  Driving information and data under potential vehicle crashes create
opportunities for extensive real-world observations of driver behaviors and
relevant factors that significantly influence the driving safety in emergency
scenarios. Furthermore, the availability of such data also enhances the
collision avoidance systems (CASs) by evaluating driver's actions in near-crash
scenarios and providing timely warnings. These applications motivate the need
for heuristic tools capable of inferring relationship among driving risk,
driver/vehicle characteristics, and road environment. In this paper, we
acquired amount of real-world driving data and built a comprehensive dataset,
which contains multiple "driver-vehicle-road" attributes. The proposed method
works in two steps. In the first step, a variable precision rough set (VPRS)
based classification technique is applied to draw a reduced core subset from
field driving dataset, which presents the essential attributes set most
relevant to driving safety assessment. In the second step, we design a decision
strategy by introducing mutual information entropy to quantify the significance
of each attribute, then a representative index through accumulation of weighted
"driver-vehicle-road" factors is calculated to reflect the driving risk for
actual situation. The performance of the proposed method is demonstrated in an
offline analysis of the driving data collected in field trials, where the aim
is to infer the emergency braking actions in next short term. The results
indicate that our proposed model is a good alternative for providing improved
warnings in real-time because of its high prediction accuracy and stability.
