide the design of safer driver–vehicle interfaces by showing the best tradeoff between the percent of glances that are on-road, the distribution of off-road glances, and the total task time for different tasks.